WO2020114448A1 - 一种光电容积脉搏波信号特征点检测方法及装置 - Google Patents

一种光电容积脉搏波信号特征点检测方法及装置 Download PDF

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WO2020114448A1
WO2020114448A1 PCT/CN2019/123252 CN2019123252W WO2020114448A1 WO 2020114448 A1 WO2020114448 A1 WO 2020114448A1 CN 2019123252 W CN2019123252 W CN 2019123252W WO 2020114448 A1 WO2020114448 A1 WO 2020114448A1
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pulse wave
wave signal
volume pulse
photoelectric volume
signal
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PCT/CN2019/123252
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English (en)
French (fr)
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李烨
刘增丁
苗芬
刘记奎
闻博
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深圳先进技术研究院
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Definitions

  • the present invention relates to the technical field of biomedical signal processing, and in particular, to a method and device for detecting a characteristic point of a photoelectric volume pulse wave signal.
  • Photoplethysmography (PhotoPlethysmoGraphy, PPG) contains a wealth of cardiovascular system information, and PPG signal feature analysis has been widely used in the assessment of cardiovascular system health.
  • the PPG signal is easily affected by the temperature of the receptor surface, the hypoperfusion of body surface tissue and the ambient light during the acquisition process.
  • the PPG signal shape may be greatly distorted, which makes it difficult to detect the characteristic points of the PPG signal.
  • the prior art proposes a method for detecting the valley and peak of the PPG signal based on the adaptive threshold.
  • the threshold drop curve is easily affected by the high-amplitude repetition wave at the falling edge of the PPG signal, making the valley detection error; at the same time, the PPG signal When the waveform changes greatly, the detection effect of this method is poor.
  • the object of the present invention is to provide a method and device for detecting the characteristic points of photoelectric volume pulse wave signals to solve the above-mentioned problems.
  • an embodiment of the present invention provides a method for detecting a characteristic point of a photoelectric volume pulse wave signal.
  • the method for detecting a characteristic point of a photoelectric volume pulse wave signal includes:
  • Pre-process the original photoelectric volume pulse wave signal to obtain the first photoelectric volume pulse wave signal and the second photoelectric volume pulse wave signal;
  • the characteristic point of the original photoelectric volume pulse wave signal is determined according to the normalized signal and the first photoelectric volume pulse wave signal.
  • an embodiment of the present invention further provides a photoelectric volume pulse wave signal characteristic point detection device.
  • the photoelectric volume pulse wave signal characteristic point detection device includes:
  • a preprocessing unit which is used to preprocess the original photoelectric volume pulse wave signal to obtain the first photoelectric volume pulse wave signal and the second photoelectric volume pulse wave signal;
  • a calculation unit configured to calculate the fundamental frequency value of the original photoelectric volume pulse wave signal according to the first photoelectric volume pulse wave signal
  • a normalization unit configured to use the fundamental frequency value to perform segmented amplitude normalization processing on the second photoelectric volume pulse wave signal to obtain a normalized signal
  • the characteristic point determining unit is configured to determine the characteristic point of the original photoelectric volume pulse wave signal according to the normalized signal and the first photoelectric volume pulse wave signal.
  • the method and device for detecting the characteristic points of the photoelectric volume pulse wave signal obtain the first photoelectric volume pulse wave signal and the second photoelectric volume pulse wave signal by preprocessing the original photoelectric volume pulse wave signal, according to the first photoelectric volume
  • the pulse wave signal calculates the fundamental frequency value of the original photoelectric volume pulse wave signal, and then uses the fundamental frequency value to perform a piecewise amplitude normalization process on the second photoelectric volume pulse wave signal to obtain a normalized signal, thereby according to the normalized signal Determine the characteristic points of the original photoelectric volume pulse wave signal.
  • the second photoelectric volume pulse wave signal is normalized by the amplitude of the second photoelectric volume pulse wave signal through the fundamental frequency value to enhance and highlight the position of the original photoelectric volume pulse wave signal in the normalized signal, thereby improving the original photoelectric volume pulse wave signal trough And the accuracy of peak feature detection.
  • Fig. 1 is a waveform diagram of a photoelectric volume pulse wave signal.
  • FIG. 2 is a block diagram of a human body parameter detection device applicable to the present invention.
  • FIG. 3 is a flowchart of a method for detecting a characteristic point of a photoelectric volume pulse wave signal provided by the present invention.
  • FIG. 4 is a specific flowchart of a method for detecting a characteristic point of a photoelectric volume pulse wave signal.
  • FIG. 5 is an effect diagram of performing a power operation on a differential signal.
  • FIG. 6 is a signal frequency distribution diagram based on the first photoelectric volume pulse wave signal.
  • FIG. 7 is a waveform diagram of each signal generated in the process of detecting a characteristic point of a photoelectric volume pulse wave signal.
  • FIG. 8 is a detection result obtained by using the detection method of the characteristic point of the photoelectric volume pulse wave signal provided by the present invention when the shape distortion of the original photoelectric volume pulse wave signal is large.
  • FIG. 9 is a functional block diagram of a photoelectric volume pulse wave signal characteristic point detection device provided by the present invention.
  • Icon 100-human body parameter detection equipment; 110-memory; 120-processor; 130-communication unit; 200-photoelectric volume pulse wave signal characteristic point detection device; 210-preprocessing unit; 220-calculation unit; 230-normalization Conversion unit; 240-feature point determination unit.
  • a periodic waveform of PPG signal is divided into ascending branch and descending branch, and includes features such as trough, crest, and dicrotic wave.
  • troughs and crests are important feature point positions of PPG signals, reflecting the beginning of ventricular ejection and the end of ventricular ejection, respectively. Therefore, the accurate detection of PPG signal troughs and peaks has become an important guarantee for PPG signal assessment of cardiovascular health.
  • the PPG signal is susceptible to environmental light intensity and body surface temperature during the acquisition process; in addition, baseline drift caused by respiration or sensor movement and low perfusion of body surface tissue can cause PPG signal morphological distortion and give PPG signal troughs And peak detection brings great difficulties.
  • a method for detecting the trough and crest of PPG signals based on adaptive thresholds in the prior art is to use a 0.5 to 10 Hz band-pass filter to remove low and high frequencies in the PPG signal. Noise; after denoising the PPG signal, the rising and falling slopes of the threshold curve are adaptively modified by the amplitude of the PPG signal. When the threshold curve and the PPG band coincide, look for the inflection point along the rising and falling edges of the PPG as the peak.
  • the threshold drop curve is easily affected by the high-amplitude repetition wave at the falling edge of the PPG signal, making the trough detection error; meanwhile, when the PPG signal waveform changes greatly, the detection effect of this method is poor. Therefore, designing a detection method that can accurately detect the positions of troughs and peaks when the amplitude and shape of the PPG signal changes greatly is of great significance for the PPG signal to be used in the assessment of the health of the cardiovascular system.
  • the human body parameter detection device 100 includes a photoelectric volume pulse wave signal characteristic point detection device 200, a memory 110, a processor 120, and a communication unit 130.
  • the elements of the memory 110, the processor 120, and the communication unit 130 are directly or indirectly electrically connected to each other to implement data transmission or interaction.
  • these components can be electrically connected to each other through one or more communication buses or signal lines.
  • the photoelectric volume pulse wave signal characteristic point detection device 200 includes at least one operating system (Operating System, OS) that can be stored in the memory 110 in the form of software or firmware (Firmware) or solidified in the human body parameter detection device 100
  • OS operating System
  • firmware firmware
  • the processor 120 is used to execute an executable module stored in the memory 110, such as a software function module and a computer program included in the photoelectric volume pulse wave signal characteristic point detection device 200.
  • the memory 110 is used to store programs or data.
  • the memory 110 may be, but not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), may Erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
  • FIG. 2 is only a schematic diagram of the structure of the human body parameter detection device 100, and the human body parameter detection device 100 may further include more or fewer components than those shown in FIG. 2 shows different configurations. Each component shown in FIG. 2 may be implemented by hardware, software, or a combination thereof.
  • the invention provides a photoelectric volume pulse wave signal characteristic point detection method, which is applied to the human body parameter detection device 100 and is used for accurately and quickly detecting the peak and trough of the photoelectric volume pulse wave signal.
  • FIG. 3 is a flowchart of a method for detecting a characteristic point of a photoelectric volume pulse wave signal provided by the present invention.
  • the photoelectric volume pulse wave signal characteristic point detection method includes:
  • S301 Pre-process the original photoelectric volume pulse wave signal to obtain a first photoelectric volume pulse wave signal and a second photoelectric volume pulse wave signal.
  • FIG. 4 is a specific flowchart of a method for detecting a characteristic point of a photoelectric volume pulse wave signal.
  • the S301 includes:
  • S3011 Perform a low-pass filtering operation on the original photoelectric volume pulse wave signal to obtain the first photoelectric volume pulse wave signal.
  • the original photoelectric volume pulse wave signal x(n) collected often has baseline drift (low frequency) and myoelectric (high frequency) noise interference, in order to accurately detect the position of the characteristic point of the PPG signal, the characteristic point of the PPG signal is detected. Before, the PPG signal needs to be denoised.
  • bandpass filters or wavelet filtering methods are generally used to remove low-frequency noise and high-frequency noise in PPG signals, but these two denoising methods are more complicated and require longer time consumption, which is not convenient for signals in portable devices Real-time processing.
  • the present invention uses a simple integer low-pass filter to remove the high-frequency noise of the PPG signal to obtain the denoised PPG signal, which is the first photoelectric volume pulse wave signal f( n).
  • the transfer function of the integer low-pass filter is:
  • f s is the sampling frequency of the PPG signal
  • f L is the cut-off frequency of the low-pass filter
  • N is a positive integer.
  • f L 20 Hz.
  • S3012 Perform differential operation on the first photoelectric volume pulse wave signal to obtain a differential signal.
  • the difference operation has the characteristics of removing the baseline drift and highlighting the position of the singular point. Therefore, the baseline drift (low frequency) noise interference contained in the original photoelectric volume pulse wave signal is suppressed by the difference operation.
  • the following first-order difference formula is used to perform a differential operation on the first photoelectric volume pulse wave signal f(n), and the difference value less than 0 is set to 0 To obtain the differential signal d(n).
  • the first-order difference formula is:
  • S3013 Perform a power operation on the differential signal to obtain a second photoelectric volume pulse wave signal.
  • the second signal photoelectric volume pulse wave signal dd(n) is obtained by performing a power operation on the differential signal d(n).
  • the power index k is related to the signal-to-noise ratio of the original photoelectric volume pulse wave signal.
  • Fig. 5 Please refer to Fig. 5 for the effect diagram of performing the power operation on the differential signal. It can be seen that as the power index k increases, the noise is gradually suppressed, and the position of the feature point in the signal becomes more prominent.
  • the noise interference in the original photoelectric volume pulse wave signal can be effectively suppressed, so that the subsequent detection based on taking the first photoelectric volume pulse wave signal and the second photoelectric volume pulse wave signal The result is more precise.
  • S302 Calculate the fundamental frequency value of the original photoelectric volume pulse wave signal according to the first photoelectric volume pulse wave signal.
  • the fundamental frequency value of the original photoelectric volume pulse wave signal can reflect the pulse pulsation frequency and the heartbeat frequency.
  • fast Fourier transform is performed on the first photoelectric volume pulse wave signal to obtain the signal frequency distribution of the first photoelectric volume pulse wave signal, and then the frequency value with the largest amplitude within the preset frequency range is used as the fundamental frequency value .
  • the human heartbeat frequency is between 30 times/min and 300 times/min, that is, between 0.5 Hz and 5 Hz. Therefore, in an alternative embodiment, the preset The frequency range is 0.5 Hz to 5 Hz. Therefore, the frequency value having the largest amplitude within 0.5 Hz to 5 Hz is taken as the fundamental frequency value.
  • S302 can be directly executed after S3011 is executed, and it is not necessary to wait until S3012 and S3013 are executed.
  • S303 Perform a segmented amplitude normalization process on the second photoelectric volume pulse wave signal using the fundamental frequency value to obtain a normalized signal.
  • f s is the sampling frequency
  • f m is the fundamental frequency value
  • ⁇ is a preset constant
  • S3032 Perform a segment amplitude normalization process on the second photoelectric volume pulse wave signal according to the signal segment length to obtain a normalized signal.
  • the normalized signal s(n) obtained according to S3032 is:
  • s i (j) is the jth data of the ith segment of s(n)
  • dd i (j) is the jth data of the ith segment of dd(n)
  • 1 ⁇ i ⁇ len/M, ( i-1) ⁇ M+1 ⁇ j ⁇ i ⁇ M, len refers to the length of dd(n)
  • min i and max i are the minimum and maximum values of the i-th stage of dd(n), respectively.
  • S304 Determine the characteristic point of the original photoelectric volume pulse wave signal according to the normalized signal and the first photoelectric volume pulse wave signal.
  • S304 includes:
  • S3041 Determine a plurality of reference positions of feature points according to the normalized signal, wherein the amplitude corresponding to each reference position of the feature point is 1 and the interval between the reference positions of two adjacent feature points is greater than or equal to the first threshold .
  • the amplitude corresponding to each feature point reference position is 1, and the interval between two adjacent feature point reference positions is greater than or equal to the first threshold.
  • the peak between two adjacent PPG signals must be greater than the duration of the refractory period.
  • the duration of the refractory period is 200 ms.
  • the first threshold is 200 ms.
  • all the points with the amplitude of 1 in the normalized signal can be determined first, and then the first point with the amplitude of 1 as the first feature point reference position to eliminate the multi-check points.
  • the checkpoint is a point whose distance from the reference position of the previous feature point is smaller than the first threshold although the amplitude is 1, so as to determine the reference positions of all feature points.
  • the reference position of the feature point actually corresponds to the rising edge position of the PPG signal.
  • the time domain range determined by the reference position of the feature point is related to the frequency range of the PPG signal.
  • the process of determining the position of the trough and the peak of the wave is to take the reference position of each feature point as the rising edge position of a PPG signal, map the reference position of the feature point to the first photoelectric volume pulse wave signal to find the trough position of the PPG signal, and The process of peak position.
  • FIG. 7 is a waveform diagram of each signal generated in the process of using the photoelectric volume pulse wave signal characteristic point detection method.
  • (a) is the original photoelectric volume pulse wave signal x(n);
  • (b) is the low-pass filtered first photoelectric volume pulse wave signal f(n);
  • (c) is the differential signal d(n);
  • ( d) is the second photoelectric volume pulse wave signal dd(n) after the power calculation;
  • (e) is the normalized signal s(n);
  • (f) is the first photoelectric volume pulse wave signal f(n) The detection effect of trough and crest position.
  • FIG. 8 when the original photoelectric volume pulse wave signal has a large shape distortion, the detection result obtained by using the photoelectric volume pulse wave signal characteristic point detection method provided by the present invention.
  • (a) is a schematic diagram of positioning positions of troughs and peaks in the first photoelectric volume pulse wave signal f(n);
  • (b) is a schematic diagram of positioning positions of characteristic points of the normalized signal s(n).
  • the photoelectric volume pulse wave signal characteristic point detection device 200 may The device structure of the human body parameter detection device 100 shown in FIG. 2 described above is used. Further, please refer to FIG. 9, which is a functional block diagram of a device 200 for detecting a characteristic point of a photoelectric volume pulse wave signal according to an embodiment of the present invention. It should be noted that the basic principle and the technical effect of the photoelectric volume pulse wave signal characteristic point detection device 200 provided in this embodiment are the same as those in the above embodiment. For the sake of brief description, some parts of this embodiment are not mentioned. Reference can be made to the corresponding content in the above embodiments.
  • the photoelectric volume pulse wave signal characteristic point detection device 200 includes a preprocessing unit 210, a calculation unit 220, a normalization unit 230, and a characteristic point determination unit 240.
  • the preprocessing unit 210 is used to preprocess the original photoelectric volume pulse wave signal to obtain the first photoelectric volume pulse wave signal and the second photoelectric volume pulse wave signal.
  • the preprocessing unit 210 is used to perform a low-pass filtering operation on the original photoelectric volume pulse wave signal to obtain the first photoelectric volume pulse wave signal, and perform a differential operation on the first photoelectric volume pulse wave signal to obtain a differential signal, and then The differential signal performs a power operation to obtain a second photoelectric volume pulse wave signal.
  • the pre-processing unit 210 may be used to execute S301, S3011, S3012, and S3013.
  • the calculating unit 220 is used to calculate the fundamental frequency value of the original photoelectric volume pulse wave signal according to the first photoelectric volume pulse wave signal.
  • calculation unit 220 may be used to perform S302.
  • the normalization unit 230 is used to perform a segmented amplitude normalization process on the second photoelectric volume pulse wave signal using the fundamental frequency value to obtain a normalized signal.
  • the normalization unit 230 is used to calculate the signal segment length according to the sampling frequency and fundamental frequency value of the original photoelectric volume pulse wave signal, and to segment the amplitude of the second photoelectric volume pulse wave signal according to the signal segment length Normalization processing to obtain a normalized signal.
  • the normalization unit 230 may be used to execute S303, S3031, and S3032.
  • the characteristic point determining unit 240 is used to determine the characteristic point of the original photoelectric volume pulse wave signal according to the normalized signal and the first photoelectric volume pulse wave signal.
  • the feature point determining unit 240 is used to determine multiple feature point reference positions according to the normalized signal, where each feature point reference position corresponds to an amplitude of 1, and between two adjacent feature point reference positions
  • the interval of is greater than or equal to the first threshold, and based on the first photoelectric volume pulse wave signal, the first zero-crossing position located in the time domain determined by the reference position of the feature point and to the left of the reference position of the feature point is taken as For the valley position, the position of the first maximum point located within the time domain determined by the reference position of the feature point and to the right of the reference position of the feature point is taken as the peak position.
  • the normalization unit 230 may be used to execute S304, S3041, and S3042.
  • the above-mentioned modules may be stored in the memory 110 shown in FIG. 2 in the form of software or firmware (Firmware) or solidified in the operating system (Operating System, OS) of the user equipment, and may be processed by the processor in FIG. 2 120 implementation.
  • the data required to execute the above modules, the code of the program, etc. may be stored in the memory 110.
  • the photoelectric volume pulse wave signal characteristic point detection method and device obtain the first photoelectric volume pulse wave signal and the second photoelectric volume pulse wave signal by preprocessing the original photoelectric volume pulse wave signal, Based on the first photoelectric volume pulse wave signal, the fundamental frequency value of the original photoelectric volume pulse wave signal is calculated, and then the second photoelectric volume pulse wave signal is subjected to the normalized processing of the segment amplitude using the fundamental frequency value to obtain a normalized signal, thereby The characteristic points of the original photoelectric volume pulse wave signal are determined according to the normalized signal.
  • the second photoelectric volume pulse wave signal is normalized by the amplitude of the second photoelectric volume pulse wave signal through the fundamental frequency value to enhance and highlight the position of the original photoelectric volume pulse wave signal in the normalized signal, thereby improving the original photoelectric volume pulse wave signal trough And the accuracy of peak feature detection.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of code that contains one or more of the Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved.
  • each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented with dedicated hardware-based systems that perform specified functions or actions Or, it can be realized by a combination of dedicated hardware and computer instructions.
  • the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
  • the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present invention essentially or part of the contribution to the existing technology or part of the technical solution can be embodied in the form of a software product, the computer software product is stored in a storage medium, including Several instructions are used to enable a computer device (which may be a personal computer, a human body parameter detection device, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present invention.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code .

Abstract

一种光电容积脉搏波信号特征点检测方法及装置，涉及生物医学信号处理技术领域。该方法及装置通过对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号（S301），根据第一光电容积脉搏波信号计算原始光电容积脉搏波信号的基频值（S302），然后利用基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号（S303），从而根据归一化信号确定原始光电容积脉搏波信号的特征点（S304）。通过基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，以增强和凸显原始光电容积脉搏波信号在归一化信号中的位置，提高了原始光电容积脉搏F波信号波谷和波峰特征检测的准确度。

Description

一种光电容积脉搏波信号特征点检测方法及装置 技术领域
本发明涉及生物医学信号处理技术领域，具体而言，涉及一种光电容积脉搏波信号特征点检测方法及装置。
背景技术
光电容积脉搏波(PhotoPlethysmoGraphy，PPG)蕴含丰富的心血管系统信息，PPG信号特征分析已经广泛运用于心血管系统健康状况评估中。准确检测PPG信号特征点位置，尤其是波谷和波峰位置，对PPG信号特征分析具有重要的作用。然而PPG信号在采集过程中容易受体表温度、体表组织低灌注以及环境光照影响，PPG信号形态可能会产生较大畸变，给PPG信号特征点检测造成困难。
现有技术提出了一种基于自适应阈值的PPG信号波谷和波峰检测方法，然而其阈值下降曲线很容易受到PPG信号下降沿中高幅值重搏波的影响，使得波谷检测出错；同时在PPG信号波形变化较大时，该方法的检测效果较差。
发明内容
有鉴于此，本发明的目的在于提供一种光电容积脉搏波信号特征点检测方法及装置，以解决上述问题。
为了实现上述目的，本发明实施例采用的技术方案如下：
第一方面，本发明实施例提供了一种光电容积脉搏波信号特征点检测方法，所述光电容积脉搏波信号特征点检测方法包括：
对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号；
根据所述第一光电容积脉搏波信号计算所述原始光电容积脉搏波信号的基频值；
利用所述基频值对所述第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号；
根据所述归一化信号及所述第一光电容积脉搏波信号确定所述原始光电容积脉搏波信号的特征点。
第二方面，本发明实施例还提供了一种光电容积脉搏波信号特征点检测装置，所述光电容积脉搏波信号特征点检测装置包括：
预处理单元，用于对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号；
计算单元，用于根据所述第一光电容积脉搏波信号计算所述原始光电容积脉搏波信号的基频值；
归一化单元，用于利用所述基频值对所述第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号；
特征点确定单元，用于根据所述归一化信号及所述第一光电容积脉搏波信号确定所述原始光电容积脉搏波信号的特征点。
本发明提供的光电容积脉搏波信号特征点检测方法及装置，通过对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号，根据第一光电容积脉搏波信号计算原始光电容积脉搏波信号的基频值，然后利用基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号，从而根据归一化信号确定原始光电容积脉搏波信号的特征点。通过基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，以增强和凸显原始光电容积脉搏波信号在归一化信号中的位置，提高了原始光电容积脉搏波信号波谷和波峰特征检测的准确度。
为使本发明的上述目的、特征和优点能更明显易懂，下文特举较佳实施例，并配合所附附图，作详细说明如下。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本发明的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为光电容积脉搏波信号的波形图。
图2为可应用于本发明的人体参数检测设备的方框示意图。
图3为本发明提供的光电容积脉搏波信号特征点检测方法的流程图。
图4为光电容积脉搏波信号特征点检测方法的具体流程图。
图5为对差分信号进行次方运算的效果图。
图6为基于第一光电容积脉搏波信号的信号频率分布图。
图7为利用光电容积脉搏波信号特征点检测方法过程中产生的各信号的波形图。
图8为原始光电容积脉搏波信号形态畸变较大时，利用本发明提供的光电容积脉搏波信号特征点检测方法得到的检测结果。
图9为本发明提供的光电容积脉搏波信号特征点检测装置的功能模块图。
图标：100-人体参数检测设备；110-存储器；120-处理器；130-通信单元；200-光电容积脉搏波信号特征点检测装置；210-预处理单元；220-计算单元；230-归一化单元；240-特征点确定单元。
具体实施方式
下面将结合本发明实施例中附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本发明实施例的组件可以 以各种不同的配置来布置和设计。
因此，以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施例。基于本发明的实施例，本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。
需要说明的是，术语“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
如图1所示，PPG信号一个周期波形分为上升支和下降支两部分，并包括波谷、波峰和重搏波等特征。其中，波谷和波峰是PPG信号的重要特征点位置，分别反映心室射血开始和心室射血结束。因此，PPG信号波谷和波峰的准确检测已成为PPG信号评估心血管健康的重要保障。
然而，PPG信号在采集过程中容易受到环境光照强度和体表温度影响；此外，呼吸作用或传感器移动带来的基线漂移以及体表组织的低灌注影响会引起PPG信号形态畸变，给PPG信号波谷和波峰检测带来很大困难。
经过发明人的研究发现，现有技术的一种基于自适应阈值的PPG信号波谷和波峰检测方法，其主要思想是：先使用0.5～10Hz的带通滤波器去除PPG信号中的低频和高频噪声；在对PPG信号去噪后，通过PPG信号幅值大小自适应修正阈值曲线上升和下降的斜率，当阈值曲线和PPG波段重合时，分别沿着PPG上升沿和下降沿寻找拐点位置作为波峰和波谷，然而其阈值下降曲线很容易受到PPG信号下降沿中高幅值重搏波影响，使得波谷检测出错；同时，在PPG信号波形变化较大时，该方法的检测效果较差。因此，设计一种能够在PPG信号幅值和形态变化较大时能准确检测波谷和波峰位置的检测方法，对于PPG信号用于心血管系统健康状况的评估具有重要的意义。
请参照图2，是人体参数检测设备100的方框示意图。所述人体参数检测设备100包括光电容积脉搏波信号特征点检测装置200、存储器110、处理器120以及通信单元130。
所述存储器110、处理器120以及通信单元130各元件相互之间直接或间接地电性连接，以实现数据的传输或交互。例如，这些元件相互之间可通过一条或多条通讯总线或信号线实现电性连接。
所述光电容积脉搏波信号特征点检测装置200包括至少一个可以软件或固件(Firmware)的形式存储于所述存储器110中或固化在所述人体参数检测设备100的操作系统(Operating System，OS)中的软件功能模块。
所述处理器120用于执行所述存储器110中存储的可执行模块，例如所述光电容积脉搏波信号特征点检测装置200所包括的软件功能模块及计算机程序等。
其中，存储器110用于存储程序或者数据。所述存储器110可以是，但不限于，随机存取存储器(Random Access Memory，RAM)，只读存储器(Read  Only Memory，ROM)，可编程只读存储器(Programmable Read-Only Memory，PROM)，可擦除只读存储器(Erasable Programmable Read-Only Memory，EPROM)，电可擦除只读存储器(Electric Erasable Programmable Read-Only Memory，EEPROM)等。
应当理解的是，图2所示的结构仅为人体参数检测设备100的结构示意图，所述人体参数检测设备100还可包括比图2中所示更多或者更少的组件，或者具有与图2所示不同的配置。图2中所示的各组件可以采用硬件、软件或其组合实现。
本发明提供了一种光电容积脉搏波信号特征点检测方法，应用于上述人体参数检测设备100，用于准确、快速地检测光电容积脉搏波信号的波峰及波谷。请参阅图3，为本发明提供的光电容积脉搏波信号特征点检测方法的流程图。该光电容积脉搏波信号特征点检测方法包括：
S301，对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号。
请参阅图4，为光电容积脉搏波信号特征点检测方法的具体流程图。该S301包括：
S3011，对原始光电容积脉搏波信号进行低通滤波操作以获取第一光电容积脉搏波信号。
由于采集到的原始光电容积脉搏波信号x(n)往往存在基线漂移(低频) 和肌电(高频)噪声干扰，为能准确检测PPG信号的特征点位置，在对PPG信号进行特征点检测之前，需先对PPG信号进行去噪处理。
在现有技术中，一般是利用带通滤波器或者小波滤波方法去除PPG信号中的低频噪声和高频噪声，但这两种去噪方法较为复杂，时间消耗较长，不便于便携式设备中信号实时处理。
因此，为简化去噪流程，能够快速实现去噪，本发明使用简单整系数低通滤波器去除PPG信号高频噪声，得到去噪后的PPG信号，即为第一光电容积脉搏波信号f(n)。
具体地，该整系数低通滤波器传递函数为：
Figure PCTCN2019123252-appb-000001
其中，f s为PPG信号的采样频率，f L为低通滤波器的截止频率，N为正整数。
由于PPG信号频率主要集中在0～20Hz，因此设计截止频率为20Hz的整系数低通滤波器，因此在一种可选的实施方式中，f L＝20Hz。
S3012，对第一光电容积脉搏波信号进行差分运算以获得差分信号。
差分运算具有去除基线漂移和凸显奇异点位置的特点，因此原始光电容积脉搏波信号中包含的基线漂移(低频)噪声干扰便通过差分运算进行抑制。
在一种可选的实施方式中，为便于后期检测波峰、波谷，利用以下一阶差分算式对第一光电容积脉搏波信号f(n)进行差分运算，并将差分数值小于0的置为0，从而得到差分信号d(n)。具体地，该一阶差分算式为：
Figure PCTCN2019123252-appb-000002
S3013，对差分信号进行次方运算以获取第二光电容积脉搏波信号。
由于获得的差分信号d(n)仍然存在部分噪声，为进一步减少噪声干扰，对差分信号d(n)进行次方运算得到第二光电容积脉搏波信号dd(n)。
dd(n)＝{d(n)} k
需要说明的是，幂指数k与原始光电容积脉搏波信号的信噪比有关。
请参阅图5，为对差分信号进行次方运算的效果图。可以看出，随着幂指数k的增加，噪声逐渐为抑制，且特征点在信号中的位置越加凸显。
从而，通过对原始光电容积脉搏波信号进行预处理，能够有效抑制原始光电容积脉搏波信号中的噪声干扰，使得后续基于取第一光电容积脉搏波信号及第二光电容积脉搏波信号得到的检测结果更加精确。
S302，根据第一光电容积脉搏波信号计算原始光电容积脉搏波信号的基频值。
其中，原始光电容积脉搏波信号的基频值可以反应脉搏的脉动频率以及 心跳频率。
具体地，对第一光电容积脉搏波信号进行快速傅里叶变换，获得第一光电容积脉搏波信号的信号频率分布情况，然后将预设频率范围内具有最大幅值的频率值作为基频值。
根据心跳频率的先验知识可知，人类的心跳频率在30次/min～300次/min之间，也即在0.5Hz～5Hz之间，因此，在一种可选的实施方式中，预设频率范围为0.5Hz～5Hz。从而，将0.5Hz～5Hz内具有最大幅值的频率值作为基频值。
需要说明的是，在一种可选的实施方式中，在执行完S3011之后可直接执行S302，无需等到执行完S3012、S3013后才执行S302。
如图6所示，为基于第一光电容积脉搏波信号的信号频率分布图。可以看出在频率为1.27Hz的点具有最大的幅值，因此，基频值f m＝1.27Hz。
S303，利用基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号。
S3031，根据原始光电容积脉搏波信号的采样频率及基频值计算信号分段长度。
具体地，通过算式
Figure PCTCN2019123252-appb-000003
计算信号分段长度。其中，f s为采样频率，f m为基频值，α为预设定的常数，且0.9＜α≤1。
S3032，根据信号分段长度，对第二光电容积脉搏波信号进行分段幅值归一化处理以获得归一化信号。
根据S3032获得的归一化信号s(n)为：
Figure PCTCN2019123252-appb-000004
其中，s i(j)为s(n)第i段的第j个数据，dd i(j)为dd(n)第i段的第j个数据，且1≤i≤len/M，(i-1)·M+1≤j≤i·M，len指dd(n)的长度，min i及max i分别为dd(n)第i段的最小值和最大值。
S304，根据归一化信号及第一光电容积脉搏波信号确定原始光电容积脉搏波信号的特征点。
请继续参阅图4，S304包括：
S3041，根据归一化信号确定多个特征点参考位置，其中，每个特征点参考位置对应的幅值均为1，且相邻两个特征点参考位置之间的间隔大于或等于第一阈值。
其中，每个特征点参考位置对应的幅值均为1，且相邻两个特征点参考位置之间的间隔大于或等于第一阈值。
可以理解地，人体反应存在不应期，也即两个相邻的PPG信号峰值之间的必然大于不应期持续的时间，一般地，该不应期持续的时间为200ms，因 此，与之对应地，第一阈值为200ms。
在一种可选的实施方式中，可以首先确定归一化信号中所有幅值为1的点，再以第一个幅值为1的点为第一个特征点参考位置剔除多检点，多检点即为虽然幅值为1但与上一个特征点参考位置之间的距离小于第一阈值的点，从而确定所有特征点参考位置。
还需要说明的是，特征点参考位置实际对应于PPG信号的上升沿位置。
S3042，以第一光电容积脉搏波信号为基准，将位于以特征点参考位置确定的时域范围内、且位于特征点参考位置左边的第一个过零点位置作为波谷位置，将位于以特征点参考位置确定的时域范围内、且位于特征点参考位置右边的第一个极大值点位置作为波峰位置。
需要说明的是，以特征点参考位置确定的时域范围与PPG信号的频率范围有关。确定波谷位置及波峰位置的过程，即为以每个特征点参考位置作为一个PPG信号的上升沿位置，将其特征点参考位置映射至第一光电容积脉搏波信号找到该PPG信号的波谷位置以及波峰位置的过程。
请参阅图7，为使用光电容积脉搏波信号特征点检测方法过程中产生的各信号的波形图。其中，(a)为原始光电容积脉搏波信号x(n)；(b)为低通滤波后的第一光电容积脉搏波信号f(n)；(c)为差分信号d(n)；(d)为进行次方运算后的第二光电容积脉搏波信号dd(n)；(e)为归一化信号s(n)；(f)为第一光电容积脉搏波信号f(n)中波谷和波峰位置检测效果。
可以看出，经由低通滤波、差分运算、次方运算以及分段幅值归一化处理后，每个周期的PPG信号在整个归一化信号s(n)中得到了有效增强和凸显，因此通过检测归一化信号s(n)的特征点参考位置，便能有效检测第一光电容积脉搏波信号f(n)中的波峰和波谷位置。
请参阅图8，为原始光电容积脉搏波信号形态畸变较大时，使用本发明提供的光电容积脉搏波信号特征点检测方法得到的检测结果。其中，(a)为第一光电容积脉搏波信号f(n)中波谷和波峰位置定位示意图；(b)为归一化信号s(n)的特征点参考位置定位示意图。
从图8可以看出，利用本发明提供的光电容积脉搏波信号特征点检测方法，即使在原始光电容积脉搏波信号形态畸变较大时，仍然能得到较为理想的检测结果。
为了执行上述实施例及各个可能的方式中的相应步骤，下面给出一种光电容积脉搏波信号特征点检测装置200的实现方式，可选地，该光电容积脉搏波信号特征点检测装置200可以采用上述图2所示的人体参数检测设备100的器件结构。进一步地，请参阅图9，图9为本发明实施例提供的一种光电容积脉搏波信号特征点检测装置200的功能模块图。需要说明的是，本实施例所提供的光电容积脉搏波信号特征点检测装置200，其基本原理及产生的技术效果和上述实施例相同，为简要描述，本实施例部分未提及之处，可参考上述的实施例中相应内容。该光电容积脉搏波信号特征点检测装置200包括预处理单元210、计算单元220、归一化单元230以及特征点确定单元240。
其中，预处理单元210用于对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号。
具体地，预处理单元210用于对原始光电容积脉搏波信号进行低通滤波操作以获取第一光电容积脉搏波信号，并对第一光电容积脉搏波信号进行差分运算以获得差分信号，然后对差分信号进行次方运算以获取第二光电容积脉搏波信号。
可以理解地，在一种可选的实施方式中，预处理单元210可用于执行S301、S3011、S3012以及S3013。
计算单元220用于根据第一光电容积脉搏波信号计算原始光电容积脉搏波信号的基频值。
可以理解地，在一种可选的实施方式中，计算单元220可用于执行S302。
归一化单元230用于利用基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号。
具体地，归一化单元230用于根据原始光电容积脉搏波信号的采样频率及基频值计算信号分段长度，并根据信号分段长度，对第二光电容积脉搏波信号进行分段幅值归一化处理以获得归一化信号。
可以理解地，在一种可选的实施方式中，归一化单元230可用于执行S303、S3031及S3032。
特征点确定单元240用于根据归一化信号及第一光电容积脉搏波信号确定原始光电容积脉搏波信号的特征点。
具体地，特征点确定单元240用于根据归一化信号确定多个特征点参考位置，其中，每个特征点参考位置对应的幅值均为1，且相邻两个特征点参考位置之间的间隔大于或等于第一阈值，并以第一光电容积脉搏波信号为基准，将位于以特征点参考位置确定的时域范围内、且位于特征点参考位置左边的第一个过零点位置作为波谷位置，将位于以特征点参考位置确定的时域范围内、且位于特征点参考位置右边的第一个极大值点位置作为波峰位置。
可以理解地，在一种可选的实施方式中，归一化单元230可用于执行S304、S3041及S3042。
可选地，上述模块可以软件或固件(Firmware)的形式存储于图2所示的存储器110中或固化于该用户设备的操作系统(Operating System，OS)中，并可由图2中的处理器120执行。同时，执行上述模块所需的数据、程序的代码等可以存储在存储器110中。
综上所述，本发明提供的光电容积脉搏波信号特征点检测方法及装置，通过对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号，根据第一光电容积脉搏波信号计算原始光电容积脉搏波信号的基频值，然后利用基频值对第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号，从而根据归一化信号确定原始光电容积脉搏波信号的特征点。通过基频值对第二光电容积脉搏波信号进行分段 幅值归一化处理，以增强和凸显原始光电容积脉搏波信号在归一化信号中的位置，提高了原始光电容积脉搏波信号波谷和波峰特征检测的准确度。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，也可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，附图中的流程图和框图显示了根据本发明的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本发明各个实施例中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质 中，包括若干指令用以使得一台计算机设备(可以是个人计算机，人体参数检测设备，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种光电容积脉搏波信号特征点检测方法，其特征在于，所述光电容积脉搏波信号特征点检测方法包括：
    对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号；
    根据所述第一光电容积脉搏波信号计算所述原始光电容积脉搏波信号的基频值；
    利用所述基频值对所述第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号；
    根据所述归一化信号及所述第一光电容积脉搏波信号确定所述原始光电容积脉搏波信号的特征点。
  2. 根据权利要求1所述的光电容积脉搏波信号特征点检测方法，其特征在于，所述利用所述基频值对所述第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号的步骤包括：
    根据所述原始光电容积脉搏波信号的采样频率及所述基频值计算信号分段长度；
    根据所述信号分段长度，对所述第二光电容积脉搏波信号进行分段幅值归一化处理以获得所述归一化信号。
  3. 根据权利要求2所述的光电容积脉搏波信号特征点检测方法，其特征在于，所述根据所述原始光电容积脉搏波信号的采样频率及所述基频值计算信号分段长度的步骤包括：
    通过算式
    Figure PCTCN2019123252-appb-100001
    计算所述信号分段长度；其中，f s为采样频率，f m为基频值，α为预设定的常数。
  4. 根据权利要求1所述的光电容积脉搏波信号特征点检测方法，其特征在于，所述对所述原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号的步骤包括：
    对所述原始光电容积脉搏波信号进行低通滤波操作以获取所述第一光电容积脉搏波信号；
    对所述第一光电容积脉搏波信号进行差分运算以获得差分信号；
    对所述差分信号进行次方运算以获取所述第二光电容积脉搏波信号。
  5. 根据权利要求1所述的光电容积脉搏波信号特征点检测方法，其特征在于，所述特征点包括波峰及波谷，所述根据所述归一化信号及所述第一光电容积脉搏波信号确定所述原始光电容积脉搏波信号的特征点的步骤包括：
    根据所述归一化信号确定多个特征点参考位置，其中，每个所述特征点参考位置对应的幅值均为1，且相邻两个所述特征点参考位置之间的间隔大于或等于第一阈值；
    以所述第一光电容积脉搏波信号为基准，将位于以所述特征点参考位置确定的时域范围内、且位于所述特征点参考位置左边的第一个过零点位置作为波谷位置，将位于以所述特征点参考位置确定的时域范围内、且位于所述特征点参考位置右边的第一个极大值点位置作为波峰位置。
  6. 一种光电容积脉搏波信号特征点检测装置，其特征在于，所述光电容积脉搏波信号特征点检测装置包括：
    预处理单元，用于对原始光电容积脉搏波信号进行预处理以获取第一光电容积脉搏波信号及第二光电容积脉搏波信号；
    计算单元，用于根据所述第一光电容积脉搏波信号计算所述原始光电容积脉搏波信号的基频值；
    归一化单元，用于利用所述基频值对所述第二光电容积脉搏波信号进行分段幅值归一化处理，获得归一化信号；
    特征点确定单元，用于根据所述归一化信号及所述第一光电容积脉搏波信号确定所述原始光电容积脉搏波信号的特征点。
  7. 根据权利要求6所述的光电容积脉搏波信号特征点检测装置，其特征在于，所述归一化单元用于根据所述原始光电容积脉搏波信号的采样频率及所述基频值计算信号分段长度；
    所述归一化单元用于根据所述信号分段长度，对所述第二光电容积脉搏波信号进行分段幅值归一化处理以获得所述归一化信号。
  8. 根据权利要求7所述的光电容积脉搏波信号特征点检测装置，其特征在于，所述归一化单元用于通过算式
    Figure PCTCN2019123252-appb-100002
    计算所述信号分段长度；其中，f s为采样频率，f m为基频值，α为预设定的常数。
  9. 根据权利要求6所述的光电容积脉搏波信号特征点检测装置，其特征在于，所述预处理单元用于对所述原始光电容积脉搏波信号进行低通滤波操作以获取所述第一光电容积脉搏波信号；
    所述预处理单元用于对所述第一光电容积脉搏波信号进行差分运算以获得差分信号；
    所述预处理单元用于对所述差分信号进行次方运算以获取所述第二光电容积脉搏波信号。
  10. 根据权利要求6所述的光电容积脉搏波信号特征点检测装置，其特征在于，所述特征点包括波峰及波谷，所述特征点确定单元用于根据所述归一化信号确定多个特征点参考位置，其中，每个所述特征点参考位置对应的幅值均为1，且相邻两个所述特征点参考位置之间的间隔大于或等于第一阈值；
    所述特征点确定单元用于以所述第一光电容积脉搏波信号为基准，将位于以所述特征点参考位置确定的时域范围内、且位于所述特征点参考位置左边的第一个过零点位置作为波谷位置，将位于以所述特征点参考位置确定的时域范围内、且位于所述特征点参考位置右边的第一个极大值点位置作为波峰位置。
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