Method for processing blood oxygen signal, blood oxygen detection device, electronic device, storage medium and computer program product

Abstract

The invention relates to a method for processing a blood oxygen signal, a blood oxygen detection device, an electronic device, a storage medium and a computer program product. The method comprises the following steps: judging whether the range of a plurality of bSpO2 (bSpO2) values measured during a specified time window meets a preset condition or not; and selecting a range for calculating the bSpO2 values from the plurality of bSpO2 values according to a judgment result, and performing mathematical statistic calculation by using the bSpO2 values in the selected range to obtain the SpO2 value serving as an oxyhemoglobin saturation output value. Therefore, the detection precision of the SpO2 value can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of signal processing, and more particularly to methods for processing pulse oximetry signals, pulse oximetry detection devices, methods for processing blood oxygenation signals, blood oxygenation detection devices, electronic devices, storage media, and computer program products. Background Technology

[0002] Oxygen is essential for the function of every cell in the human body. Cells will die under prolonged oxygen deficiency. Therefore, delivering oxygen to cells is a crucial indicator of a patient's health.

[0003] Red blood cells contain a protein called hemoglobin. When this protein is exposed to oxygen, these elements bind together to form hemoglobin (HbO2). Oxygenated hemoglobin-containing red blood cells circulate throughout the body's bloodstream, delivering oxygen to various tissues. When blood comes into contact with tissues with lower oxygen levels, the hemoglobin in red blood cells releases oxygen, becoming deoxygenated hemoglobin (Hb).

[0004] In clinical medicine, a common indicator of a patient's ability to transport oxygen from the lungs to other tissues in the body is the percentage of oxygen carried by hemoglobin. This measurement is called "arterial oxygen saturation," abbreviated as SaO2. Arterial oxygen saturation can be calculated using the following equation, where C... HbO2 It is the concentration of oxyhemoglobin in arterial blood, C Hb It is the concentration of deoxyhemoglobin in the same arterial blood sample.

[0005]

[0006] Several methods exist for measuring arterial oxygen saturation, including laboratory oxygen saturation measurement (SaO2) which involves in vitro analysis of arterial blood samples, and pulse oximetry, a non-invasive method for measuring arterial oxygen saturation (SpO2). Pulse oximetry measures arterial oxygen saturation by illuminating a detector on the other side of a tissue bed with red and infrared light of specific wavelengths through peripheral tissues of the patient (such as fingers, earlobes, or toes). SpO2 is calculated by measuring the intensity of the red and infrared light received by the detector at its peak during each cardiac cycle. This peak occurs when blood flow through the tissue bed reaches its maximum. The SpO2 value calculated based on the light intensity associated with a single cardiac cycle is referred to herein as bSpO2, where "b" indicates that the SpO2 value is derived from a measurement associated with a single heartbeat.

[0007] Pulse oximetry is the most commonly used clinical method for measuring arterial oxygen saturation because it is non-invasive, easy to use, can be operated continuously, and is accurate for most clinical purposes. However, the accuracy of this technique can be significantly reduced when the motion of the pulse oximeter sensor relative to the tissue bed in which it is placed (motion artifact) temporarily interferes with the red and / or infrared light paths. Summary of the Invention

[0008] In view of this, the present disclosure provides a method for processing blood oxygen signals, a blood oxygen detection device, an electronic device, a storage medium, and a computer program product.

[0009] According to a first aspect of this disclosure, a method for processing pulse oximetry signals is provided, comprising: determining whether a range of multiple pulse oximetry (bSpO2) values ​​measured during a specified time window meets predetermined conditions; selecting a range from the multiple bSpO2 values ​​for calculating bSpO2 values ​​based on the result of the determination; and performing mathematical statistics calculations using the bSpO2 values ​​within the selected range to obtain the SpO2 value used as an output value for blood oxygen saturation.

[0010] According to a second aspect of this disclosure, a pulse oximetry detection device is provided, comprising: a judgment module for judging whether a range of multiple pulse oximetry (bSpO2) values ​​measured during a specified time window meets predetermined conditions; and a processing module for selecting a range from the multiple bSpO2 values ​​for calculating bSpO2 values ​​based on the judgment result, and performing mathematical statistics calculations on the bSpO2 values ​​within the selected range to obtain the SpO2 value used as an output value for blood oxygen saturation.

[0011] According to a third aspect of this disclosure, a method for processing blood oxygen signals is provided, comprising: a judgment step for judging whether the signal changes of a currently acquired red light signal and an infrared light signal are similar during a specified time window; and a processing step for determining, based on the result of the judgment, whether motion artifacts exist in the red light signal and the infrared light signal.

[0012] According to a fourth aspect of this disclosure, a blood oxygen detection device is provided, comprising: a judgment module for judging whether the signal changes of a red light signal and an infrared light signal collected during a specified time window are similar; and a processing module for determining, based on the judgment result, whether motion artifacts exist in the red light signal and the infrared light signal.

[0013] According to a fifth aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the method described above when executing the processor-executable instructions.

[0014] According to a sixth aspect of this disclosure, a non-volatile computer-readable storage medium is provided, having stored thereon computer program instructions, characterized in that the computer program instructions, when executed by a processor, implement the method described above.

[0015] According to a seventh aspect of this disclosure, a computer program product is provided that stores instructions which, when executed by a computer, cause the computer to perform the methods described above.

[0016] Compared to existing technologies that directly use numerous bSpO2 values ​​to calculate the blood oxygen saturation output value, i.e., the SpO2 value, this disclosure uses only those bSpO2 values ​​within the statistically dominant range of values ​​measured during a specified time window. The range used to calculate the SpO2 value is selected from numerous bSpO2 values ​​based on a judgment result of whether the range of multiple bSpO2 values ​​measured during the specified time window meets predetermined conditions (which eliminates noise data corresponding to motion artifacts). The SpO2 value is calculated using bSpO2 values ​​within the selected range. Therefore, this disclosure uses bSpO2 values ​​with less noise to calculate the SpO2 value compared to existing technologies, thereby improving the detection accuracy of the SpO2 value.

[0017] Furthermore, compared to the prior art where doctors determine the presence of motion artifacts in red and infrared signals based on the displayed SpO2 value, this disclosure determines the presence of motion artifacts in red and infrared signals based on the similarity of signal changes between the two signals collected during a specified time window. Therefore, this disclosure uses a different approach than the prior art to obtain intermediate processing results for determining the presence of motion artifacts, thereby enabling a more accurate distinction between physiological and motion artifact-generated signal components.

[0018] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0019] Figures 1a-1e A flowchart illustrating a method for processing blood oxygen signals according to an embodiment of the present disclosure is shown.

[0020] Figure 2a This shows the data formed when red and infrared light penetrate the skin.

[0021] Figure 2b A schematic diagram of a time window according to an embodiment of the present disclosure is shown.

[0022] Figure 3 An example of histogram data is shown according to an embodiment of the present disclosure.

[0023] Figure 4The diagram shows a waveform representation of a segment including motion artifacts and the corresponding bSpO2 value according to an embodiment of the present disclosure.

[0024] Figure 5 Show Figure 4 The histogram of the waveform in the clean signal period of 10s-24s.

[0025] Figure 6 Show Figure 4 The histogram of the waveform in the period 18s-32s.

[0026] Figure 7 Show Figure 4 The histograms of the waveform in the full noise period of 23s-37s and 25s-39s.

[0027] Figure 8 Show Figure 4 The histograms of the waveform in the low-noise period and the clean period of 38s-52s.

[0028] Figure 9 A waveform diagram of bSpO2 according to an embodiment of the present disclosure is shown.

[0029] Figure 10 and Figure 11 This diagram illustrates the use of all warehouses as target warehouses according to an embodiment of the present disclosure.

[0030] Figure 12 A schematic diagram showing the use of a portion of the warehouse as the target warehouse according to an embodiment of the present disclosure is shown.

[0031] Figure 13 A waveform diagram of bSpO2 according to an embodiment of the present disclosure is shown.

[0032] Figure 14 Showing with Figure 13 Histograms constructed using different bin widths and center points corresponding to the period 968s-982s.

[0033] Figure 15 Showing with Figure 13 Histograms constructed using different bin widths and center points corresponding to the period 972s-986s.

[0034] Figure 16 Showing with Figure 13 Histograms constructed using different bin widths and center points corresponding to the period 975s-989s.

[0035] Figure 17 A waveform diagram of bSpO2 according to an embodiment of the present disclosure is shown.

[0036] Figure 18 and Figure 19 Showing with Figure 17 The waveforms in the graph correspond to histograms constructed using different bin widths and center points.

[0037] Figure 20 The effect of motion artifact contamination on red (R) and infrared (IR) pulse oxygen saturation signals on the calculated SpO2 value is shown.

[0038] Figure 21 A block diagram of a blood oxygen detection device 200 according to an embodiment of the present disclosure is shown.

[0039] Figure 22 A block diagram of a blood oxygen detection device 300 according to an embodiment of the present disclosure is shown.

[0040] Figure 23 A flowchart is shown for a method 400 for processing blood oxygen signals according to an embodiment of the present disclosure.

[0041] Figure 24 and 25 A schematic diagram showing clean data of red light signals and infrared light signals according to an embodiment of the present disclosure.

[0042] Figure 26 and 27 This diagram illustrates noisy data of red light signals and infrared light signals according to an embodiment of the present disclosure.

[0043] Figure 28 A waveform diagram of bSpO2 according to an embodiment of the present disclosure is shown.

[0044] Figure 29 A schematic diagram showing raw data of red light signals and infrared light signals according to an embodiment of the present disclosure is provided.

[0045] Figure 30 A schematic diagram showing the waveform of the red light signal in the absence of motion artifacts.

[0046] Figure 31 The diagram shows a waveform representation of the cleaning data.

[0047] Figure 32 A waveform diagram of the noise data is shown.

[0048] Figure 33 A block diagram of a blood oxygen detection device 500 according to an embodiment of the present disclosure is shown.

[0049] Figure 34 A block diagram of a blood oxygen detection device 600 according to an embodiment of the present disclosure is shown.

[0050] Figure 35This is a block diagram of an electronic device 700 according to an exemplary embodiment. Detailed Implementation

[0051] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0052] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0053] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0054] In related technologies, such as Figure 2a As shown, the system uses the AC and DC components of these red and infrared light signals at peak attenuation and calculates bSpO2 using the general equation bSpO2=K*(RedAC / RedDC) / (InfraredAC / InfraredDC).

[0055] In view of the above, this disclosure provides a better method to determine which bSpO2 values ​​obtained during a specified time window, a sliding measurement window, should be averaged to produce the most accurate SpO2 output.

[0056] This method evaluates the statistical distribution of bSpO2 values ​​obtained during a specified time window and uses the measurements that are most consistent with each other and fall within a range that includes the dominant consistent measurement to calculate the average output SpO2.

[0057] After each bSpO2 determination or after an incremental time interval (e.g., 1 second), the window "slides" by removing the oldest bSpO2 value from the array and adding the latest bSpO2 value. We have observed that when the red and infrared light signals are noise-free and free of motion artifacts, the statistical distribution of bSpO2 values ​​remains within a narrow SpO2 range. When artifacts are present in these signals, or when the patient's actual SaO2 changes rapidly, the distribution of bSpO2 values ​​widens, the number of consistent bSpO2 values ​​decreases, and sometimes even leads to a bimodal distribution.

[0058] In order to distinguish between bSpO2 measurements associated with noise and motion artifacts and bSpO2 measurements associated with rapidly changing physiological values, this disclosure determines whether the red and infrared light signals “track” each other in terms of AC and DC components, or whether one of the signals changes in an uncorrelated manner.

[0059] It is well known to those skilled in the art that the wavelengths of red and infrared light used in pulse oximeters are selected to maximize the change in red light absorption as SaO2 changes, and to minimize the effect of SaO2 changes on the infrared light absorption signal. Based on these red and infrared light characteristics, a clean bSpO2 measurement and an artifact-related bSpO2 value can be distinguished by assessing whether the red and infrared light signals track each other (potential motion artifacts) or change independently with limited changes in the behavior of the infrared light signal (rapid changes in patient SaO2).

[0060] When analysis of red and infrared light signal tracking indicates that artificial bSpO2 values ​​may exist in the bSpO2 data array, this disclosure uses more stringent criteria to select bSpO2 values ​​for averaging to determine the output SpO2.

[0061] When analysis of red and infrared light signal tracking indicates that there may be little or no artifacts in the SpO2 array, this disclosure calculates the output SpO2 by averaging over a wide range of bSpO2 values. This larger range is designed to capture most of the varying bSpO2 measurements within the array as the average output SpO2. Using a wider range of bSpO2 values ​​allows the average output SpO2 to respond more quickly to changing patient conditions compared to using a narrower averaging range.

[0062] Although this choice of averaging range may seem counterintuitive, it is effective because clean red and infrared light signals representing constant patient SpO2 typically produce bSpO2 values ​​that are highly consistent between each beat, and the averaging result is the same regardless of whether the range of values ​​used to calculate SpO2 is narrow or wide.

[0063] While not necessarily part of this disclosure, the inventors of this disclosure recognize that all pulse oximeters include signal processing of red and infrared light signals prior to bSpO2 calculation in order to distinguish signal peaks associated with pulsatile activity in the patient's circulation from those associated with noise and motion artifacts. This processing includes: filtering to attenuate signal components with frequencies more likely to be associated with artifacts than the actual circulatory pulses; rate checking to verify that the detected pulse rate is within the physiological range and relatively consistent from pulse to pulse (beat-by-beat); and beat-by-beat consistency of the red / infrared light pulse amplitude. Red and / or infrared light signal pulses that do not meet these or other similar criteria are rejected for bSpO2 calculation.

[0064] In one example, the statistical processing described above is implemented using a histogram of bSpO2 values ​​collected over a sliding time window. This process involves organizing the bSpO2 values ​​into a histogram; calculating the width of the histogram bins to be used; identifying bins containing the dominant number of bSpO2 values; and then selecting bins (based on red / infrared light tracking analysis) whose data will be averaged to calculate the SpO2 output value.

[0065] In practice, signal processing systems must also include means for determining when the red / infrared peak amplitude is too small to be processed by the system, and when the bSpO2 value observed in the sliding window is so widely dispersed that SpO2 cannot be accurately determined.

[0066] When noise becomes too severe to obtain an accurate SpO2 value, the SpO2 value will not be displayed, for example, it will not be shown on the screen. Example 1: Motion artifacts persist for a long time, and an accurate bSpO2 value does not exist within the calculation period (called a specified time window, such as 12 seconds). Example 2: Cardiac arrest; there is no heartbeat or pulse. In both cases, the SpO2 value may not be calculated.

[0067] Figure 1a A flowchart illustrating a method 100a for processing blood oxygen signals according to an embodiment of the present disclosure is shown. Figure 1a As shown, the method 100a for processing blood oxygenation signals may include the following steps: In step 102 (collection step), multiple pulse oximetry (bSpO2) values ​​measured during a specified time window are collected. In step 103 (judgment step), it is determined whether the range of the multiple pulse oximetry (bSpO2) values ​​measured during the specified time window meets predetermined conditions. In step 104 (processing step), based on the judgment result of step 103, a range for calculating bSpO2 values ​​is selected from the multiple bSpO2 values, and mathematical statistics are calculated using the bSpO2 values ​​within the selected range to obtain the SpO2 value used as the blood oxygen saturation output value.

[0068] A pulse oximeter measures arterial oxygen saturation by shining specific wavelengths of red and infrared light through a patient's peripheral tissue (such as a finger, earlobe, or toe) onto a detector on the opposite side of the tissue bed. SpO2 is calculated by measuring the intensity of red and infrared light received by the detector at the DC trough and AC peak values ​​during each cardiac cycle. These AC peaks and DC troughs occur when blood flow through the tissue bed reaches its maximum and minimum values, respectively.

[0069] like Figure 2aAs shown, the pulsating component parameters (also known as AC components) and stable component parameters (also known as DC components) of red and infrared light signals can be used, using the formula bSPo2=K*(Red AC / Red DC ) / (Infrared AC / Infrared DC To calculate bSpO2, where (Red) AC / Red DC (Infrared) is the ratio of the AC component to the DC component of the red light signal. AC / Infrared DC K is the ratio of the AC component to the DC component of the infrared light signal, and K is a curve representing each (Red) AC / Red DC ) / (Infrared AC / Infrared DC ) has a corresponding K, for example, K follows (Red AC / Red DC ) / (Infrared AC / Infrared DC It increases with the increase of ), and tends to stabilize after increasing to a certain value.

[0070] Thus, by acquiring red and infrared light signals during a specified sliding time window, and calculating multiple bSpO2 values ​​based on these acquired signals using the aforementioned method, the time window can then be slid, for example, by 1 second. That is, the next acquisition again comes from a time window that is slightly offset in time. Multiple bSpO2 values ​​for the next time window can also be calculated using the aforementioned method based on the red and infrared light signals acquired during the next time window. Therefore, multiple bSpO2 values ​​measured during a specified sliding time window can be collected. It should be understood that the length of the time window and the duration of each slide can be adjusted according to the sampling frequency or other criteria related to the system's responsiveness to changes in the patient's condition.

[0071] For example, such as Figure 2bAs shown, the time window can be 12 seconds long, used to store data within that time period. After each data acquisition, if the time difference between the latest and oldest elements within the sliding time window exceeds 12 seconds, the first element is deleted, and the sliding time window shifts forward, always ensuring that the time difference between the latest and oldest elements within the sliding time window does not exceed 12 seconds. For example, the first time window (0s to 12s) slides to the second time window (1s to 13s), and the first time window (1s to 13s) slides to the third time window (2s to 14s). It should be understood that the time window length is not limited to 12 seconds; it can be any other suitable value, such as 14 seconds.

[0072] like Figure 1e As shown, red light signals and infrared light signals can be acquired during a specified time window. Correspondingly, the raw data of the red light signals and infrared light signals acquired during the specified time window can be input (step 120); noise reduction processing is performed on the red light signals and infrared light signals (step 122); using the noise-reduced red light signals and infrared light signals, SpO2 values ​​are calculated sequentially to obtain multiple bSpO2 values ​​(step 124); then steps 103 and 104 are executed. Noise reduction processing may include, but is not limited to, filtering. Filtering can be implemented through a processor program; the program module implementing this function can be called a filter. For example, the cutoff frequency of a notch filter can be set to 50Hz and 60Hz. By performing filtering processing, the red light signals and infrared light signals can be preprocessed, thereby improving the accuracy of the calculated bSpO2 values ​​and thus improving the detection accuracy of SpO2 values.

[0073] Predefined criteria are used to select the statistically dominant range of bSpO2 values ​​from multiple collected bSpO2 values ​​for use in SpO2 value calculation. Appropriate criteria can be set according to actual application needs. For example, the statistical distribution of the collected bSpO2 values ​​can be consistent with the statistical distribution of the statistically dominant bSpO2 values ​​(referred to as the statistically dominant distribution). bSpO2 values ​​whose statistical distribution is inconsistent with the statistically dominant distribution contain noise caused by motion artifacts and need to be removed; conversely, bSpO2 values ​​whose statistical distribution is consistent with the statistically dominant distribution do not contain noise caused by motion artifacts and need to be retained for SpO2 value calculation.

[0074] Accordingly, in one implementation, if the statistical distribution of the collected bSpO2 values ​​meets a predetermined condition that the statistical distribution among the collected bSpO2 values ​​is consistent with the statistical distribution of the statistically dominant bSpO2 values, then the range of bSpO2 values ​​whose statistical distribution is consistent with the statistically dominant distribution is selected as the range for calculating the SpO2 value, and the SpO2 value is calculated using the bSpO2 values ​​within the selected range.

[0075] In step 104, if the range of the collected bSpO2 values ​​meets a predetermined condition, the range of the statistically dominant set of bSpO2 values ​​collected during a specified time window can be selected from the collected bSpO2 values ​​as the range for calculating the SpO2 value. In this way, the bSpO2 values ​​in the selected range are those bSpO2 values ​​that are within the statistically dominant range of the values ​​measured during the specified time window, and do not include noise due to motion artifacts.

[0076] Therefore, compared to existing technologies that directly use numerous bSpO2 values ​​to calculate the blood oxygen saturation output value, i.e., the SpO2 value, this disclosure uses only those bSpO2 values ​​within the statistically dominant range of values ​​measured during a specified time window. The range used to calculate the SpO2 value is selected from numerous bSpO2 values ​​(excluding noisy data corresponding to motion artifacts) based on a judgment result that the range of multiple bSpO2 values ​​measured during the specified time window meets predetermined conditions, and the SpO2 value is calculated using bSpO2 values ​​within the selected range. Therefore, this disclosure uses bSpO2 values ​​with less noise to calculate the SpO2 value compared to existing technologies, thereby improving the detection accuracy of the SpO2 value.

[0077] In one possible implementation, performing mathematical statistics calculations using bSpO2 values ​​within a selected range to obtain the SpO2 value includes: calculating the average of all bSpO2 values ​​within the selected range as the SpO2 value.

[0078] Regarding step 104, in a specific implementation of mathematical statistics, the average value of the data can be calculated. After selecting a range of bSpO2 values ​​measured during a specified sliding time window for calculating the SpO2 value—that is, after removing non-physiological bSpO2 values—the SpO2 value can be calculated by averaging the bSpO2 values ​​within the selected range. However, other mathematical statistics well-known in the art can also be applied, as long as they can characterize the degree of variability in the signal. Statistical calculation methods can be extended to similarity analysis, difference analysis, range, mean absolute deviation, coefficient of variation, etc.

[0079] Therefore, compared to existing technologies that directly perform statistical calculations on numerous bSpO2 values, such as calculating the average of the bSpO2 values ​​to obtain the SpO2 value, this disclosure performs statistical calculations on bSpO2 values ​​within a statistically dominant range selected from numerous bSpO2 values ​​during a specified sliding time window, such as calculating the average of all bSpO2 values ​​within that range to obtain the SpO2 value. Since the bSpO2 values ​​within the statistically dominant range are the bSpO2 values ​​corresponding to the signal components after removing motion artifacts (i.e., removing non-physiological bSpO2 values), the detection accuracy of SpO2 values ​​can be improved compared to existing technologies.

[0080] In one specific implementation of step 104, if the predetermined conditions are met, a probability analysis is performed on the plurality of bSpO2 values ​​(i.e., the probability of occurrence of each bSpO2 value is analyzed), and the selected range is selected from the plurality of bSpO2 values ​​based on the result of the probability analysis. The selected range of bSpO2 values ​​is then used to perform mathematical statistics calculations to obtain the SpO2 value.

[0081] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, a range for calculating the SpO2 value can be selected by performing probability analysis on multiple bSpO2 values ​​and based on the probability analysis result, and the SpO2 value can be calculated using the bSpO2 values ​​within the selected range.

[0082] like Figure 1b As shown, it can be determined whether all bSpO2 values ​​among multiple bSpO2 values ​​are within the range (step 105). If it is determined that all bSpO2 values ​​are within the range, it is determined that the predetermined condition is met; otherwise, it is determined that the predetermined condition is not met. If the predetermined condition is met, step 106 is executed to perform probability analysis on the multiple bSpO2 values ​​and select the selected range from the multiple bSpO2 values ​​based on the result of the probability analysis. Then, step 107 is executed to perform mathematical statistics calculations using the bSpO2 values ​​within the selected range to obtain the SpO2 value. Then, step 108 can be executed to output (display) the calculated SpO2 value.

[0083] In one specific implementation of the value range, the value range may include at least one of the following: the difference between the maximum and minimum values ​​among a plurality of bSpO2 values ​​is within ±2%, the difference is within ±3%, the difference is within ±4%, the difference is within ±5%, and the difference is within ±6%.

[0084] For a difference within ±2%, possible values ​​are [95%, 99%], [90%, 94%], [85%, 89%], [80%, 84%], [75%, 79%], and [70%, 74%]. For a difference within ±3%, possible values ​​are [93%, 99%], [86%, 92%], and [79%, 85%]. For a difference within ±4%, possible values ​​are [91%, 99%], [82%, 90%], and [73%, 81%]. For a difference within ±5%, possible values ​​are [89%, 99%] and [78%, 88%]. For a difference within ±6%, possible values ​​are [87%, 99%] and [74%, 86%].

[0085] In one specific implementation of step 104, if the predetermined conditions are met, a probability analysis is performed on the plurality of bSpO2 values ​​to obtain the probability of occurrence of each bSpO2 value among the plurality of bSpO2 values, and the selected range is selected from the plurality of bSpO2 values, wherein the probability of occurrence of each bSpO2 value within the selected range is greater than a probability threshold.

[0086] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by performing probability analysis on multiple bSpO2 values ​​and using whether the probability of occurrence is greater than a probability threshold as the standard. This probability threshold can be a fixed threshold or a dynamic threshold, and can be set according to actual application requirements.

[0087] In one specific implementation of step 104, if the predetermined conditions are met, a probability analysis is performed on the plurality of bSpO2 values ​​to obtain the occurrence probability of each bSpO2 value among the plurality of bSpO2 values. The bSpO2 values ​​that are ranked in the top N from the plurality of bSpO2 values ​​according to the occurrence probability from largest to smallest are selected as the bSpO2 values ​​within the selected range.

[0088] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by performing probability analysis on the multiple bSpO2 values ​​and ranking them according to their probability of occurrence. For example, the bSpO2 value with the highest probability of occurrence can be selected as each bSpO2 value within the selected range, in which case N is 1; or, the bSpO2 values ​​with the highest and second highest probabilities of occurrence can be selected as each bSpO2 value within the selected range, in which case N is 2.

[0089] Therefore, compared with the prior art, this disclosure uses a bSpO2 value with less noise selected through probability analysis to calculate the SpO2 value, thereby improving the detection accuracy of the SpO2 value.

[0090] In one specific implementation of step 104, if the predetermined conditions are met, a counting analysis is performed on the plurality of bSpO2 values ​​(i.e., the frequency of occurrence of each bSpO2 value is analyzed), and the selected range is selected from the plurality of bSpO2 values ​​based on the results of the counting analysis. The selected range of bSpO2 values ​​is then used to perform mathematical statistics calculations to obtain the SpO2 value.

[0091] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by performing a counting analysis on the multiple bSpO2 values ​​and based on the counting analysis result.

[0092] like Figure 1c As shown, it can be determined whether all bSpO2 values ​​among multiple bSpO2 values ​​are within the range (step 105). If it is determined that all bSpO2 values ​​are within the range, it is determined that the predetermined condition is met; otherwise, it is determined that the predetermined condition is not met. If the predetermined condition is met, step 109 is executed to perform a count analysis on the multiple bSpO2 values ​​and select the selected range from the multiple bSpO2 values ​​based on the result of the count analysis. Then, step 110 is executed to perform mathematical statistics calculations using the bSpO2 values ​​within the selected range to obtain the SpO2 value. Then, step 108 can be executed to output (display) the calculated SpO2 value.

[0093] In one specific implementation of step 104, if the predetermined conditions are met, a counting analysis is performed on the plurality of bSpO2 values ​​to obtain the count value of each bSpO2 value among the plurality of bSpO2 values. The selected range is then selected from the plurality of bSpO2 values, wherein the count value of each bSpO2 value within the selected range is greater than a counting threshold.

[0094] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by counting and analyzing the multiple bSpO2 values ​​and using whether the count value is greater than a counting threshold as the standard. This counting threshold can be a fixed threshold or a dynamic threshold, and can be set according to actual application requirements.

[0095] In one specific implementation of step 104, if the predetermined conditions are met, a counting analysis is performed on the plurality of bSpO2 values ​​to obtain the count value of each bSpO2 value among the plurality of bSpO2 values. The bSpO2 values ​​that are ranked in the top N from the plurality of bSpO2 values ​​according to the count value from largest to smallest are selected as the bSpO2 values ​​within the selected range.

[0096] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by performing a counting analysis on multiple bSpO2 values ​​and using the sorting of the count values ​​as the standard. For example, the bSpO2 value with the largest count value can be selected as each bSpO2 value within the selected range, in which case N is 1; or, the bSpO2 values ​​with the largest and second largest count values ​​can be selected as each bSpO2 value within the selected range, in which case N is 2.

[0097] Therefore, compared with the prior art, this disclosure uses a bSpO2 value with less noise selected by counting analysis to calculate the SpO2 value, thereby improving the detection accuracy of the SpO2 value.

[0098] In one specific implementation of step 104, if the predetermined conditions are met, histogram analysis is performed on the plurality of bSpO2 values. Based on the results of the histogram analysis, a range for calculating the SpO2 value is selected from the plurality of bSpO2 values. Then, mathematical statistics are calculated using the bSpO2 values ​​within the selected range to obtain the SpO2 value. It should be understood that histogram analysis utilizes histograms for data analysis.

[0099] A histogram is a chart that plots the distribution of variable values ​​as a series of bars. Each bar covers a range of values ​​called a bin; the height of the bar indicates the number of data points whose value falls within the corresponding bin. Here, bSpO2 is the value of interest.

[0100] The bSpO2 value can be obtained as follows: [96, 96, 96, 96, 96, 97, 96, 96, 95, 95, 95, 95, 96, 96, 96, 96, 97, 97, 96, 97, 97, 97, 97, 97, 97, 97, 96 ... Figure 3 The histograms shown indicate that the percentages of bSpO2 values ​​are 96% for 32 values, 95% for 4 values, and 97% for 15 values.

[0101] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by performing histogram analysis on multiple bSpO2 values ​​and based on the histogram analysis results.

[0102] In one specific implementation of step 104, if the predetermined conditions are met, histogram analysis is performed on the plurality of bSpO2 values ​​to obtain the histogram corresponding to the plurality of bSpO2 values. A target warehouse is selected from the warehouses in the histogram, wherein the bSpO2 values ​​in the target warehouse are bSpO2 values ​​within the selected range.

[0103] In this embodiment, if the judgment result in step 102 meets the predetermined conditions, the range for calculating the SpO2 value can be selected by performing histogram analysis on the multiple bSpO2 values ​​and using the target warehouse as the standard.

[0104] Therefore, compared with the prior art, this disclosure uses a bSpO2 value with less noise selected through histogram analysis to calculate the SpO2 value, thereby improving the detection accuracy of the SpO2 value.

[0105] It should be understood that probability analysis, counting analysis, and histogram analysis are only a few of the implementation methods of this disclosure. This disclosure is not limited to these, and any other suitable analysis method can be used to select the range for calculating the bSpO2 value.

[0106] In a specific implementation of step 102, such as Figure 1d As shown, it can be determined whether all bSpO2 values ​​are within the range (step 105). If all bSpO2 values ​​are within the range, it is determined that the predetermined condition is met; otherwise, it is determined that the predetermined condition is not met. Regarding the range of values, please refer to the previous description, which will not be repeated here.

[0107] Figure 5 yes Figure 4 The histogram of the waveform in the clean signal period of 10s-24s is shown below. Figure 5 As shown, during the 14-second period of the virtually artifact-free red signal, all pulse-calculated SpO2 values ​​were similar and consistently within the range of 96.5% to 100%. Plotting these pulse-calculated SpO2 values ​​in a histogram of the coverage assessment period yields a high-resolution chart encompassing all data points. Figure 5 The histogram shows a total of 18 SpO2 values ​​in the range of [96.5%, 100%].

[0108] Figure 6 yes Figure 4 The histogram of the waveform in the period 18s-32s is shown below. Figure 6 As shown, during this 14-second time period, the red signal, which was free of artifacts between seconds 18 and 24, was suddenly contaminated by motion artifacts starting at second 24 and continuing until second 32. When a histogram of the pulse-wise SpO2 (bSpO2) values ​​calculated during this period was plotted, these values ​​were found to be distributed across a wide range of 55% to 100% SpO2. The consistency of oxygen saturation values ​​observed during earlier analyses has now been replaced by SpO2 values ​​that are widely dispersed across the measurement range. Although the histogram continues to show the most common SpO2 values ​​in the 92%–100% range, the height of the bins is lower than in the previous plot, and more and shorter bins now appear in the histogram. The presence of these additional new bins indicates a change in the R / IR signal. In this case, the altered signal produces less consistent pulse-wise SpO2 (bSpO2) values ​​and a large number of spurious, highly variable additional values. The reduced consistency and the additional widely dispersed bins are a result of errors in the calculated SpO2 values ​​caused by motion artifacts.

[0109] Figure 7 yes Figure 4 The histograms of the waveform in the full noise period of 23s-37s and 25s-39s are shown below. Figure 7 As shown, as the analysis period shifted from 18-32 seconds to 23-37 seconds, the previously artifact-free and artifact-contaminated beat-by-beat SpO2 values ​​became one of the fully contaminated R / IR signals, leading to errors in beat-by-beat SpO2 calculation. Consequently, additional bins appeared in the histogram, spread across a wider range than previously observed. The number of observations in the highest histogram bins also decreased accordingly. This broad dispersion in the calculated SpO2 values ​​is a characteristic of either rapid changes in the patient's true SpO2 or artifact contamination of the SpO2 signal. By measuring characteristics of the real-time R and IR signals during analysis (such as peak-to-peak amplitude consistency, beat-to-beat cycle or rate consistency, pulse duration consistency, pulse area consistency, and temporal correlation between peaks and troughs in the red and infrared signals), algorithms can be created to determine whether the wide dispersion in the histogram bins is due to significant changes in the patient's SpO2 or the presence of motion artifacts. Histogram-based techniques or other consistency statistics can also be used to analyze the consistency of beat-by-beat amplitude, rate, interval, and cycle. Figure 7 The histogram shows a total of 7 SpO2 values ​​in the range of [91.5%, 100%].

[0110] Over time, the R / IR and pulsed SpO2 signals slid within a 14-second analysis window, and observations were made. Figure 7 The additional histogram shown is illustrated. The histogram for the period from 25 to 39 seconds continues to show a wide dispersion in the calculated SpO2 values, as the signal is heavily contaminated by motion artifacts during this period.

[0111] Figure 8 yes Figure 4 The histograms of the waveform in the low-noise period and the clean period of 38s-52s are shown below. Figure 8 As shown, however, during the analysis period between 38 and 53 seconds, most of the widely dispersed bins observed during the artifact contamination period had disappeared, and the number of observations in the highest bin was approaching its maximum possible value. The signal during this period was much cleaner than the signals in the previous periods, and the consistency of the calculated SpO2 values ​​was also greatly improved. Stable, artifact-free R and IR signals were obtained again.

[0112] Finding the dominant cell in the histogram of SpO2 values ​​and using it for SpO2 calculation can improve the ability to distinguish motion artifacts from physiological signals and calculate SpO2 more accurately.

[0113] When a patient's SpO2 suddenly decreases or increases spontaneously (e.g., during sleep, drug-induced apnea, or recovery from hypoventilation), a histogram of SpO2 frequency values ​​will show the distribution within the range of SpO2 variation. In many cases, it is difficult to determine from histogram data alone whether the increase in SpO2 value dispersion is a result of physiological processes or artifact contamination of the R / IR signal.

[0114] Based on this, in a specific implementation of steps 102 and 104, such as Figure 1d As shown, if all bSpO2 values ​​among the plurality of bSpO2 values ​​are within the range (yes in step 105), it is determined that the predetermined condition is met. A first number of warehouses with heights greater than a height threshold are selected from the histogram as target warehouses. The bSpO2 values ​​within the target warehouses are within the selected range (step 111). Mathematical statistics are performed on the selected range of bSpO2 values ​​to obtain the SpO2 value (step 116), and the SpO2 value is output (step 118). For example, if the difference between the maximum and minimum values ​​among the plurality of bSpO2 values ​​is within ±2%, target warehouses are selected from the histogram corresponding to all bSpO2 values ​​based on warehouse height. The selected target warehouses have heights greater than the height threshold and the number is a first number.

[0115] In one possible implementation, if the number of warehouses whose bSpO2 values ​​all fall within the range of a plurality of bSpO2 values ​​is a first quantity, then all of these bSpO2 values ​​are selected as bSpO2 values ​​within the range used to calculate SpO2 values. The average of all bSpO2 values ​​within the window can then be calculated, and this average is the SpO2 value displayed on the monitor. Conversely, if the number of warehouses whose bSpO2 values ​​all fall within the range is greater than the first quantity, then a subset of warehouses is selected as target warehouses. All bSpO2 values ​​within these target warehouses fall within the range used to calculate SpO2 values, and the average of these bSpO2 values ​​can then be calculated, and this average is the SpO2 value displayed on the monitor.

[0116] In one specific implementation of steps 102 and 104, such as Figure 1d As shown, if not all bSpO2 values ​​are within the range of values, but all bSpO2 values ​​are greater than or equal to the first threshold (step 105 is no but step 112 is yes), it is determined that the predetermined condition is met. A first number of warehouses with a height greater than the height threshold are selected from the warehouses in the histogram as the target warehouses. The bSpO2 value in the target warehouse is the bSpO2 value within the selected range (step 108). Mathematical statistics are performed on the bSpO2 values ​​within the selected range to obtain the SpO2 value (step 116), and the SpO2 value is output (step 118).

[0117] In one possible implementation, the first threshold includes 92%, 94%, or 96%. For example, if the difference between the maximum and minimum values ​​among all the collected bSpO2 values ​​is not within ±2%, and if all the bSpO2 values ​​are greater than or equal to 94%, then target warehouses are selected from the histogram corresponding to all the bSpO2 values ​​based on the warehouse height, and the height of the selected target warehouses is greater than the height threshold and the number is a first quantity.

[0118] In one specific implementation of steps 102 and 104, such as Figure 1dAs shown, if not all bSpO2 values ​​are within the specified range, and not all bSpO2 values ​​are greater than or equal to the first threshold, but the red light signal and infrared light signal collected during the specified time window are free of motion artifacts (step 105 is no, step 112 is no and step 113 is yes), it is determined that the predetermined condition is met. A first number of warehouses with a height greater than the height threshold are selected from the warehouses in the histogram as the target warehouses. The bSpO2 value in the target warehouse is the bSpO2 value within the selected range (step 108). Mathematical statistics are performed on the bSpO2 values ​​within the selected range to obtain the SpO2 value (step 116), and the SpO2 value is output (step 118).

[0119] like Figure 9 As shown, the pulse waveform signal is similar in shape, amplitude, rate, and duration throughout the entire cycle, even though the pulse bSpO2 value varies gradually within a significant range during the cycle. Therefore, a criterion is introduced to determine whether red and infrared signals are free of motion artifacts. For example, if the difference between the maximum and minimum values ​​of all bSpO2 values ​​is not within ±2%, and not all bSpO2 values ​​are greater than or equal to 94%, and if the red and infrared signals are free of motion artifacts, then target chambers are selected from the histogram corresponding to all bSpO2 values ​​based on chamber height. The selected target chambers have a height greater than a height threshold and their number is the first set.

[0120] In one possible implementation, determining whether the red light signal and infrared light signal acquired during the specified time window are motion-artifact-free signals includes at least one of the following: determining whether the interpeak amplitudes of the red light signal and infrared light signal acquired during the specified time window are consistent; determining whether the beat cycle, rate, first-order difference, reciprocal first-order difference, or second-order difference of the red light signal and infrared light signal acquired during the specified time window are consistent; determining whether the pulse durations of the red light signal and infrared light signal acquired during the specified time window are consistent; and determining the specified time window period... The system determines whether the pulse areas of the red and infrared light signals acquired during the specified time window are consistent; whether the temporal correlation between the peaks and valleys of the red and infrared light signals acquired during the specified time window is consistent; whether the red light signals acquired during the specified time window are consistent with historical red light signals; whether the infrared light signals acquired during the specified time window are consistent with historical infrared light signals; whether the red light signals acquired during the specified time window are consistent with clean red light signals; and whether the infrared light signals acquired during the specified time window are consistent with clean infrared light signals.

[0121] In one specific implementation of steps 102 and 104, such as Figure 1dAs shown, if the predetermined conditions are not met (step 105 is no, step 112 is no and step 113 is no), the warehouse with the largest height in the histogram is selected as the target warehouse. The bSpO2 value in the target warehouse is the bSpO2 value within the selected range (step 114). Mathematical statistics are performed on the bSpO2 values ​​within the selected range to obtain the SpO2 value (step 116), and the SpO2 value is output (step 118).

[0122] In this implementation, consistency is first assessed by testing whether the pulse-wise SpO2 values ​​are all within ±2% of each other (e.g., 95% to 99%). If all values ​​are consistent within ±2%, or all values ​​are above 94%, the SpO2 value displayed by the monitor is calculated as the average of all points within the window. When the pulse-wise SpO2 values ​​are not perfectly consistent within ±2%, and some SpO2 values ​​are observed to be below 94%, the consistency of the interpeak amplitude and R rate of the IR signal within the window is evaluated.

[0123] If there is consistency in amplitude and rate between R / IR peaks in the analysis window, the SpO2 values ​​in the three or four highest bins are averaged to produce the displayed SpO2 values. However, if the amplitude and rate consistency does not meet a predetermined threshold, only the SpO2 values ​​in the highest histogram bins are averaged to produce the displayed saturation output.

[0124] In one specific implementation of steps 102 and 104, such as Figure 1d As shown, if the predetermined conditions are not met (step 105 is no, step 112 is no, and step 113 is no), when there are multiple adjacent warehouses, the sum of the values ​​of the multiple adjacent warehouses is compared with the value of the warehouse with the largest height. If the sum of the values ​​of the multiple adjacent warehouses is greater than the value of the warehouse with the largest height, then the multiple adjacent warehouses are selected as the target warehouse. If the sum of the values ​​of the multiple adjacent warehouses is equal to or less than the value of the warehouse with the largest height, then the warehouse with the largest height is selected as the target warehouse.

[0125] In one possible implementation, selecting a first number of warehouses with heights greater than a height threshold as the target warehouses includes: selecting the top N warehouses sorted by height from largest to smallest as the target warehouses; or, selecting the top N+1 warehouses sorted by height from largest to smallest as the target warehouses. For example, N can be 3. Of course, N can also take other values, and the value of N can be adjusted according to actual application requirements. Figure 10 As shown, there are only 3 warehouses and 3 warehouses need to be selected. Therefore, the warehouses with the highest, second highest, and third highest prices are all selected as target warehouses. The average value of bSpO2 in the target warehouses is calculated as the SpO2 value.

[0126] In a possible implementation, when the sum of all bSpO2 values in the bin ranked N + 1 is greater than or equal to the sum of all bSpO2 values in the bin ranked N, select the top N + 1 bins as the target bins; when the sum of all bSpO2 values in the bin ranked N + 1 is less than the sum of all bSpO2 values in the bin ranked N, select the top N bins as the target bins.

[0127] For example, as Figure 11 shown, when the sum of all bSpO2 values in the bin ranked the 4th highest is greater than or equal to the sum of all bSpO2 values in the bin ranked the 3rd highest, select the top 4 highest bins as the target bins; otherwise, as Figure 12 shown, select the top 3 highest bins as the target bins. That is to say, when there are multiple bins ranked the 4th highest and the sum of all bSpO2 values in all the bins ranked the 4th highest is greater than or equal to the sum of all bSpO2 values in the bin ranked the 3rd highest, use the 4 highest bins; otherwise, use the 3 highest bins.

[0128] In a possible implementation, perform histogram analysis on the multiple bSpO2 values to obtain the histogram corresponding to the multiple bSpO2 values, including: by fixing the width and the center of the bins of the histogram, such as [100%, 95%], [95%, 90%], …, [5%, 0%], perform histogram analysis on the multiple bSpO2 values to obtain the histogram corresponding to the multiple bSpO2 values. In this example, the width of the bin is fixed at 5%. As Figure 3 stated, the width of the bin is fixed at 1%. As Figure 12 stated, the width of the bin is fixed at 5%. If edges(k) < X(i) <= edges(k + 1) or edges(k) <= X(i) < edges(k + 1), then X(i) is in the kth bin, where X(i) is the bSpO2 value, and edges(k) and edges(k + 1) are the boundaries of the kth bin and the (k + 1)th bin respectively. The last bin also includes the right bin boundary. Therefore, if edges(end - 1) <= X(i) <= edges(end), then this bin contains X(i), where X(i) is the bSpO2 value, and edges(end - 1) and edges(end) are the boundaries of the second last bin and the last bin respectively.

[0129] In one possible implementation, histogram analysis of the plurality of bSpO2 values ​​to obtain the histogram corresponding to the plurality of bSpO2 values ​​includes: performing histogram analysis on the plurality of bSpO2 values ​​by fixing the width of the bins in the histogram but adapting the center of the bins. For example, adapting the center of the bins in the histogram includes calculating the center of the bin based on the bSpO2 values ​​within the bin. For example, the center of the bin in the histogram is the average value of the bSpO2 values ​​within the bin. That is, the average value of the bSpO2 values ​​within the bin is used as the center of the bin, and then the bin is [center - 2.5%, center + 2.5%], until it reaches 100% and 0%. If the average value of the bSpO2 values ​​is equal to 96%, then the boundary of the bin is equal to [3.5%, 8.5%, ..., 78.5%, 83.5%, 88.5%, 93.5%, 100%].

[0130] In one possible implementation, histogram analysis of the plurality of bSpO2 values ​​to obtain the histograms corresponding to the plurality of bSpO2 values ​​includes: performing histogram analysis on the plurality of bSpO2 values ​​by making the width of the bin adaptive while keeping the center of the bin fixed, thereby obtaining the histograms corresponding to the plurality of bSpO2 values. The width of the bin can be adjusted according to the data distribution of the plurality of bSpO2 values, or the width of the bin can be adjusted to a preset width, or any existing suitable method can be used to adjust the width of the bin.

[0131] In one possible implementation, performing histogram analysis on the plurality of bSpO2 values ​​to obtain the histograms corresponding to the plurality of bSpO2 values ​​includes: performing histogram analysis on the plurality of bSpO2 values ​​in a manner that adapts both the width and center of the bin in the histogram. The methods for adapting the bin width and center are described above and will not be repeated here.

[0132] Figure 13 and 17 The following are schematic diagrams showing the waveforms of bSpO2 according to an embodiment of the present disclosure. Figures 14-16 and Figures 18-19 As shown, it sequentially displays the targets for Figure 13 and 17The data were analyzed using histograms constructed according to the Freedman-Diaconis rule (a), the Scott rule (b), a rule with fixed bin widths and centers (c), and a rule with fixed bin widths but adaptive bin centers (d). The Freedman-Diaconis rule is less sensitive to outliers and may be more suitable for data with heavy-tailed distributions. It uses a bin width of 2*IQR(X(:))*numel(X)^(-1 / 3), where IQR is the interquartile range of X. The Scott rule is optimal if the data approximates a normal distribution. This rule also applies to most other distributions. It uses a bin width of 3.5*std(X(:))*numel(X)^(-1 / 3). Figure 13 The results show that the bSpO2 value increased from 70% to 100% within a period of 975s-989s.

[0133] For example, in Figure 14 In the above embodiment, for histogram (c), warehouses with values ​​[65, 70] can be selected as target warehouses according to the method described above; for histogram (d), warehouses with values ​​[68.5, 73.5] and [63.5, 68.5] can be selected as target warehouses according to the method described above. Figure 16 In the above embodiment, for histogram (c), warehouses with values ​​of [95, 100] and [90, 95] can be selected as target warehouses according to the method described above; for histogram (d), warehouses with values ​​of [92.5, 100] and [87.5, 92.5] can be selected as target warehouses according to the method described above. Figure 18 In the above embodiment, for histogram (c), warehouses in the range [95, 100] can be selected as target warehouses according to the method described above; for histogram (d), warehouses in the range [90, 100] can be selected as target warehouses according to the method described above. Figure 19 In the above embodiment, for histogram (c), warehouses of [95, 100] and [90, 95] can be selected as target warehouses according to the above method of this embodiment; for histogram (d), warehouses of [92.5, 100] can be selected as target warehouses according to the above method of this embodiment.

[0134] By comparison Figures 14-16 and Figures 18-19 It can be seen that fixing the width of the bins in the histogram while making the center of the bins adaptive is more beneficial for SpO2 calculation. In other words, obtaining a histogram corresponding to multiple bSpO2 values ​​by fixing the width of the bins in the histogram while making the center of the bins adaptive is better than obtaining a histogram by other methods.

[0135] Figure 20 The effect of motion artifact-contaminated red (R) and infrared (IR) pulse oxygen saturation signals on the calculated SpO2 value is shown. Before 25 seconds and after 40 seconds in the figure, the R and IR signals clearly reflect the pulsating blood flow in the monitored tissue bed and the stable bSpO2 value within the 99-100% range. However, between 25 and 40 seconds, these signals are masked by increased motion artifacts. Therefore, the calculated SpO2 value during this period deviates significantly from the patient's true stable SpO2. The SpO2 curve labeled "Existing SpO2 Results" illustrates this erroneous bias in the output SpO2 value. The "Updated SpO2 Results" curve in the figure shows that the newly discovered motion artifact suppression algorithm of this disclosure significantly reduces the measured SpO2 error when the R and IR signals associated with arterial blood flow pulsation are masked by signal artifacts.

[0136] Figure 21 A block diagram of a blood oxygen detection device 200 according to an embodiment of the present disclosure is shown. Figure 21 As shown, the blood oxygen detection device 200 includes a processor 202 and a memory 204 for storing processor-executable instructions. The processor 202 is configured to determine whether a range of multiple pulse oximetry (bSpO2) values ​​measured during a specified time window meets predetermined conditions, select a range from the multiple bSpO2 values ​​for calculating the bSpO2 value based on the determination result, and perform mathematical statistics calculations using the bSpO2 values ​​within the selected range to obtain the SpO2 value used as the blood oxygen saturation output value.

[0137] In one specific implementation, the processor 202 is configured to: perform probability analysis on the plurality of bSpO2 values ​​when it is determined that the predetermined conditions are met; select the selected range from the plurality of bSpO2 values ​​according to the result of the probability analysis; and use the bSpO2 values ​​within the selected range to perform mathematical statistics calculation to obtain the SpO2 value.

[0138] In one specific implementation, the processor 202 is configured to: perform probability analysis on the plurality of bSpO2 values ​​when it is determined that the predetermined conditions are met, obtain the probability of occurrence of each bSpO2 value among the plurality of bSpO2 values, and select the selected range from the plurality of bSpO2 values, wherein the probability of occurrence of each bSpO2 value within the selected range is greater than a probability threshold.

[0139] In one specific implementation, the processor 202 is configured to: perform a counting analysis on the plurality of bSpO2 values ​​when it is determined that the predetermined conditions are met; select the selected range from the plurality of bSpO2 values ​​based on the result of the counting analysis; and perform mathematical statistics calculations on the bSpO2 values ​​within the selected range to obtain the SpO2 value.

[0140] In one specific implementation, the processor 202 is configured to: perform counting analysis on the plurality of bSpO2 values ​​when it is determined that the predetermined conditions are met, obtain the count value of each bSpO2 value among the plurality of bSpO2 values, and select the selected range from the plurality of bSpO2 values, wherein the count value of each bSpO2 value within the selected range is greater than a counting threshold.

[0141] In one specific implementation, the processor 202 is configured to: perform histogram analysis on the plurality of bSpO2 values ​​when it is determined that the predetermined conditions are met; select the selected range from the plurality of bSpO2 values ​​based on the results of the histogram analysis; and perform mathematical statistics calculations on the bSpO2 values ​​within the selected range to obtain the SpO2 value.

[0142] In one specific implementation, the processor 202 is configured to: perform histogram analysis on the plurality of bSpO2 values ​​when it is determined that the predetermined conditions are met, obtain the histogram corresponding to the plurality of bSpO2 values, select a target bin from the bins of the histogram, wherein the bSpO2 values ​​in the target bin are bSpO2 values ​​within the selected range.

[0143] In one specific implementation, the processor 202 is configured to: determine whether all bSpO2 values ​​among the plurality of bSpO2 values ​​are within the range of values, wherein the predetermined condition includes all bSpO2 values ​​among the plurality of bSpO2 values ​​being within the range of values.

[0144] In one specific implementation, the value range includes at least one of the following: the difference between the maximum and minimum values ​​among the plurality of bSpO2 values ​​is within ±2%, the difference is within ±3%, the difference is within ±4%, the difference is within ±5%, and the difference is within ±6%.

[0145] In one specific implementation, if all bSpO2 values ​​among the plurality of bSpO2 values ​​are within the range of the given values, it is determined that the predetermined condition is met, and a first number of warehouses with heights greater than a height threshold are selected from the warehouses in the histogram as the target warehouses.

[0146] In one specific implementation, if not all bSpO2 values ​​are within the range of the given values, but all bSpO2 values ​​are greater than or equal to a first threshold, it is determined that the predetermined condition is met, and a first number of warehouses with heights greater than a height threshold are selected from the warehouses in the histogram as the target warehouses.

[0147] In one specific implementation, if not all bSpO2 values ​​are within the specified range and not all bSpO2 values ​​are greater than or equal to the first threshold, but the red light signal and infrared light signal collected during the specified time window are free of motion artifacts, then the predetermined condition is met, and a first number of bins with heights greater than the height threshold are selected from the bins of the histogram as the target bins.

[0148] In one specific implementation, the first threshold includes 92%, 94%, or 96%.

[0149] In one specific implementation, the processor 202 is configured to: if it is determined that the predetermined condition is not met, select the warehouse with the largest height from the warehouses in the histogram as the target warehouse; or, if it is determined that the predetermined condition is not met, when there are multiple adjacent warehouses, compare the sum of the values ​​of the multiple adjacent warehouses with the value of the warehouse with the largest height; if the sum of the values ​​of the multiple adjacent warehouses is greater than the value of the warehouse with the largest height, then select the multiple adjacent warehouses as the target warehouse; if the sum of the values ​​of the multiple adjacent warehouses is equal to or less than the value of the warehouse with the largest height, then select the warehouse with the largest height as the target warehouse.

[0150] In one specific implementation, processor 202 is configured to calculate the average of all bSpO2 values ​​within the selected range as the SpO2 value.

[0151] In one specific implementation, the processor 202 is configured to: determine whether the peak-to-peak amplitudes of the red light signal and the infrared light signal acquired during the specified time window are consistent; determine whether the beat cycle, rate, first-order difference, reciprocal first-order difference, or second-order difference of the red light signal and the infrared light signal acquired during the specified time window are consistent; determine whether the pulse durations of the red light signal and the infrared light signal acquired during the specified time window are consistent; determine whether the pulse areas of the red light signal and the infrared light signal acquired during the specified time window are consistent; determine whether the temporal correlation between the peaks and troughs of the red light signal and the infrared light signal acquired during the specified time window is consistent; determine whether the red light signal acquired during the specified time window is consistent with historical red light signals; determine whether the infrared light signal acquired during the specified time window is consistent with historical infrared light signals; determine whether the red light signal acquired during the specified time window is consistent with red light signals without noise and motion artifacts; and determine whether the infrared light signal acquired during the specified time window is consistent with infrared light signals without noise and motion artifacts.

[0152] In one specific implementation, the processor 202 is configured to: perform histogram analysis on the plurality of bSpO2 values ​​by fixing both the width and center of the histogram bins, to obtain a histogram corresponding to the plurality of bSpO2 values; or perform histogram analysis on the plurality of bSpO2 values ​​by fixing the width of the histogram bins but adapting the center of the bins, to obtain a histogram corresponding to the plurality of bSpO2 values; or perform histogram analysis on the plurality of bSpO2 values ​​by adapting the width of the histogram bins but fixing the center of the bins, to obtain a histogram corresponding to the plurality of bSpO2 values; or perform histogram analysis on the plurality of bSpO2 values ​​by adapting both the width and center of the histogram bins, to obtain a histogram corresponding to the plurality of bSpO2 values.

[0153] In one specific implementation, making the center of the bin in the histogram adaptive includes calculating the center of the bin based on the bSpO2 value within that bin.

[0154] In one specific implementation, the center of the bin of the histogram is the average or median of the bSpO2 values ​​within that bin.

[0155] In one specific implementation, the processor 202 is configured to: acquire red light signals and infrared light signals during the specified time window; perform noise reduction processing on the red light signals and infrared light signals; and use the noise-reduced red light signals and infrared light signals to calculate the SpO2 value associated with each heartbeat to obtain the plurality of bSpO2 values.

[0156] In one specific implementation, the processor 202 is configured to: select the top N warehouses sorted by height from largest to smallest as the target warehouse; or select the top N+1 warehouses sorted by height from largest to smallest as the target warehouse.

[0157] In one specific implementation, the processor 202 is configured to: select the top N+1 warehouses as the target warehouses if the sum of all bSpO2 values ​​in the N+1 warehouse is greater than or equal to the sum of all bSpO2 values ​​in the Nth warehouse; and select the top N warehouses as the target warehouses if the sum of all bSpO2 values ​​in the N+1 warehouse is less than the sum of all bSpO2 values ​​in the Nth warehouse.

[0158] Figure 22 A block diagram of a blood oxygen detection device 300 according to an embodiment of the present disclosure is shown. Figure 22 As shown, the blood oxygen detection device 300 includes: a judgment module 302, used to judge whether the range of multiple pulse oximetry (bSpO2) values ​​measured during a specified time window meets a predetermined condition; and a processing module 304, used to select a range from the multiple bSpO2 values ​​for calculating the SpO2 value according to the judgment result, and use the bSpO2 values ​​within the selected range to perform mathematical statistics calculation to obtain the SpO2 value used as the blood oxygen saturation output value.

[0159] Figure 23 A flowchart illustrating a method 400 for processing blood oxygen signals according to an embodiment of the present disclosure is shown. Figure 23 As shown, the method 400 for processing blood oxygenation signals may include the following steps: In step 402 (judgment step), it is determined whether the signal changes of red light signals and infrared light signals acquired during a specified time window are similar. In step 404 (processing step), based on the judgment result of step 102, it is determined whether motion artifacts exist in the red light signals and the infrared light signals.

[0160] As is well known in the art, pulse oximeters measure arterial oxygen saturation by illuminating a detector on the other side of a tissue bed with red and infrared light of specific wavelengths through the patient's peripheral tissues (such as fingers, earlobes, or toes). SpO2 is calculated by measuring the intensity of the red and infrared light received by the detector at its peak during each cardiac cycle. This peak occurs when blood flow through the tissue bed reaches its maximum.

[0161] Under normal circumstances (e.g., when the SpO2 value changes little), such as Figure 24 and Figure 25 As shown, the signal changes of red light and infrared light are similar. Figure 24 and Figure 25 The corresponding data is clean data, i.e., data without motion artifacts; such as Figure 26 As shown, in the presence of noisy data, the signal changes of red light signals and infrared light signals are not similar. Figure 26 The corresponding data is noisy data; such as Figure 27 As shown, during the periods of 75–90s and 108–119s, the signal changes of the red and infrared signals are similar, and the data during these periods is clean data. During the period of 91–105s, the signal changes of the red and infrared signals are dissimilar, and the data during this period is noise-contaminated. That is, when the SpO2 value changes little, if the signal changes of the red and infrared signals are similar, it is determined that there is no noise; conversely, if the signal changes of the red and infrared signals are dissimilar, it is determined that there is noise. In some cases (e.g., when the SpO2 value changes greatly), if the signal changes of the red and infrared signals are similar, it is determined that there is noise; conversely, if the signal changes of the red and infrared signals are dissimilar, it is determined that there is no noise. The specific reasons will be described later. For example, as... Figure 28 As shown, during the period when the SpO2 value changes from 70% to 100%, the signal changes of the red light signal and the infrared light signal are not similar. The data during this period is clean data. The waveforms of the raw data of the red light signal and the infrared light signal during this period can be found in [reference needed]. Figure 29 .

[0162] In this embodiment, the normalized standard deviation (ampSTD) of the amplitudes between the R and IR peaks within the sliding analysis window can be measured. These ampSTD values ​​are then compared to a pre-set threshold to distinguish between motion artifacts and rapidly changing SpO2 conditions. The normalized standard deviation is calculated according to the following equation:

[0163] ampSTD = STD(pulse amplitude) * 100 / MEAN(pulse amplitude), where STD() is a well-known equation for calculating the standard deviation of a small sample, and MEAN() is an equation for calculating the mean of a sampled dataset.

[0164] The steps performed in this process are as follows: Step 1: Collect all pulse amplitude values ​​measured during the analysis window (e.g., 14 seconds). Step 2: Calculate the standard deviation (STD) of all amplitude values. Step 3: Calculate the average of all amplitude values. Step 4: Calculate the normalized standard deviation (ampSTD) as described above.

[0165] Figure 30The red light waveform received by the photodetector of a pulse oximeter over a 180-second time period was plotted when the R / IR signal was not contaminated by motion artifacts. The red and infrared pulse amplitudes measured during this period are plotted below the real-time waveform.

[0166] Figure 31 The ampSTD of the R and IR values ​​calculated over each analysis period, sliding across a 14-second window, is plotted. Note that in this example, the normalized standard deviation (ampSTD) of the R and IR signals varies in the range of approximately 0–15%.

[0167] Figure 32 The real-time R signal and corresponding R and IR ampSTD calculations were plotted when the R / IR signal was momentarily contaminated by motion artifacts. In this example, the ampSTD value can be seen to vary over a wide range of 0-120%, with high ampSTD values ​​observed during motion artifacts. It was also observed that the momentary increases in ampSTD for the red and infrared signals are somewhat correlated, as they both track each other's rise and fall behavior over time.

[0168] like Figure 28 As shown, compared to the very high R / IR ampSTD values ​​and similar behavior observed during motion artifacts, Figure 28 This demonstrates that, in the absence of motion artifacts, the R / IR ampSTD parameter does not behave similarly during rapid SpO2 changes. Although the red ampSTD rises to approximately 60% of its peak during rapid SpO2 increases, the IR ampSTD value remains low and relatively stable during this period compared to the rise in red ampSTD. This stability of the IR signal is consistent with the stable and unchanging optical path traversing the monitored tissue bed.

[0169] Based on the above examples and other cases collected and analyzed, this disclosure establishes the following general rules for algorithms aimed at separating motion artifacts from clean SpO2 signals:

[0170] 1) Clean data has stable low red and infrared ampSTD values.

[0171] 2) Data contaminated with motion artifacts exhibit high R and IR ampSTD values, which vary considerably, but their increase and decrease behavior is largely due to similar interference along the path of R and IR light through the monitored tissue bed. In other words, the signal changes of R and IR are similar in the presence of motion artifacts.

[0172] 3) Rapid changes in SpO2 may result in a wide ampSTD variation in the R signal (due to rapid changes in HbO2 concentration), but the ampSTD value of the IR signal remains small (due to the stable optical path used to monitor the tissue). These R and IR changes do not track each other in the absence of motion artifacts. That is, in the absence of motion artifacts, the signal changes of R and IR are not similar.

[0173] ampSTD (rrSTD, etc.) breaks through the limitations of histograms. Statistical parameters (ampSTD, rrSTD, etc.) can be extended to areas (calculating waveform area), slopes (waveform slope), etc.

[0174] Therefore, the red light signal in the waveform increases, but the infrared light signal does not increase but tends to stabilize. Figure 29 In this case, the increase in red light signal is due to the patient's physiological signals rather than motion artifacts. Figure 29 As shown, the signal changes of red light and infrared light signals are not similar. Relatively speaking, the red light signal increases and the infrared light signal also increases in the waveform. Figure 32 In this case, the increase in red and infrared light signals is due to motion artifacts rather than the patient's physiological signals. In this situation, such as... Figure 32 As shown, the signal changes of red light signals and infrared light signals are similar. For example, as... Figure 32 As shown, during the specified time window of 40s-52s, both the red light signal and the infrared light signal increased from a first value of less than 40% to a second value of nearly 60%; during the specified time window of 52s-64s, both the red light signal and the infrared light signal decreased from a third value of nearly 60% to a fourth value of less than 40%.

[0175] Based on this, this disclosure proposes to determine whether the signal changes of red light signals and infrared light signals are similar, and to determine whether motion artifacts exist in red light signals and infrared light signals based on the determination results of whether the signal changes of red light signals and infrared light signals are similar. This can distinguish the signal components generated by physiological and motion artifacts, thereby improving the detection accuracy of SpO2 values.

[0176] In one possible implementation, it can be determined whether the increase in the red light signal and the increase in the infrared light signal acquired during a specified time window are both greater than a threshold. If both increases are greater than the threshold, the signal changes of the red light signal and the infrared light signal acquired during the specified time window are considered similar; otherwise, they are considered dissimilar. In another possible implementation, it can be determined whether the rate of change of the red light signal and the rate of change of the infrared light signal acquired during the specified time window are consistent. If their rates of change are consistent, the signal changes of the red light signal and the infrared light signal acquired during the specified time window are considered similar; otherwise, they are considered dissimilar. Of course, any other suitable method can be used to determine whether the signal changes of the red light signal and the infrared light signal are similar; this disclosure does not impose specific limitations on the method of determination.

[0177] Therefore, compared to the existing technology where doctors determine the presence of motion artifacts in red and infrared signals based on the displayed SpO2 value, this embodiment determines the presence of motion artifacts in red and infrared signals based on the similarity of the signal changes of the two signals collected during a specified time window. Thus, this embodiment uses a different approach than the existing technology to obtain intermediate processing results for the presence of motion artifacts, thereby enabling a more accurate distinction between physiological and motion artifact-generated signal components.

[0178] In one specific implementation of step 404, if it is determined that the signal changes of the red light signal and the infrared light signal are similar, it is determined that there is motion artifact in the red light signal and the infrared light signal; if it is determined that the signal changes of the red light signal and the infrared light signal are dissimilar, it is determined that there is no motion artifact in the red light signal and the infrared light signal.

[0179] In one specific implementation of step 404, in response to determining that motion artifacts exist in the red light signal and the infrared light signal, the acquired red light signal and infrared light signal are removed, the red light signal and infrared light signal are reacquired during the next specified time window, and the judgment step 402 and the processing step 404 are executed for the reacquired red light signal and infrared light signal.

[0180] Therefore, the use of red light and infrared light signals, which contain motion artifacts, to calculate the bSpO2 value can be avoided, thereby improving the detection accuracy of SpO2 value.

[0181] In one specific implementation of step 404, in response to determining that motion artifacts exist in the red light signal and the infrared light signal, prompting processing is performed to prompt the subject (e.g., the person measuring SpO2) to "stop moving the patient".

[0182] Therefore, by stopping patient movement, motion artifacts will be reduced in the re-acquisition of red and infrared light signals, thereby improving the time required to obtain more accurate SpO2 values.

[0183] Figure 33 A block diagram of a blood oxygen detection device 500 according to an embodiment of the present disclosure is shown. Figure 33 As shown, the blood oxygen detection device 500 includes a processor 502 and a memory 504 for storing processor-executable instructions. The processor 502 is configured to determine whether the signal changes of red light signals and infrared light signals acquired during a specified time window are similar, and based on the result of the determination, to determine whether motion artifacts exist in the red light signals and the infrared light signals.

[0184] In one specific implementation, the processor 202 is configured to: determine that motion artifacts exist in the red light signal and the infrared light signal if the signal changes of the red light signal and the infrared light signal are similar; and determine that motion artifacts do not exist in the red light signal and the infrared light signal if the signal changes of the red light signal and the infrared light signal are dissimilar.

[0185] In one specific implementation, the processor 202 is configured to: in response to determining that motion artifacts exist in the red light signal and the infrared light signal, remove the acquired red light signal and infrared light signal, reacquire the red light signal and infrared light signal during the next specified time window, and determine whether the signal changes of the reacquired red light signal and infrared light signal are similar, and based on the result of the determination, re-determine whether motion artifacts exist in the red light signal and the infrared light signal.

[0186] In one specific implementation, the processor 202 is configured to: in response to determining that motion artifacts exist in the red light signal and the infrared light signal, perform prompting processing to prompt the person measuring SpO2 to "stop moving the patient".

[0187] Figure 34 A block diagram of a blood oxygen detection device 600 according to an embodiment of the present disclosure is shown. Figure 34 As shown, the blood oxygen detection device 600 includes: a judgment module 610, used to judge whether the signal changes of the red light signal and the infrared light signal collected during a specified time window are similar; and a processing module 630, used to determine whether there are motion artifacts in the red light signal and the infrared light signal based on the judgment result.

[0188] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0189] Figure 35 This is a block diagram illustrating an electronic device 700 according to an exemplary embodiment. The electronic device 700 is one specific implementation of blood oxygen detection devices 200 and 500. For example, device 700 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc. In one possible implementation, blood oxygen detection devices 200 and 500 are devices capable of detecting electrocardiogram (ECG) signals. In one possible implementation, the blood oxygen detection devices 200 and 500 may include a defibrillator and / or a monitor.

[0190] Reference Figure 35 The device 700 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0191] Processing component 802 typically controls the overall operation of device 700, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0192] Memory 804 is configured to store various types of data to support the operation of device 700. Examples of this data include instructions for any application or method operating on device 700, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0193] Power supply component 806 provides power to various components of device 700. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 700.

[0194] Multimedia component 808 includes a screen that provides an output interface between the device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 700 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0195] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 700 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0196] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0197] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 700. For example, sensor assembly 814 may detect the on / off state of device 700, the relative positioning of components such as the display and keypad of device 700, changes in the position of device 700 or a component of device 700, the presence or absence of user contact with device 700, the orientation or acceleration / deceleration of device 700, and temperature changes of device 700. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0198] Communication component 816 is configured to facilitate wired or wireless communication between device 700 and other devices. Device 700 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0199] In an exemplary embodiment, device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0200] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory including computer program instructions that can be executed by a processor to perform the above-described method for processing electrocardiogram signals.

[0201] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0202] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0203] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0204] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0205] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0206] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0207] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0208] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0209] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for processing pulse oximetry signals, characterized in that, include: The judgment step is used to determine whether the range of multiple pulse oximetry (bSpO2) values ​​measured during a specified time window meets the predetermined conditions. as well as The processing step is used to select a range for calculating the SpO2 value from the plurality of bSpO2 values ​​based on the result of the judgment, and to perform mathematical statistics calculations using the bSpO2 values ​​within the selected range to obtain the SpO2 value used as the blood oxygen saturation output value.

2. The method according to claim 1, characterized in that, The processing steps include: If the predetermined conditions are met, a probability analysis is performed on the plurality of bSpO2 values. Based on the result of the probability analysis, a selected range is chosen from the plurality of bSpO2 values, and the bSpO2 values ​​within the selected range are used to perform mathematical statistics calculations to obtain the SpO2 value.

3. The method according to claim 2, characterized in that, If the predetermined conditions are met, a probability analysis is performed on the plurality of bSpO2 values ​​to obtain the probability of occurrence of each bSpO2 value among the plurality of bSpO2 values. The selected range is then selected from the plurality of bSpO2 values, wherein the probability of occurrence of each bSpO2 value within the selected range is greater than a probability threshold.

4. The method according to claim 1, characterized in that, The processing steps include: If the predetermined conditions are met, a counting analysis is performed on the plurality of bSpO2 values. Based on the results of the counting analysis, a selected range is chosen from the plurality of bSpO2 values, and the bSpO2 values ​​within the selected range are used to perform mathematical statistics calculations to obtain the SpO2 value.

5. The method according to claim 4, characterized in that, If the predetermined conditions are met, a counting analysis is performed on the plurality of bSpO2 values ​​to obtain the count value of each bSpO2 value among the plurality of bSpO2 values. A selected range is then selected from the plurality of bSpO2 values, wherein the count value of each bSpO2 value within the selected range is greater than a counting threshold.

6. The method according to claim 1, characterized in that, The processing steps include: If the predetermined conditions are met, histogram analysis is performed on the plurality of bSpO2 values. Based on the results of the histogram analysis, a selected range is chosen from the plurality of bSpO2 values, and mathematical statistics are calculated using the bSpO2 values ​​within the selected range to obtain the SpO2 value.

7. The method according to claim 6, characterized in that, If the predetermined conditions are met, histogram analysis is performed on the plurality of bSpO2 values ​​to obtain the histogram corresponding to the plurality of bSpO2 values. A target warehouse is selected from the warehouses in the histogram, wherein the bSpO2 values ​​in the target warehouse are bSpO2 values ​​within the selected range.

8. The method according to claim 1, characterized in that, The determination step includes: determining whether all bSpO2 values ​​among the plurality of bSpO2 values ​​are within the range of values, wherein the predetermined condition includes that all bSpO2 values ​​among the plurality of bSpO2 values ​​are within the range of values.

9. The method according to claim 8, characterized in that, The value range includes at least one of the following: the difference between the maximum and minimum values ​​among the plurality of bSpO2 values ​​is within ±2%, the difference is within ±3%, the difference is within ±4%, the difference is within ±5%, and the difference is within ±6%.

10. The method according to claim 8, characterized in that, If all bSpO2 values ​​are within the specified range, it is determined that the predetermined condition is met, and a first number of warehouses with heights greater than a height threshold are selected from the warehouses in the histogram as the target warehouses.

11. The method according to claim 8, characterized in that, If not all bSpO2 values ​​are within the specified range, but all bSpO2 values ​​are greater than or equal to a first threshold, it is determined that the predetermined condition is met, and a first number of warehouses with heights greater than a height threshold are selected from the warehouses in the histogram as the target warehouses.

12. The method according to claim 8, characterized in that, If not all bSpO2 values ​​are within the specified range, and not all bSpO2 values ​​are greater than or equal to the first threshold, but the red light signal and infrared light signal collected during the specified time window are free of motion artifacts, then the predetermined condition is met, and a first number of bins with heights greater than the height threshold are selected from the bins of the histogram as the target bins.

13. The method according to claim 11 or 12, characterized in that, The first threshold includes 92%, 94%, or 96%.

14. The method according to any one of claims 10-12, characterized in that, If the predetermined conditions are not met, the warehouse with the highest height in the histogram is selected as the target warehouse; or If the predetermined conditions are not met, when there are multiple adjacent warehouses, the sum of the values ​​of the multiple adjacent warehouses is compared with the value of the warehouse with the largest height. If the sum of the values ​​of the multiple adjacent warehouses is greater than the value of the warehouse with the largest height, then the multiple adjacent warehouses are selected as the target warehouse. If the sum of the values ​​of the multiple adjacent warehouses is equal to or less than the value of the warehouse with the largest height, then the warehouse with the largest height is selected as the target warehouse.

15. The method according to any one of claims 1-12, characterized in that, The SpO2 value is obtained by performing mathematical statistics calculations using bSpO2 values ​​within the selected range, including: The average of all bSpO2 values ​​within the selected range is calculated as the SpO2 value.

16. The method according to claim 12, characterized in that, Determining whether the red light signal and infrared light signal acquired during the specified time window are motion-artifact-free signals includes at least one of the following: Determine whether the peak amplitudes of the red light signal and the infrared light signal collected during the specified time window are consistent; Determine whether the beat cycle, rate, first-order difference, reciprocal first-order difference, or second-order difference of the red light signal and infrared light signal collected during the specified time window are consistent; Determine whether the pulse durations of the red light signal and the infrared light signal collected during the specified time window are consistent; Determine whether the pulse areas of the red light signal and the infrared light signal collected during the specified time window are consistent; Determine whether the temporal correlation between the peaks and valleys of the red light signal and the infrared light signal collected during the specified time window is consistent; Determine whether the red light signal collected during the specified time window is consistent with the historical red light signal; Determine whether the infrared light signal collected during the specified time window is consistent with the historical infrared light signal; Determine whether the red light signal collected during the specified time window is consistent with the red light signal without noise and motion artifacts; Determine whether the infrared light signal collected during the specified time window is consistent with the infrared light signal without noise and motion artifacts.

17. The method according to claim 7, characterized in that, Histogram analysis was performed on the plurality of bSpO2 values ​​to obtain the histograms corresponding to the plurality of bSpO2 values, including: Histogram analysis is performed on the multiple bSpO2 values ​​by fixing both the width and center of the histogram bins; or... Histogram analysis is performed on the multiple bSpO2 values ​​by fixing the width of the bins in the histogram but adapting the center of the bins; or Histogram analysis is performed on the multiple bSpO2 values ​​by making the width of the bins in the histogram adaptive while keeping the center of the bins fixed, to obtain the histograms corresponding to the multiple bSpO2 values; or Histogram analysis is performed on the multiple bSpO2 values ​​by adapting both the width and center of the histogram bins. This yields the histograms corresponding to the multiple bSpO2 values.

18. The method according to claim 17, characterized in that, To make the center of the bin in the histogram adaptive, the center of the bin is calculated based on the bSpO2 value within that bin.

19. The method according to claim 18, characterized in that, The center of the bin in the histogram is the average or median of the bSpO2 values ​​within that bin.

20. The method according to any one of claims 1-12, 16-19, characterized in that, Also includes: Red light and infrared light signals are acquired during the specified time window; The red light signal and the infrared light signal are subjected to noise reduction processing; Using the noise-reduced red light signal and infrared signal, the SpO2 value is calculated to obtain the multiple bSpO2 values.

21. The method according to any one of claims 10-12, characterized in that, Selecting a first number of warehouses with a height greater than a height threshold as the target warehouses includes: Select the top N warehouses sorted by height from largest to smallest as the target warehouse; or Select the warehouses that are ranked in the top N+1 according to their height from largest to smallest as the target warehouses.

22. The method according to claim 21, characterized in that, If the sum of all bSpO2 values ​​in the N+1th warehouse is greater than or equal to the sum of all bSpO2 values ​​in the Nth warehouse, the warehouse ranked in the top N+1 is selected as the target warehouse. If the sum of all bSpO2 values ​​in the warehouse ranked N+1 is less than the sum of all bSpO2 values ​​in the warehouse ranked N, then the warehouse ranked in the top N is selected as the target warehouse.

23. A pulse oximetry device, comprising: The judgment module is used to determine whether the range of multiple per-wave oxygen saturation (bSpO2) values ​​measured during a specified time window meets the predetermined conditions. as well as The processing module is configured to select a range for calculating the SpO2 value from the plurality of bSpO2 values ​​based on the result of the judgment, and to perform mathematical statistics calculations using the bSpO2 values ​​within the selected range to obtain the SpO2 value used as the blood oxygen saturation output value.

24. A method for processing blood oxygenation signals, characterized in that, include: The judgment step is used to determine whether the signal changes of the red light signal and the infrared light signal collected during a specified time window are similar; as well as The processing step is used to determine, based on the result of the judgment, whether motion artifacts exist in the red light signal and the infrared light signal.

25. The method according to claim 24, characterized in that, The processing steps include: If the signal changes of the red light signal and the infrared light signal are determined to be similar, it is determined that there is motion artifact in the red light signal and the infrared light signal. If it is determined that the signal changes of the red light signal and the infrared light signal are not similar, it is determined that there are no motion artifacts in the red light signal and the infrared light signal.

26. The method according to claim 25, characterized in that, The processing steps also include: In response to the determination that motion artifacts exist in the red light signal and the infrared light signal, the acquired red light signal and infrared light signal are removed, and the red light signal and infrared light signal are reacquired during the next specified time window. The judgment step and the processing step are then performed on the reacquired red light signal and infrared light signal.

27. The method according to claim 25 or 26, characterized in that, The processing steps also include: In response to the determination that motion artifacts exist in the red light signal and the infrared light signal, a prompting process is performed to remind the subject not to move.

28. A pulse oximetry device, comprising: The judgment module is used to determine whether the signal changes of the red light signal and the infrared light signal collected during a specified time window are similar; as well as The processing module is used to determine whether motion artifacts exist in the red light signal and the infrared light signal based on the result of the judgment.

29. An electronic device comprising: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1-22 and 24-27 when executing processor-executable instructions.

30. The electronic device according to claim 29, characterized in that, The electronic device is capable of detecting blood oxygen signals.

31. The electronic device according to claim 30, characterized in that, The electronic device includes at least one of a defibrillator, a monitor, and a pulse oximeter.

32. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method of any one of claims 1-22 and 24-27.

33. A computer program product storing instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1-22, 24-27.

Images