Sleep diagnosis method based on blood volume pulse waves

Abstract

The invention relates to the technical field of sleep monitoring, and discloses a blood volume pulse wave-based sleep diagnosis method, which comprises the following steps of: 1, measuring PPG waves of a patient through an optical sensor; step 2, wirelessly transmitting the PPG wave signal obtained by measurement to an electronic terminal; step 3, applying a preset signal processing algorithm on the electronic terminal to process the PPG wave signal, wherein the signal processing algorithm comprises the following steps: eliminating 50 / 60Hz power frequency interference by using an LMS filter; baseline drift correction is carried out through morphological filtering to remove respiratory rhythm interference, and the length of a structural element is larger than or equal to 2 seconds; the motion artifact elimination technology is adopted, independent components of PPG wave signals are separated, and components with the acceleration correlation larger than 0.8 are removed. According to the invention, the signal-to-noise ratio is improved by 15dB based on multi-stage anti-interference processing of PPG signals, and the data reliability is guaranteed. High-precision event identification is realized by innovatively fusing multiple parameters; the autonomic nerve wakefulness is identified by utilizing the PPG pulse interval, and electroencephalogram dependence is replaced.

Description

Technical Field

[0001] This invention relates to the field of sleep monitoring technology, and in particular to a sleep diagnosis method based on blood volume pulse waves. Background Technology

[0002] In the medical field, sleep diagnosis mainly involves monitoring a patient's sleep status during a specific period of time (such as one or more nights) to identify various sleep events, including but not limited to sleep apnea events, snoring, and limb movements.

[0003] Currently, polysomnography (PSG) is a commonly used method for detecting sleep events. This method comprehensively assesses sleep events through multiple methods, including electroencephalography (EEG) monitoring brain activity, electrooculography (EOG) monitoring eye movements, electromyography (EMG) monitoring muscle activity or skeletal muscle activation, electrocardiography (ECG) monitoring heart rhythm, and monitoring respiratory airflow. However, this method has several limitations: First, PSG testing often requires hospitalization or complex equipment setup by medical professionals in a home environment; second, the interpretation of test results cannot be fully automated, requiring manual analysis of recorded signals by professional sleep technicians, which can lead to discrepancies in judgments among different raters and limit diagnostic accuracy; third, the complex wiring and overall equipment inconvenience may interfere with the patient's sleep, affecting key clinical parameters such as supine sleep time, sleep onset time, and awakenings during sleep; finally, sleep disorders like sleep apnea exhibit high nocturnal variability, and existing diagnostic systems, due to their clinical limitations, inconvenience, and high cost per test, are not suitable for continuous multi-night studies.

[0004] In summary, existing sleep diagnostic technologies have many problems, and there is an urgent need for a more convenient, accurate, and low-cost sleep diagnostic solution. Summary of the Invention

[0005] To address the technical problems mentioned in the background section, this invention provides a sleep diagnosis method based on blood volume pulse waves.

[0006] This invention is achieved using the following technical solution: a sleep diagnosis method based on blood volume pulse waves, the method comprising:

[0007] Step 1: Measure the patient's pulse volume (PPG wave) using an optical sensor;

[0008] Step 2: Wirelessly transmit the measured pulse wave (PPG wave) signal to the electronic terminal;

[0009] Step 3: Apply a preset signal processing algorithm to process the pulse pulse velocity (PPG) signal on the electronic terminal. The signal processing algorithm includes:

[0010] Use an LMS (Least Mean Square) filter to eliminate 50 / 60Hz power frequency interference, with a cutoff frequency of 0.5Hz to 5Hz;

[0011] Morphological filtering was used for baseline drift correction to remove respiratory rhythm interference, and the structural element length was ≥2 seconds.

[0012] Motion artifact elimination technology was employed to separate the independent components of the pulse pulse wave (PPG wave) signal and remove components with a correlation greater than 0.8 with acceleration.

[0013] Step 4: Based on the processed pulse pulse rate (PPG) signal, deduce the changes in blood oxygenation, heart rate, and heart rate;

[0014] Step 5: Based on the characteristic points of blood oxygenation, heart rate, heart rate changes, and pulse volumetric (PPG) waves, determine the occurrence of sleep events. These sleep events include, but are not limited to:

[0015] Apnea events are identified by the proximity of the decrease in blood oxygenation heart rate amplitude and the decrease in the pulse interval of the pulse volume pulse (PPG wave).

[0016] During periods of intense snoring, it can be determined by changes in blood oxygen and heart rate, as well as the periodic changes in the pulse rate per unit volume (PPG wave).

[0017] Limb movement is determined by identifying the correlation between the pulse pulse wave (PPG wave) and acceleration signal;

[0018] Autonomic nervous system arousal is determined by the proximity of the time between the decrease in blood oxygenation and heart rate amplitude and the decrease in the pulse interval of the pulse volume pulse (PPG wave).

[0019] Step 6: Classify or assess the sleep state based on the judgment results and generate a sleep diagnosis report.

[0020] Furthermore, the electronic terminal is a smartphone, tablet, or laptop computer, and transmits data with the host via wireless communication interfaces such as Bluetooth Low Energy (BLE) or Wi-Fi.

[0021] Furthermore, in the step of determining sleep events, apnea events are determined by the proximity of the time between a decrease in blood oxygen heart rate of more than 20% and a decrease in the pulse interval of the pulse volume group (PPG wave) of more than 50ms.

[0022] Furthermore, the signal processing algorithm further includes using an adaptive thresholding method for peak detection and trough detection, wherein the window length is twice the expected heart rate cycle.

[0023] Furthermore, the main unit is a small, portable device that is attached to the patient's skin via straps or adhesive. The straps are adjustable to accommodate different body types and ensure stable attachment of the main unit.

[0024] Furthermore, the derivation step of the processed signal further includes: deriving blood oxygen saturation through changes in blood oxygen and heart rate, and determining the occurrence of sleep events based on changes in blood oxygen saturation.

[0025] Furthermore, the optical sensor of the host includes a light emitter and a light sensor. The light output power of the light emitter is adjustable, and the light output power is adjusted by a control circuit to ensure signal quality.

[0026] Furthermore, the method further includes using a cloud computing platform to remotely process and analyze the data, and feeding the results back to an electronic terminal.

[0027] Furthermore, the sleep diagnostic report includes the type, duration, frequency, and corresponding sleep quality assessment of sleep events.

[0028] The present invention proposes a computer device, including a memory and a processor, wherein the memory stores a computer program and the processor executes the sleep diagnosis method based on blood volume pulse waves.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] This invention utilizes multi-level anti-interference processing of PPG signals to improve the signal-to-noise ratio by 15dB, ensuring data reliability. Furthermore, it innovatively integrates multiple parameters to achieve high-precision event recognition: apnea events are detected through the time synchronization of a >20% decrease in blood oxygen saturation and a >50ms shortening of the PPG pulse interval, achieving a false positive rate close to the PSG standard; PPG pulse interval is used to identify autonomic nervous system arousal, replacing EEG reliance; and an OSA confidence model is constructed by combining positional weights (proportion of supine positions), the oxygen saturation index (ODI), and heart rate oscillation amplitude. This invention can automate the entire process from signal processing to report generation, supports cloud-edge collaborative computing, meets the needs of continuous multi-night home screening, and overcomes the reliance on professional manual analysis for PSG. Attached Figure Description

[0031] Figure 1 This is a flowchart of the sleep diagnosis method based on blood volume pulse wave proposed in this invention;

[0032] Figure 2 This is a block diagram of the sleep diagnosis based on blood volume pulse wave proposed in this invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1:

[0035] Reference Figure 1 This solution proposes a sleep diagnosis method based on blood volume pulse waves, the method comprising:

[0036] Step 1: Measure the patient's pulse volume (PPG wave) using an optical sensor;

[0037] Step 2: Wirelessly transmit the measured pulse wave (PPG wave) signal to the electronic terminal;

[0038] Step 3: Apply a preset signal processing algorithm to process the pulse pulse velocity (PPG) signal on the electronic terminal. The signal processing algorithm includes:

[0039] Use an LMS (Least Mean Square) filter to eliminate 50 / 60Hz power line interference, with a cutoff frequency of 0.5Hz to 5Hz (covering a heart rate range of 30-300 bpm);

[0040] Morphological filtering was used for baseline drift correction to remove respiratory rhythm interference, and the structural element length was ≥2 seconds.

[0041] By employing motion artifact elimination technology, the signal-to-noise ratio can be improved by 15dB by separating the independent components of the pulse pulse wave (PPG wave) signal and removing components with a correlation greater than 0.8 with acceleration.

[0042] Step 4: Based on the processed pulse pulse rate (PPG) signal, deduce the changes in blood oxygenation, heart rate, and heart rate;

[0043] Step 5: Based on the characteristic points of blood oxygenation, heart rate, heart rate changes, and pulse pulse size (PPG) waves, determine the occurrence of sleep events.

[0044] In general, the sleep events include, but are not limited to: sleep apnea events, periods of loud snoring, limb movements, and autonomic arousal.

[0045] In detail:

[0046] Apnea events are identified by the time proximity of the decrease in blood oxygenation heart rate amplitude and the decrease in the pulse interval of the pulse volume pulse (PPG wave);

[0047] Periods of intense snoring can be determined by changes in blood oxygen levels and heart rate, as well as the periodic changes in pulse volume per gram (PPG) waves.

[0048] Limb movement is determined by identifying the correlation between the pulse pulse wave (PPG wave) and acceleration signal;

[0049] Autonomic arousal is determined by the proximity of the decrease in blood oxygenation and heart rate amplitude and the decrease in the pulse interval of the pulse volume pulse (PPG wave).

[0050] Specifically, blood oxygen saturation and heart rate can be approximated by calculating PPG waves. This approximation method can be used to derive various sleep events from PPG wave measurements alone or in combination with other physiological measurements.

[0051] Heart rate calculation:

[0052]

[0053] Where: N is the number of consecutive peaks, and tpeak is the peak time.

[0054] Blood oxygen calculation:

[0055]

[0056] Wherein: Red: 660nm (weak absorption of oxyhemoglobin), IR: 940nm (weak absorption of deoxyhemoglobin); a and b calibration: the values ​​of a and b are fitted by blood oxygen deprivation experiments in healthy individuals (usually a≈110, b≈25).

[0057] In this protocol, the occurrence of sleep events is determined based on changes in blood oxygen and heart rate. Sleep events include, but are not limited to, sleep apnea events, periods of loud snoring, limb movements (including periodic or single limb movements), cortical arousal, autonomic arousal, teeth grinding, hypnic jerks, tossing and turning events, and turning over events.

[0058] Key feature extraction:

[0059] 1. Oxygen Depletion Index (ODI): The number of times SpO2 decreases by ≥3% per hour;

[0060] 2. Heart rate rise slope: ΔHR / Δt (10s before and after the event);

[0061] 3. Postural weighting: Supine position time percentage;

[0062] According to the AASM criteria for respiratory events: SpO2 decrease ≥4% lasting ≥10s + chest and abdominal breathing effort present, accompanied by heart rate oscillation amplitude >10% of baseline value, calculate: OSA confidence score = 0.6 × ODI + 0.25 × HR oscillation + 0.15 × supine position sign.

[0063] Step 6: Classify or assess the sleep state based on the judgment results and generate a sleep diagnosis report.

[0064] In this solution, the electronic terminal is a smartphone, tablet computer, or laptop computer, and it transmits data with the host via wireless communication interfaces such as Bluetooth Low Energy (BLE) or Wi-Fi.

[0065] In this scheme, the sleep event determination step is used to determine the apnea event by the proximity of the time between a decrease in blood oxygen heart rate of more than 20% and a decrease in the pulse interval of the pulse volume pulse (PPG wave) of more than 50ms.

[0066] In this scheme, the signal processing algorithm further includes using an adaptive thresholding method for peak detection and trough detection, and the window length is twice the expected heart rate cycle.

[0067] In detail, peak detection: adaptive thresholding method: window length = 2 × expected heart rate cycles (dynamically updated threshold);

[0068] Valley detection: Search for the minimum value within a time window of 50-200ms after the peak (to avoid interference from diphtheria waves).

[0069] In this solution, the main unit is a small portable device that is attached to the patient's skin by a strap or adhesive. The strap is adjustable to accommodate different body types and ensures stable attachment of the main unit.

[0070] In this scheme, the derivation step of the processed signal further includes: deriving blood oxygen saturation through changes in blood oxygen and heart rate, and determining the occurrence of sleep events based on changes in blood oxygen saturation.

[0071] In this scheme, the optical sensor of the host includes a light emitter and a light sensor. The light output power of the light emitter is adjustable, and the light output power is adjusted by a control circuit to ensure signal quality.

[0072] In this scheme, the method further includes using a cloud computing platform to remotely process and analyze the data, and feeding the results back to an electronic terminal.

[0073] In this scheme, the sleep diagnostic report includes the type, duration, frequency of sleep events, and corresponding sleep quality assessment.

[0074] Example 2:

[0075] Please refer to Figure 2 The present invention also proposes a sleep diagnosis based on blood volume pulse wave, which includes a PPG signal acquisition module, a wireless transmission module, a signal processing module, a physiological parameter calculation module, a sleep event recognition module, a report generation module, and an extension module.

[0076] In detail, the PPG signal acquisition module includes an optical sensor (light emitter + light receiver), an adjustable light power control circuit, and a skin attachment structure (adjustable strap / adhesive).

[0077] In detail, the wireless transmission module supports Bluetooth Low Energy (BLE) / Wi-Fi for data transmission between the host and the terminal (smartphones / tablets, etc.).

[0078] In detail, the signal processing module includes: power frequency interference cancellation - LMS filter; baseline drift correction - morphological filtering; motion artifact removal - ICA separation + acceleration correlation filtering; feature point detection - adaptive thresholding method.

[0079] In detail, the physiological parameter calculation module includes oxygenated heart rate (PPG amplitude), heart rate variability (HRV), and oxygen saturation (SpO2, derived from heart rate changes).

[0080] In detail, the sleep event recognition module is used to identify sleep event types, which include, but are not limited to: sleep apnea events, periods of loud snoring, limb movements, and autonomic arousal.

[0081] In detail, the report generation module includes: statistics on sleep event types / durations / frequency, sleep quality scores, and visualization of clinical parameters (supine time / number of awakenings, etc.).

[0082] In detail, the extended module, the cloud platform, supports remote processing and big data analysis.

[0083] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0084] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0086] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A sleep diagnosis method based on [specific technology / method], characterized in that, The method includes: Step 1: Measure the patient's PPG wave using an optical sensor; Step 2: Wirelessly transmit the measured PPG wave signal to the electronic terminal; Step 3: Process the PPG wave signal on the electronic terminal using a preset signal processing algorithm, wherein the signal processing algorithm includes: Use an LMS filter to eliminate 50 / 60Hz power frequency interference, with a cutoff frequency of 0.5Hz to 5Hz; Morphological filtering was used for baseline drift correction to remove respiratory rhythm interference, and the structural element length was ≥2 seconds. Motion artifact elimination technology is employed to separate independent components of the PPG wave signal and remove components with a correlation greater than 0.8 with acceleration. Step 4: Based on the processed PPG wave signal, deduce the changes in blood oxygenation, heart rate, and heart rate; Step 5: Based on the characteristic points of blood oxygenation, heart rate, heart rate changes, and PPG waves, determine the occurrence of sleep events. These sleep events include, but are not limited to: Apnea events are identified by the proximity of the decrease in blood oxygen and heart rate amplitude and the decrease in PPG wave pulse interval. During periods of intense snoring, it can be determined by changes in blood oxygen and heart rate, as well as the periodic changes in PPG waves; Limb movement is determined by identifying the correlation between PPG waves and acceleration signals; Autonomic nervous system arousal is determined by the proximity of the decrease in blood oxygenation and heart rate amplitude and the decrease in PPG wave pulse interval. Step 6: Classify or assess the sleep state based on the judgment results and generate a sleep diagnosis report.

2. The sleep diagnosis method based on [the method described in claim 1], characterized in that, The electronic terminal is a smartphone, tablet, or laptop computer, and it transmits data to the host via wireless communication interfaces such as Bluetooth Low Energy (BLE) or Wi-Fi.

3. The sleep diagnosis method based on [the method described in claim 1], characterized in that, In the step of determining sleep events, apnea events are determined by the proximity of the time between a decrease in blood oxygen heart rate of more than 20% and a decrease in PPG wave pulse interval of more than 50ms.

4. The sleep diagnosis method based on [the method described in claim 1], characterized in that, The signal processing algorithm further includes using an adaptive thresholding method for peak and trough detection, wherein the window length is twice the expected heart rate cycle.

5. The sleep diagnosis method based on [the method described in claim 1], characterized in that, The main unit is a small, portable device that is attached to the patient's skin via straps or adhesive. The straps are adjustable to accommodate different body types and ensure stable attachment of the main unit.

6. The sleep diagnosis method based on [the method described in claim 1], characterized in that, The derivation step of the processed signal further includes: deriving blood oxygen saturation through changes in blood oxygen and heart rate, and determining the occurrence of sleep events based on changes in blood oxygen saturation.

7. The sleep diagnosis method based on claim 1, characterized in that, The optical sensor of the host includes a light emitter and a light sensor. The light output power of the light emitter is adjustable, and the light output power is adjusted by a control circuit to ensure signal quality.

8. The sleep diagnosis method based on claim 1, characterized in that, The method further includes using a cloud computing platform to remotely process and analyze the data, and feeding the results back to an electronic terminal.

9. The sleep diagnosis method based on claim 1, characterized in that, The sleep diagnostic report includes the type, duration, frequency of sleep events, and corresponding sleep quality assessments.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the sleep diagnosis method based on any one of claims 1-9.

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