Physiological parameter measurement method and electronic device

Abstract

The present application proposes a physiological parameter measurement method and an electronic device, the physiological parameter measurement method comprising: obtaining pulse wave data to be processed within a current first time window; determining the length of a second time window according to the pulse wave data; sliding the pulse wave data to be processed within the current first time window using the second time window, and determining all peak positions within the first time window; and determining the pulse rate according to the difference in the peak positions and the sampling frequency of the pulse wave data. The present application sets a second time window with an adjustable length on the pulse wave data, uses the second time window to find the peak position of the pulse wave, and then determines the pulse rate from the interval of the peak positions, thereby achieving fast and accurate measurement of the pulse rate.

Description

Technical Field

[0001] The present application relates to the field of physiological parameter detection, and in particular to a physiological parameter measurement method. Background Art

[0002] There are two general methods for calculating pulse rate. The first is the frequency domain method, which uses Fourier transform to calculate the maximum frequency point. This method requires a long period of pulse wave data to accurately obtain the pulse rate value, which results in a long measurement time. The second is the time domain method, which searches for the pulse wave peak. This method is prone to interference, especially when the pulse wave signal quality is poor, and is prone to missed detection and false detection, resulting in low accuracy of pulse rate measurement. Summary of the invention

[0003] In view of this, it is necessary to provide a physiological parameter measurement method, which can reduce the measurement time and improve the accuracy of physiological parameter measurement.

[0004] A first aspect of the present application provides a physiological parameter measurement method for measuring pulse rate, the physiological parameter measurement method comprising: acquiring pulse wave data to be processed within a current first time window; determining the length of a second time window based on the pulse wave data; using the second time window to slide on the pulse wave data to be processed within the current first time window, and determining all peak positions within the first time window; and determining the pulse rate based on the difference in the peak positions and the sampling frequency of the pulse wave data.

[0005] Optionally, before the step of obtaining the pulse wave data to be processed within the current first time window, the method also includes: obtaining a first initial signal, and determining a first AC signal based on the first initial signal; using the first time window to slide on the first AC signal, and determining that the first AC signal located within the first time window is the pulse wave data to be processed within the current first time window.

[0006] Optionally, the use of the second time window to slide on the pulse wave data to be processed in the current first time window and determine all peak positions in the first time window includes: using the second time window to slide on the pulse wave data to be processed in the current first time window, and determining whether the value of the pulse wave data located at the center position of the second time window is the maximum value in the second time window and is greater than a threshold value; if so, the position is a peak position; if not, continue to slide the second time window.

[0007] Optionally, determining the length of the second time window based on the pulse wave data includes: converting the pulse wave data from a time domain signal to a frequency domain signal, and determining the maximum peak position of the pulse wave data in the frequency domain; and determining the length of the second window based on the length of the first time window, the sampling frequency of the pulse wave data and the maximum peak position.

[0008] Optionally, the physiological parameter measurement method also includes, after the step of obtaining the pulse wave data to be processed in the current first time window, obtaining the pulse wave data to be processed in the current third time window, and determining the maximum peak value of the peak position, and determining the threshold value according to the maximum peak value, wherein the third time window is located in the first time window and each of the first time windows corresponds to a unique third time window.

[0009] Optionally, after the step of determining the pulse rate based on the difference in the peak positions and the sampling frequency of the pulse wave data, the physiological parameter measurement method further includes: acquiring a second initial signal and a third initial signal, and determining the blood oxygen saturation based on the second initial signal, the third initial signal and the first AC signal.

[0010] Optionally, acquiring the second initial signal and the third initial signal, and determining the blood oxygen saturation according to the second initial signal, the third initial signal and the first AC signal includes:

[0011] A signal acquisition step, acquiring a red light initial signal and an infrared initial signal;

[0012] an AC / DC signal extraction step, determining red light DC data and a red light AC signal according to the red light initial signal, and determining infrared DC data and an infrared AC signal according to the infrared initial signal;

[0013] A preprocessing step, determining a red light signal according to the red light DC data and the red light AC signal; determining an infrared signal according to the infrared DC data and the infrared AC signal;

[0014] an adaptive filtering step, taking the first AC signal as a reference signal, and performing adaptive filtering processing on the red light signal and the infrared light signal to obtain red light data and infrared data;

[0015] The blood oxygen calculation step determines the blood oxygen saturation according to the red light data and the infrared data.

[0016] Optionally, the adaptive filtering step includes: comparing the red light signal with the first AC signal, and determining that data corresponding to at least one component signal in the red light signal that is closest to the first AC signal is the red light data; comparing the infrared signal with the first AC signal, and determining that data corresponding to at least one component signal in the infrared signal that is closest to the first AC signal is the infrared data.

[0017] Optionally, the blood oxygen calculation step includes: determining pulse blood oxygen according to the red light data and the infrared data, and determining the blood oxygen saturation by querying a preconfigured comparison table according to the pulse blood oxygen.

[0018] A second aspect of the present application provides an electronic device for measuring physiological parameters, the electronic device for measuring physiological parameters comprising:

[0019] Processor; and

[0020] A memory, wherein a plurality of program modules are stored in the memory, and the plurality of program modules are loaded by the processor and execute the physiological parameter measurement method as described above.

[0021] Compared with the prior art, the present application has at least the following beneficial effects: by setting a second time window with adjustable length on the pulse wave data, using the second time window to find the peak position of the pulse wave, and then determining the pulse rate from the interval of the peak position, fast and accurate measurement of the pulse rate can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flow chart of a physiological parameter measurement method in one embodiment of the present application.

[0023] Figure 2 Schematic diagram of the relationship between the first time window, the second time window and the third time window in one embodiment of the present application.

[0024] Figure 3 for Figure 1 Schematic diagram of the sub-process of step S14.

[0025] Figure 4 Schematic diagram of pulse wave data in the frequency domain in one embodiment of the present application.

[0026] Figure 5 for Figure 1 Schematic diagram of the sub-process of step S17.

[0027] Figure 6 Schematic diagram of the effect of adaptive filtering of a red light signal in one embodiment of the present application.

[0028] Figure 7It is a schematic diagram of the effect of adaptive filtering of infrared signals in one embodiment of the present application.

[0029] Figure 8 Schematic diagram of a physiological parameter measuring device in one embodiment of the present application.

[0030] Main component symbols

[0031] Physiological parameter measurement electronic device 1

[0032] Processor 11

[0033] Memory 12

[0034] Program Module 121

[0035] The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0037] See also Figure 1 The embodiment disclosed in the present application provides a method for measuring physiological parameters. The physiological parameter is, for example, pulse rate. The method for measuring physiological parameters comprises the following steps:

[0038] Step S11, acquiring a first initial signal, and determining a first AC signal according to the first initial signal.

[0039] In this embodiment, since green light as the measurement light source obtains a better signal and a better signal-to-noise ratio than other light sources, this application uses green light (usually with a wavelength of 520nm) as the first initial signal for measurement, which is used as reference data for the pulse wave signal.

[0040] In this embodiment, determining the first AC signal according to the first initial signal includes passing the first initial signal through a high-pass filter and a low-pass filter in sequence to eliminate high-frequency components (such as power supply) and low-frequency components (such as changes in capillary density and venous blood volume, temperature changes, etc.) in the pulse wave signal (i.e., the first initial signal). The cutoff frequency of the high-pass filter is, for example, 0.5 Hz, and the cutoff frequency of the low-pass filter is, for example, 5 Hz.

[0041] It can be understood that the acquisition frequency of the first initial signal can be adjusted. In this embodiment, the acquisition frequency of the first initial signal is Fs=50 Hz.

[0042] Step S12: Slide on the first AC signal using a first time window of a predetermined duration.

[0043] See also Figure 2 , Figure 2 The horizontal axis is time, and the vertical axis is the first AC signal. It can be understood that the predetermined duration T of the first time window is w It can be adjusted, and in this embodiment, it is 5 seconds, for example.

[0044] Step S13, obtaining the pulse wave data to be processed in the current first time window, wherein the pulse wave data is a part of the first AC signal.

[0045] It is understandable that when the first time window slides to a new position each time, only the first AC signal in the current first time window is processed. The interval of each sliding of the first time window can be set, for example, the interval is set to the time interval between two samplings.

[0046] Step S14, determining the length of the second time window according to the pulse wave data.

[0047] Please refer to Figure 3 It can be understood that step S14 includes:

[0048] Step S141, converting the pulse wave data from a time domain signal into a frequency domain signal, and determining the maximum peak position of the pulse wave data in the frequency domain.

[0049] In this embodiment, converting the pulse wave data from a time domain signal to a frequency domain signal may be, for example, performing Fourier transform on the pulse wave data to determine the maximum peak position (Hamp) of the pulse wave data in the frequency domain.

[0050] See also Figure 4 , Figure 4 FIG. 5 is a schematic diagram showing how to convert 5 seconds of pulse wave data from a time domain signal to a frequency domain signal. In this embodiment, the horizontal axis represents the sampling point, and the vertical axis represents the amplitude of the pulse wave data. Figure 4 As shown, the maximum peak position Hamp=6.

[0051] Step S142, determining the length of the second window according to the length of the first time window, the sampling frequency of the pulse wave data and the maximum peak position.

[0052] Please continue reading Figure 2In this embodiment, the length of the second time window wind is based on the length of the first time window T w , the sampling frequency of the pulse wave data and the maximum peak position. Specifically, the length wind of the second time window can be obtained by the following formula (1).

[0053]

[0054] Where wind is the length of the second time window, α is the empirical coefficient, T w is the length of the first time window, the F S is the sampling frequency of the pulse wave data, and Hamp is the maximum peak position.

[0055] By setting the length wind of the second time window to be dynamic, adaptive changes can be made according to the first time window and the real-time pulse wave data in the first time window. For example, when Hamp is too large, wind is adjusted to be small, and when Hamp is too small, wind is adjusted to be large. In this way, the length wind of the second time window is suitable for different pulse rate ranges, thereby improving the accuracy of pulse rate measurement.

[0056] Step S15, using the second time window to slide on the pulse wave data to be processed in the current first time window, and determining all peak positions in the first time window.

[0057] In this embodiment, the determining of all peak positions in the first time window includes determining whether the pulse wave data value located at the center of the second time window is the maximum value in the second time window and is greater than a threshold value. If so, the position belongs to the peak position, and if not, the second time window continues to slide, for example, sliding the second time window to the right.

[0058] It can be understood that the interval of each sliding of the second time window can be set, for example, one sampling point is set as the interval.

[0059] It can be understood that the threshold is determined according to the maximum amplitude of the pulse wave data (for example, the pulse wave data in the first time window). The threshold σ can be obtained according to the following formula (2).

[0060] σ=β×AmpMax (2)

[0061] Wherein, is the threshold, β is an empirical coefficient, for example, it can be 0.6, and AmpMax is the maximum peak value of the pulse wave data in the first time window.

[0062] It is understood that in some other embodiments, the maximum amplitude of the pulse wave data can also be determined based on the maximum amplitude of the pulse wave data in the third time window. Each of the third time windows is located in the first time window and each of the first time windows corresponds to a unique third time window, so that the judgment criteria for the pulse wave peak value in each of the first time windows are consistent. The position of the third time window in the first time window can be adjusted arbitrarily, for example, it can be located at the leftmost, rightmost, or in the middle of the first time window (such as Figure 2 The length of the third time window is T a For example, it can be 2.5 seconds. According to the monitoring range of medical-grade pulse rate, that is, 25dpm-250dpm, it can be known that the peak value of a pulse wave can be collected in 0.24 seconds-2.4 seconds.

[0063] Step S16, determining the pulse rate according to the difference in the peak positions and the sampling frequency of the pulse wave data.

[0064] In this embodiment, the pulse rate P can be obtained by the following formula (3).

[0065]

[0066] Among them, F S is the sampling frequency of the pulse wave data, mRR is the average value of the peak position difference, and the average peak position difference mRR is generally the average value of 5 to 8 peak position differences.

[0067] By setting a second time window with adjustable length at the maximum peak position of the pulse wave data in the frequency domain, the second time window is used to find the peak position of the first AC signal in the time domain, and finally the pulse rate is determined by the interval of the peak position, fast and accurate measurement of the pulse rate can be achieved.

[0068] Please continue reading Figure 1 In one embodiment of the present application, the physiological parameter measurement method further includes:

[0069] Step 17, obtaining a second initial signal and a third initial signal, and determining the blood oxygen saturation according to the second initial signal, the third initial signal and the first AC signal.

[0070] Please also read Figure 5 ,for Figure 1 Schematic diagram of the sub-process of step 17 in FIG.

[0071] S171, signal acquisition step, acquiring a red light initial signal and an infrared initial signal.

[0072] It can be understood that in addition to using the green light to irradiate the skin, the present application also uses red light (usually with a wavelength of 660nm) and infrared light (usually with a wavelength of 904nm) to irradiate the skin for the collection of the red light initial signal and the infrared initial signal.

[0073] Step S172, an AC / DC signal extraction step, determining red light DC data and a red light AC signal according to the red light initial signal, and determining infrared DC data and an infrared AC signal according to the infrared initial signal.

[0074] It can be understood that, similar to the extraction of the green light AC signal, the AC / DC signal extraction step includes passing the red light initial signal and the infrared initial signal through a high-pass filter and a low-pass filter in sequence to eliminate high-frequency components (such as power supply) and low-frequency components (such as changes in capillary density and venous blood volume, temperature changes, etc.) in the pulse wave signal (i.e., the red light initial signal and the infrared initial signal). The cutoff frequency of the high-pass filter is, for example, 0.5 Hz, and the cutoff frequency of the low-pass filter is, for example, 5 Hz.

[0075] Step S173, a preprocessing step, determines a red light signal according to the red light DC data and the red light AC signal, and determines an infrared signal according to the infrared DC data and the infrared AC signal.

[0076] In this embodiment, determining the red light signal according to the red light DC data and the red light AC signal, and determining the infrared signal according to the infrared DC data and the infrared AC signal includes dividing the red light AC data by the red light DC signal, and dividing the infrared AC data by the infrared DC signal.

[0077] Specifically, the red light signal N Rd And the infrared signal N Ir It can be obtained by the following formula (4) and formula (5).

[0078] N Rd =Rd AC / Rd DC (4)

[0079] N Ir =Ir AC / Ir DC (5)

[0080] Among them, Rd AC is the red light AC signal, Rd DC is the red light DC data, Ir AC is an infrared AC signal, Ir DC It is an infrared DC signal.

[0081] It can be understood that by pre-dividing the red light AC signal by the red light DC data and the infrared AC signal by the infrared DC data, subsequent calculations can be simplified and the efficiency of measuring blood oxygen saturation can be improved.

[0082] Step S174, an adaptive filtering step, uses the first AC signal as a reference signal, and performs adaptive filtering processing on the red light signal and the infrared signal to obtain red light data and infrared data.

[0083] In this embodiment, the adaptive filtering step includes: comparing the red light signal with the first AC signal, and determining that data corresponding to at least one component signal in the red light signal closest to the first signal is the red light data. Comparing the infrared signal with the first signal, and determining that data corresponding to at least one component signal in the infrared signal closest to the first signal is the infrared data.

[0084] It can be understood that the red light AC signal includes multiple component signals, at least one of which is an arterial signal, and other component signals may contain noise signals reflected by venous blood flow or capillaries, and such noise signals are less similar to the first AC signal (green light AC signal). On the contrary, since green light can be better absorbed by arterial blood, the signal collected after reflection (i.e., the first AC signal) is more similar to the component containing the arterial signal in the red light AC signal (i.e., infrared data). Similarly, the infrared AC signal is also the same, and will not be described in detail here.

[0085] See also Figure 6 and Figure 7 , Figure 6 The red light signal N Rd And after adaptive filtering, the red light data L is obtained Rd , Figure 7 The infrared signal N is shown Ir And after adaptive filtering, the infrared data L is obtained Ir .

[0086] Step S175, a blood oxygen calculation step, determines the blood oxygen saturation based on the red light data and the infrared data.

[0087] In this embodiment, the blood oxygen calculation step includes determining pulse blood oxygen according to the red light data and the infrared data, and determining the blood oxygen saturation by querying a pre-configured comparison table according to the pulse blood oxygen.

[0088] Specifically, the pulse oximetry can be obtained by the following formula (6):

[0089]

[0090] Among them, the L Rd For red light data, the L IR Infrared data.

[0091] It can be understood that in other embodiments, the blood oxygen saturation Sp determined according to the pulse blood oxygen measurement may be obtained by the following formula (7) instead of by looking up a table: 2 :

[0092] SpO 2 =A+B×R (7)

[0093] Wherein, A and B are coefficients of blood oxygen saturation.

[0094] By using the green light AC signal to adaptively filter the red light signal and the infrared signal, the noise signal in the red light signal and the infrared signal can be effectively removed, thereby improving the measurement accuracy of blood oxygen saturation.

[0095] See also Figure 7 The embodiment of the present application further provides a physiological parameter measurement electronic device 1, which includes: a processor 11 and a memory 12. The memory 12 stores a plurality of program modules 121, which are loaded by the processor 11 and execute the physiological parameter measurement method as described above.

[0096] It can be understood that the physiological parameter measurement electronic device 1 can be, for example, a smart watch / bracelet with the function of measuring pulse rate and blood oxygen saturation.

[0097] Those skilled in the art should recognize that the above implementation modes are only used to illustrate the present application and are not used as limitations on the present application. As long as they are within the essential spirit of the present application, appropriate changes and modifications made to the above embodiments should fall within the scope of protection claimed in the present application.

Claims

1. A physiological parameter measurement method for measuring pulse rate, characterized in that: The physiological parameter measurement method comprises: Acquire a first initial signal, and determine a first AC signal according to the first initial signal; Slide the first AC signal using the first time window to determine that the first AC signal within the first time window is the pulse wave data to be processed within the current first time window; Converting the pulse wave data from a time domain signal to a frequency domain signal, and determining the maximum peak position of the pulse wave data in the frequency domain; Determine the length of the second time window according to the length of the first time window, the sampling frequency of the pulse wave data and the maximum peak position; Slide the second time window on the pulse wave data to be processed in the current first time window, and determine whether the value of the pulse wave data located at the center of the second time window is the maximum value in the second time window and is greater than a threshold value. If so, the position is a peak position; if not, continue to slide the second time window; and determining the pulse rate according to the difference in the peak positions and the sampling frequency of the pulse wave data; The second time window is obtained according to the following formula: wind is the length of the second time window, α is the empirical coefficient, T w is the length of the first time window, F S is the sampling frequency of the pulse wave data, and Hamp is the maximum peak position.

2. The physiological parameter measurement method according to claim 1, characterized in that: The physiological parameter measurement method also includes, after the step of obtaining the pulse wave data to be processed in the current first time window, obtaining the pulse wave data to be processed in the current third time window, and determining the maximum peak value of the peak position, and determining the threshold value according to the maximum peak value, wherein the third time window is located in the first time window and each of the first time windows corresponds to a unique third time window.

3. The physiological parameter measurement method according to claim 1, characterized in that: After the step of determining the pulse rate according to the difference in the peak positions and the sampling frequency of the pulse wave data, the physiological parameter measurement method further comprises: A second initial signal and a third initial signal are acquired, and blood oxygen saturation is determined according to the second initial signal, the third initial signal and the first AC signal.

4. The physiological parameter measurement method according to claim 3, characterized in that: The acquiring the second initial signal and the third initial signal, and determining the blood oxygen saturation according to the second initial signal, the third initial signal and the first AC signal comprises: A signal acquisition step, acquiring a red light initial signal and an infrared initial signal; an AC / DC signal extraction step, determining red light DC data and a red light AC signal according to the red light initial signal, and determining infrared DC data and an infrared AC signal according to the infrared initial signal; A preprocessing step, determining a red light signal according to the red light DC data and the red light AC signal; determining an infrared signal according to the infrared DC data and the infrared AC signal; an adaptive filtering step, taking the first AC signal as a reference signal, and performing adaptive filtering processing on the red light signal and the infrared light signal to obtain red light data and infrared data; The blood oxygen calculation step determines the blood oxygen saturation according to the red light data and the infrared data.

5. The physiological parameter measurement method according to claim 4, characterized in that: The adaptive filtering step comprises: Compare the red light signal with the first AC signal, and determine that data corresponding to at least one component signal in the red light signal that is closest to the first AC signal is the red light data; The infrared signal is compared with the first AC signal, and data corresponding to at least one component signal in the infrared signal that is closest to the first AC signal is determined as the infrared data.

6. The physiological parameter measurement method according to claim 4, characterized in that: The blood oxygen calculation step comprises: The pulse blood oximetry is determined according to the red light data and the infrared data, and the blood oxygen saturation is determined by querying a pre-configured comparison table according to the pulse blood oximetry.

7. An electronic device for measuring physiological parameters, characterized in that: The physiological parameter measurement electronic device comprises: Processor; and A memory, wherein a plurality of program modules are stored in the memory, and the plurality of program modules are loaded by the processor to execute the physiological parameter measurement method according to any one of claims 1 to 6.

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