WO2020125494A1 - 一种生物信号的分析处理方法、装置及设备 - Google Patents

一种生物信号的分析处理方法、装置及设备 Download PDF

Info

Publication number
WO2020125494A1
WO2020125494A1 PCT/CN2019/124333 CN2019124333W WO2020125494A1 WO 2020125494 A1 WO2020125494 A1 WO 2020125494A1 CN 2019124333 W CN2019124333 W CN 2019124333W WO 2020125494 A1 WO2020125494 A1 WO 2020125494A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
effective component
biological signal
bits
component
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2019/124333
Other languages
English (en)
French (fr)
Inventor
郭海光
程俊
高向阳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Institute of Advanced Technology of CAS
Original Assignee
Shenzhen Institute of Advanced Technology of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Institute of Advanced Technology of CAS filed Critical Shenzhen Institute of Advanced Technology of CAS
Publication of WO2020125494A1 publication Critical patent/WO2020125494A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • 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 application belongs to the field of signal processing, and particularly relates to a method, device and device for analyzing and processing biological signals used in a low-power wearable device.
  • the embodiments of the present application provide a method, device, and equipment for analyzing and processing biological signals, to solve the problem that the analysis and processing algorithms in the prior art are generally complicated and low-power processors cannot be used to process biological signals.
  • a first aspect of the embodiments of the present application provides a method for analyzing and processing biological signals.
  • the efficient data analyzing and processing method includes:
  • the parameter value of the biological signal is calculated according to the slope of the filtered data.
  • the determination of the cumulative data of the previous sampling based on the cumulative data and the sampled data, extracts the data by a fixed-point integer method with a predetermined number of digits
  • the steps of describing the effective component in the sampled data include:
  • N1 and N2 are natural numbers, and N1 is greater than N2;
  • it also includes determining the AC component according to the difference between the current sampled data and the DC component.
  • the sampling data collected by the sensor is 16 bits
  • the preset number of bits is 32 bits
  • the N1 is 15, and the N2 is 4.
  • the digital filter parameter is determined according to the signal collected by the sensor, and a convolution operation is performed on the discrete effective component and the determined digital filter,
  • the step of filtering the effective component includes:
  • the initial index i, j is 0, the filter order N, the filter parameters are stored in the array f, where i is the index of the array f, j is the index of the array g;
  • step S4 after j+1, find the remainder of the filter order N, assign the result of the remainder to j, and cyclically shift the array f forward.
  • new effective component data is obtained, return to step S2.
  • the step of calculating the parameter value of the biological signal according to the slope of the filtered data includes:
  • the change period or frequency of the characteristic signal is calculated according to the change points of the positive and negative values of the slopes of two adjacent points.
  • the step of acquiring sampling data of a biological signal through a sensor includes:
  • the analog signal collected by the analog sensor is filtered by a noise filter circuit and/or the pre-processed signal after the amplification process of the amplification circuit, and the analog-to-digital conversion circuit periodically according to the control instruction of the single chip microcomputer Convert the preprocessed signal into a digital signal.
  • a second aspect of the embodiments of the present application provides a biological signal analysis and processing device.
  • the efficient data analysis and processing device includes:
  • the sampling unit is used to obtain the sampling data of the biological signal through the sensor
  • An effective component extraction unit used to determine the accumulated data of the last sampling, and extracting the effective component in the sampled data by a fixed-point integer method of a predetermined number of digits according to the accumulated data and the sampled data;
  • the effective component filtering unit is used to determine the digital filter parameters according to the signal collected by the sensor, and filter the effective component by performing a convolution operation on the discrete effective component and the determined digital filter;
  • the parameter value calculation unit is configured to calculate the parameter value of the biological signal according to the slope of the filtered data.
  • the effective component extraction unit includes:
  • the first shift subunit is used to shift the current sampled data to the left by N1 bits to obtain the first shifted data
  • a second shift subunit configured to subtract the previous sampled accumulated data from the first shifted data, and shift the obtained difference to the right by N2 bits to obtain second shifted data;
  • the third shifting subunit is used to add the second shifted data to the last sampled accumulated data and shift right by N1 bits to obtain the DC component of the current sample, where N1 and N2 are natural numbers, and N1 is greater than N2;
  • AC component determination subunit which is used to determine the AC component according to the difference between the current sampling data and the DC component.
  • a third aspect of the embodiments of the present application provides a biological signal analysis and processing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing When the computer program is described, the steps of the biological signal analysis processing method according to any one of the first aspect are realized.
  • a fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the biological program according to any one of the first aspects is implemented Signal analysis and processing method steps.
  • the embodiment of the present application has the following beneficial effects: after acquiring the sampling data of the biological signal, the effective component in the sampling data is extracted by a fixed-point integer method of a predetermined number of bits, and then the discrete effective component is The determined filter is used for convolution operation without floating point operation, and the hardware system has low requirements, so that the low-power processor can be used to complete the analysis and processing of the biological signal, which is beneficial to reduce the hardware cost and reduce the hardware complexity.
  • FIG. 1 is a schematic flowchart of an implementation of a method for analyzing and processing a biological signal according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of raw sampling data provided by an embodiment of the present application.
  • FIG. 3 is a schematic diagram of an implementation process of extracting effective components from original sampled data provided by an embodiment of the present application
  • FIG. 4 is a schematic diagram of a DC component extracted from original sampled data provided by an embodiment of the present application.
  • FIG. 5 is a schematic diagram of an implementation process of filtering an effective component provided by an embodiment of the present application.
  • FIG. 6 is a schematic diagram of filtered sampling data provided by an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a biological signal analysis and processing device provided by an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a biological signal analysis and processing device provided by an embodiment of the present application.
  • FIG. 1 is a schematic diagram of an implementation process of a method for analyzing and processing a biological signal provided by an embodiment of the present application, and the details are as follows:
  • step S101 sampling data of a biological signal is acquired through a sensor
  • the main purpose of the biological signal analysis and processing method described in the embodiments of the present application is to enable a low-power chip, such as a single-chip microcomputer, to also complete the biological signal analysis and processing. Therefore, the following uses the single-chip microcomputer as an example.
  • the sampled data is analyzed.
  • the single-chip computer is used as the main control unit to coordinate the work of each functional module.
  • conventional sensors for measuring human biological signals can be divided into analog and digital.
  • Digital sensors can be directly connected to the single-chip computer through conventional communication protocols (such as I2C, SPI, etc.); the output of analog sensors generally needs to be connected to a
  • the signal processing unit completes functions such as noise filtering and amplification of the signal, and then enters the analog-to-digital conversion unit, and the analog-to-digital conversion unit can be controlled by the single-chip computer to periodically complete the signal conversion work, and the conversion result is fed back to the single-chip computer.
  • the single-chip microcomputer can output human-computer interaction information, such as displaying related information or prompting the current status; and can save the collected data and analysis results locally.
  • related data can also be sent to the cloud server through the communication module.
  • the analysis and processing of biological signals can generally be divided into two cases.
  • the data is directly displayed on the Cartesian coordinate system with the time as the horizontal axis on the screen (such as an electrocardiogram);
  • the laws such as period, amplitude, etc.
  • the analyzed data such as pulse, blood oxygen, etc.
  • these two kinds of data can be saved to internal storage or uploaded to the cloud server for later professionals to analyze.
  • This application mainly analyzes and deals with the second situation. Since the human body is a very complicated system, there will still be a lot of interference information after pre-processing of the sensor data. Before input into the single-chip microcomputer for analysis and calculation, pre-processing is also needed to reduce the amount of calculation and improve real-time performance.
  • step S102 the accumulated data of the last sampling is determined, and according to the accumulated data and the sampled data, the effective component in the sampled data is extracted by a fixed-point integer method of a predetermined number of bits;
  • the signals generally obtained can be divided into DC signals and AC signals.
  • the biological signal is a heart rate sampling signal, it is only necessary to care about the change in the signal, that is, the AC component, and extracting the AC signal therein becomes our main purpose.
  • a variety of methods can be used to extract the AC component. Considering that the microcontroller system we adopt generally does not have a floating-point processing unit, an integer-based AC component extraction method is proposed here.
  • FIG. 2 it is a schematic diagram of heart rate sampling data provided by an embodiment of the present application.
  • non-invasive heart rate measurement that can be used for wearable devices generally uses a light volume measurement method, and its principle is that when light passes through When the skin tissue is then reflected to the photosensitive sensor, the light has a certain attenuation.
  • the absorption of light like muscles, bones, veins and other connecting tissues is basically unchanged.
  • blood is different, because there is blood flow in the arteries, so the absorption of light naturally changes.
  • Figure 2 is measured using the infrared 880nm light through the tip of the fingertip.
  • the new sampled data is X and the accumulated data is P (both data are signed integers, where the accumulated data is the sum of the sampled data of the predetermined number of times before the current sampled data Value, the sum value can also be a weighted sum value, for example, the closer to the current time, the higher the weight value), the specific extraction method can be shown in Figure 3, including:
  • step S301 shift the current sampled data to the left by N1 bits to obtain the first shifted data
  • the predetermined number of bits of the fixed-point integer may be 32 bits, then the N1 bit of the current sampled data may be shifted to the left by 15 bits (of course not Limited to this, it may also be 14 bits, etc.), that is, multiply the current data by the power of 15 to obtain the first shifted data after the multiplication.
  • step S302 subtract the accumulated data of the previous sampling from the first shifted data, and shift the obtained difference to the right by N2 bits to obtain second shifted data;
  • the obtained difference is shifted to the right by N2 bits.
  • the predetermined number of bits is 32 bits and N1 is 15, the right shift of N2 bits may be a right shift 4.
  • step S303 the second shifted data is added to the last sampled accumulated data and shifted to the right by N1 bits to obtain the DC component of the current sample, where N1 and N2 are natural numbers, and N1 is greater than N2;
  • the right shifted data is added to the last sampled accumulated data and right shifted by N1 bits.
  • the predetermined number of bits is 32
  • the right shifted data is compared with the last sampled accumulated data After the addition, it is shifted right by 15 bits to obtain the DC component of the current sample.
  • step S304 or further includes determining the AC component according to the difference between the current sampling data and the DC component.
  • the difference between the current sampled data and the DC component can be obtained to obtain the AC component in the sampled data.
  • step S103 the digital filter parameters are determined according to the signal collected by the sensor, and the effective component is filtered by performing a convolution operation on the discrete effective component and the determined digital filter;
  • the collected biological signals are generally more complicated.
  • the AC data of the arterial blood flow change signal obtained by the optical volume method is generally formed by superimposing waveforms of multiple frequencies. Bringing this data directly into the low-power single-chip operation will greatly increase the complexity of the operation. We have to filter the signal to extract the most important signal.
  • the number of heartbeats per minute in a normal person in a quiet state is 60 to 100. Individual differences can occur due to age, gender or other physiological factors. Generally speaking, the younger the age, the faster the heart rate. Older people have a slower heart rate than younger people. Females have a faster heart rate than men of the same age. Athletes have a slower heart rate than ordinary people. These are normal physiological phenomena. When the heart rate is more than 100 or less than 60 times per minute in a quiet state, it can be considered abnormal, and a detailed examination should be carried out as soon as possible to treat the cause. In response to this feature, we can design a low-pass filter to filter out signals with frequencies greater than 5 Hz. There are many methods for designing this filter. This application uses a relatively simple way to achieve this, using the numerical analysis software Matlab's fdatool (filter analysis design tool) toolbox.
  • the sensor sampling frequency is 400 Hz
  • the filter cut-off frequency is 5 Hz, using a fixed-point method.
  • the filter parameter f is obtained.
  • this application can preferentially use a finite impulse response digital filter, which is a non-recursive filter, whose current output signal value is only the same as the current input signal value and N-1 previous input signal values Related, not related to output, it has the characteristics of fast operation speed and small operation error, which is very suitable for single-chip system.
  • the process of the signal passing through the filter is actually the relationship of multiplication of frequency domain characteristics.
  • the filter response coefficient table can be obtained through software simulation, and the time sampling value of the input signal is known in the actual system, so it can be used. Discrete signal linear convolution operation to complete the entire filtering process.
  • the signal Since the signal is obtained by sampling at a fixed time interval through the digital-to-analog conversion unit in the single-chip system, the signal is discrete in amplitude and time.
  • the discrete definition of the above formula is as follows:
  • the step of filtering the effective component may include:
  • step S501 initialize the index i, j is 0, the filter order N, the filter parameters are stored in the array f, where i is the index of the array f, j is the index of the array g;
  • the filter parameters can be obtained, and the obtained filter parameters are stored in the array f.
  • g is the effective component data of the discrete sampled signal.
  • step S502 the effective component data is acquired and stored in the array g, and the current index of the stored data is recorded as j;
  • the effective component data is updated in real time during the sampling process, and its subscript j is also updated according to the corresponding process in the filtering process.
  • j 0.
  • step S503 the index data corresponding to the array f and the array g are multiplied and accumulated to obtain filtered data;
  • the filter parameter array f corresponds to the index of the effective component array g, which means that the closer the elements in the effective component array are, the newer the corresponding filter parameters.
  • step S504 find the remainder after j+1 and the filter order N, assign the result of the remainder to j, and cyclically shift the array f forward.
  • new effective component data is obtained, return to step S2 .
  • step S502 is performed again to perform new filter calculation.
  • the sampling described in FIG. 2 can obtain the AC signal shown in FIG. 6 after extracting the effective component and filtering.
  • step S104 the parameter value of the biological signal is calculated according to the slope of the filtered data.
  • the heart rate value per minute can be determined according to the heartbeat cycle, that is, the Multiply the reciprocal of the heartbeat cycle by 60.
  • fixed-point arithmetic is used throughout, without recursive calls, and the calculation time is fixed. It can be completed by low-power microcontrollers and other processors.
  • FIG. 7 is a schematic structural diagram of an apparatus for analyzing and processing biological signals according to an embodiment of the present application.
  • the biological signal analysis and processing device includes:
  • the sampling unit 701 is used to acquire the sampling data of the biological signal through the sensor;
  • the effective component extracting unit 702 is used to determine the accumulated data of the last sampling, and extract the effective components of the sampled data in a fixed-point integer manner with a predetermined number of bits according to the accumulated data and the sampled data;
  • the effective component filtering unit 703 is used to determine the digital filter parameters according to the signal collected by the sensor, and perform a convolution operation on the discrete effective component and the determined digital filter to filter the effective component;
  • the parameter value calculation unit 704 is configured to calculate the parameter value of the biological signal according to the slope of the filtered data.
  • the effective component extraction unit includes:
  • the first shift subunit is used to shift the current sampled data to the left by N1 bits to obtain the first shifted data
  • a second shift subunit configured to subtract the last sampled accumulated data from the first shifted data, and shift the obtained difference to the right by N2 bits to obtain second shifted data;
  • the third shifting subunit is used to add the second shifted data to the last sampled accumulated data and shift right by N1 bits to obtain the DC component of the current sample, where N1 and N2 are natural numbers, and N1 is greater than N2;
  • AC component determination sub-unit for determining the AC component according to the difference between the current sampled data and the DC component.
  • the biological signal analysis and processing device shown in FIG. 7 corresponds to the biological signal analysis and processing method shown in FIG. 1.
  • the biological signal analysis and processing device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80, for example, biological Signal analysis and processing program.
  • the processor 80 executes the computer program 82, the steps in the above embodiments of the analysis and processing method of each biological signal are implemented.
  • the processor 80 executes the computer program 82, the functions of the modules/units in the foregoing device embodiments are realized.
  • the computer program 82 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 81 and executed by the processor 80 to complete This application.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 82 in the biological signal analysis and processing device 8.
  • the computer program 82 may be divided into:
  • the sampling unit is used to obtain the sampling data of the biological signal through the sensor
  • An effective component extracting unit used to determine the accumulated data of the last sampling, and extracting the effective component in the sampled data according to the accumulated data and the sampled data by a fixed-point integer method with a predetermined number of bits;
  • the effective component filtering unit is used to determine the digital filter parameters according to the signal collected by the sensor, and filter the effective component by performing a convolution operation on the discrete effective component and the determined digital filter;
  • the parameter value calculation unit is configured to calculate the parameter value of the biological signal according to the slope of the filtered data.
  • the biological signal analysis and processing device 8 may be a computing device such as a desktop computer, a notebook, a palmtop computer and a cloud server.
  • the biological signal analysis and processing device may include, but is not limited to, the processor 80 and the memory 81.
  • FIG. 8 is only an example of the biological signal analysis and processing device 8 and does not constitute a limitation on the biological signal analysis and processing device 8, and may include more or fewer components than the illustration, or a combination
  • Certain components, or different components, for example, the biological signal analysis and processing device may further include an input and output device, a network access device, a bus, and the like.
  • the so-called processor 80 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 81 may be an internal storage unit of the biological signal analysis and processing device 8, such as a hard disk or a memory of the biological signal analysis and processing device 8.
  • the memory 81 may also be an external storage device of the biological signal analysis and processing device 8, for example, a plug-in hard disk equipped on the biological signal analysis and processing device 8, a smart memory card (Smart Media Card, SMC), Secure Digital (SD) cards, Flash cards, etc.
  • the memory 81 may also include both an internal storage unit of the biological signal analysis processing device 8 and an external storage device.
  • the memory 81 is used to store the computer program and other programs and data required by the biological signal analysis and processing device.
  • the memory 81 can also be used to temporarily store data that has been or will be output.
  • each functional unit and module is used as an example for illustration.
  • the above-mentioned functions may be allocated by different functional units
  • Module completion means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.
  • the functional units and modules in the embodiment may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit may use hardware It can also be implemented in the form of software functional units.
  • the specific names of each functional unit and module are only for the purpose of distinguishing each other, and are not used to limit the protection scope of the present application.
  • the disclosed device/terminal device and method may be implemented in other ways.
  • the device/terminal device embodiments described above are only schematic.
  • the division of the module or unit is only a logical function division, and in actual implementation, there may be another division manner, such as multiple units Or components can be combined or integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or software function unit.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the present application can implement all or part of the processes in the methods of the above embodiments, or it can be completed by a computer program instructing related hardware.
  • the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments may be implemented.
  • the computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file, or some intermediate form, etc.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals and software distribution media, etc.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals and software distribution media, etc.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
  • Physiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Psychiatry (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

一种生物信号的分析处理方法包括：通过传感器获取生物信号的采样数据（S101）；确定上一次采样时的累计数据，根据累计数据和采样数据，通过预定位数的定点整数方式提取采样数据中的有效分量（S102）；根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对有效分量进行过滤（S103）；根据过滤后的数据的斜率计算生物信号的参数值（S104）。通过预定位数的定点整数的方式，提取采样数据中的有效分量，然后通过离散的有效分量与所确定的滤波器进行卷积运算，不需要通过浮点运算，对硬件系统要求低，从而可以使用低功耗处理器完成对生物信号的分析处理，有利于降低硬件成本，减小硬件复杂度。

Description

一种生物信号的分析处理方法、装置及设备 技术领域
本申请属于信号处理领域，尤其涉及一种应用于低功耗穿戴式设备中的生物信号的分析处理方法、装置及设备。
背景技术
在运动医学研究、临床诊断领域经常需要长期监测人体的多种生物信号。在传统的人体生物信号采集设备中，附着于人体的测量端只负责模拟信号的采集，数据处理交由运算能力强大的上位机处理，仪器一般较大不方便随身携带，无法实现随时监控。
近年来，随着智能穿戴技术和人体生物传感器的发展，使得越来越多的人体生物信号可以通过小型穿戴设备采集，同时可采集的信号量也越来越多。信号的处理需要在设备端完成，由穿戴设备中的单片机进行边缘计算。而由于这类设备一般需要长时间工作，通常会选用低功耗处理器，比如单片机，它们运算能力一般比较有限，而目前的数据分析处理算法一般需要较复杂的运算，因而不能由低功耗处理器直接对采集的数据进行有效的分析处理。
技术问题
有鉴于此，本申请实施例提供了一种生物信号的分析处理方法、装置及设备，以解决现有技术中的分析处理算法一般较为复杂，无法选用低功耗处理器处理生物信号的问题。
技术解决方案
本申请实施例的第一方面提供了一种生物信号的分析处理方法，所述高效的数据分析处理方法包括：
通过传感器获取生物信号的采样数据；
确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量；
根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与 所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
根据过滤后的数据的斜率计算所述生物信号的参数值。
结合第一方面，在第一方面的第一种可能实现方式中，所述确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量的步骤包括：
将当前的采样数据左移N1位，得到第一移位数据；
由所述第一移位数据减去上一次采样的累计数据，并对得到的差值右移N2位，得到第二移位数据；
将所述第二移位数据与上一次采样的累计数据相加后并右移N1位，得到当前采样的直流分量，其中N1、N2为自然数，且N1大于N2；
或者还包括根据当前的采样数据和直流分量的差值确定交流分量。
结合第一方面，在第一方面的第二种可能实现方式中，传感器采集的采样数据为16位，所述预设位数为32位，所述N1为15，所述N2为4。
结合第一方面，在第一方面的第三种可能实现方式中，所述根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤的步骤包括：
S1，初始化索引i，j为0，滤波器阶数N，将滤波器参数存入数组f，其中i为数组f的索引，j为数组g的索引；
S2，获取有效分量数据存入数组g，并记录存入的数据的当前索引为j；
S3，将数组f与数组g相应的索引数据相乘后累加，得到过滤后的数据；
S4，将j+1后与滤波器阶数N求余，将求余结果赋值为j，并将数组f循环向前移位，当获取到新的有效分量数据时，返回步骤S2。
结合第一方面，在第一方面的第四种可能实现方式中，所述根据过滤后的 数据的斜率计算所述生物信号的参数值的步骤包括：
根据过滤后的数据生成数据曲线，计算当前数据在所述数据曲线上的斜率；
根据相邻两点的斜率正负值变化点计算所述特征信号的变化周期或频率。
结合第一方面，在第一方面的第五种可能实现方式中，所述通过传感器获取生物信号的采样数据的步骤包括：
当所述传感器为模拟传感器时，所述模拟传感器采集的模拟信号通过噪声过滤电路过滤处理和/或放大电路的放大处理后的预处理信号，由模数转换电路根据单片机的控制指令，周期性的将所述预处理信号转换为数字信号。
本申请实施例的第二方面提供了一种生物信号的分析处理装置，所述高效的数据分析处理装置包括：
采样单元，用于通过传感器获取生物信号的采样数据；
有效分量提取单元，用于确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量；
有效分量过滤单元，用于根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
参数值计算单元，用于根据过滤后的数据的斜率计算所述生物信号的参数值。
结合第二方面，在第二方面的第一种可能实现方式中，所述有效分量提取单元包括：
第一移位子单元，用于将当前的采样数据左移N1位，得到第一移位数据；
第二移位子单元，用于由所述第一移位数据减去上一次采样的累计数据，并对得到的差值右移N2位，得到第二移位数据；
第三移位子单元，用于将所述第二移位数据与上一次采样的累计数据相加后并右移N1位，得到当前采样的直流分量，其中N1、N2为自然数，且N1大于N2；
或者还包括交流分量确定子单元，用于根据当前的采样数据和直流分量的差值确定交流分量。
本申请实施例的第三方面提供了一种生物信号的分析处理设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如第一方面任一项所述生物信号的分析处理方法的步骤。
本申请实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面任一项所述生物信号的分析处理方法的步骤。
有益效果
本申请实施例与现有技术相比存在的有益效果是：获取生物信号的采样数据后，通过预定位数的定点整数的方式，提取采样数据中的有效分量，然后通过离散的有效分量与所确定的滤波器进行卷积运算，不需要通过浮点运算，对硬件系统要求低，从而可以使用低功耗处理器完成对生物信号的分析处理，有利于降低硬件成本，减小硬件复杂度。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅 仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种生物信号的分析处理方法的实现流程示意图；
图2是本申请实施例提供的原始采样数据示意图；
图3是本申请实施例提供的一种对原始采样数据进行有效分量提取的实现流程示意图；
图4是本申请实施例提供的对原始采样数据提取的直流分量示意图；
图5是本申请实施例提供的一种对有效分量进行滤波的实现流程示意图；
图6是本申请实施例提供的采样数据滤波后的示意图；
图7是本申请实施例提供的一种生物信号的分析处理装置的结构示意图；
图8是本申请实施例提供的生物信号的分析处理设备的示意图。
本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
图1为本申请实施例提供的一种生物信号的分析处理方法的实现流程示意图，详述如下：
在步骤S101中，通过传感器获取生物信号的采样数据；
具体的，本申请实施例中所述生物信号的分析处理方法的主要是目的是可 以使得低功耗的芯片，比如单片机也可以完成对生物信号的分析处理，因此，下面以单片机为例，对采样的数据进行分析。
一般的，单片机作为主控单元协调各功能模块工作。一般常规人体生物信号测量用传感器可分为模拟与数字式两种，数字式传感器可以直接通过常规的通讯协议(如I2C、SPI等)与单片机连接；模拟式的传感器的输出端一般需要连接一个信号处理单元，完成信号的噪声过滤、放大等功能，然后进入模数转换单元，而模数转换单元可以由单片机控制其周期性的完成信号的转换工作，并把转换结果反馈回单片机。同时，单片机可以输出人机交互信息，如显示相关信息或提示当前状态；并且可以把采集的数据、分析的结果保存在本地。另外，如有需求，还可以把相关数据通过通讯模块上发到云服务器。
对生物信号的分析处理，一般可以分为两种情况。第一种情况，数据直接通过屏幕以时间为横轴的笛卡尔坐标系方式显示出来(如心电图)；第二种情况，数据采集后需要分析出其中规律(如周期、幅值等)，并且显示分析后的数据(如脉搏、血氧等)。同时这两种数据可以保存到内部存储器或上传到云端服务器，以供后期专业人员分析。
本申请主要针对第二种情况进行分析处理。由于人体是一个十分复杂的系统，传感器数据经过前期处理后一般仍会存在大量干扰信息，在投入到单片机分析运算前还需要预处理，以降低运算量，提高实时性。
在步骤S102中，确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量；
一般得到的信号就可以分为直流信号和交流信号。当所述生物信号为心率采样信号时，则只需要关心信号中的变化情况，即交流分量，提取其中的交流信号便成为我们的主要目的。一般来讲提取交流分量可以使用多种方法，考虑 到我们采用的单片机系统一般不具备浮点数处理单元，在这里提出一种基于整数的交流分量提取方法。
如图2所示为本申请实施例提供的一种心率采样数据的示意图，以当前的技术来说，可用于穿戴式设备的无创心率测量普遍采用光容积测量法，其原理是当光照透过皮肤组织然后再反射到光敏传感器时光照有一定的衰减的。当测量部位没有大幅度的运动时，像肌肉、骨骼、静脉和其他连接组织等等对光的吸收是基本不变的。但是血液不同，由于动脉里有血液的流动，那么对光的吸收自然也有所变化。当我们把光转换成电信号时，正是由于动脉对光的吸收有变化而其他组织对光的吸收基本不变，就能反应出血液流动的特点。图2即是采用红外880nm光通过指尖末梢测得。
为了得到提取采样数据中的交流分量，假设新采样数据为X，累计数据为P(两个数据均为有符号的整型数，其中累计数据为当前采样数据之前的预定次数的采样数据的和值，所述和值也可以为加权和值，比如可以采用距离当前时间越近，则权值越高)，具体提取方法可以如图3所示，包括：
在步骤S301中，将当前的采样数据左移N1位，得到第一移位数据；
可选一种实施方式中，当传感器采集的数据为16位时，所述定点整数的预定位数可以为32位，则当前采样数据左移的N1位，可以为左移15位(当然不局限于此，也可以为14位等)，即将当前数据乘以2的15次方，得到倍乘后的第一移位数据。
在步骤S302中，由所述第一移位数据减去上一次采样的累计数据，并对得到的差值右移N2位，得到第二移位数据；
第一移位数据减去上一次采样时的累计数据后，得到的差值再右移N2位，当预定位数为32位时，N1为15时，所述右移N2位可以为右移4位。
在步骤S303中，将所述第二移位数据与上一次采样的累计数据相加后并右移N1位，得到当前采样的直流分量，其中N1、N2为自然数，且N1大于N2；
将当前的采样数据倍乘后，减去上一次采样时的累计数据，再向右移N2位，使得当前采样数据与上一次采样的累计数据求和时，当前采样数据具有较大的权值，累计求平均后的计算结果更加趋于当前的采样数据。
右移N2位后，将右移后的数据与上一次采样的累计数据相加后并右移N1位，比如预定位数32位时，将右移后的数据与上一次采样的累计数据相加后，并右移15位，得到当前采样的直流分量。如图4所示，针对图2所述的采样数据，通过预定位数的定点整数方式所提取的直流分量。
在步骤S304中，或者还包括根据当前的采样数据和直流分量的差值确定交流分量。
在获取信号的直流分量后，将当前采样数据与直流分量求差，即可得到采样数据中的交流分量。
在步骤S103中，根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
所采集的生物信号一般较为复杂，比如通过光容积法获得的动脉血流变化信号的交流数据一般由多种频率的波形叠加而成。直接把该数据带入低功耗的单片机运算会使运算复杂度大大提升。我们要对该信号进行滤波处理，提取其中最主要的信号。
一般来说，正常人在安静状态下每分钟心跳的次数为60～100次。可因年龄、性别或其他生理因素产生个体差异。一般来说，年龄越小，心率越快，老 年人心跳比年轻人慢，女性的心率比同龄男性快，运动员心率较普通人慢，这些都是正常的生理现象。当安静状态下每分钟心率超过100次或少于60次都可以认为异常，应及早进行详细检查，以便针对病因进行治疗。针对这一特性，我们可以设计一个低通滤波器，过滤掉频率大于5Hz以上的信号。设计这一滤波器可以采用多种方法，本申请采用一种较简便的方式来实现，使用数值分析软件Matlab的fdatool(滤波器分析设计工具)工具箱。
设定使用低通FIR滤波，传感器采样频率400Hz，滤波截止频率5Hz，采用定点数方式。通过“Design Filter(设计滤波器)”生成滤波器，从菜单“Targets(目标)”选择“Generate C Header”生成滤波响应系数，在生成前需要设定为导出32位有符号整型，这样即获得了滤波器参数f。
当然，以上描述的是针对心率所设定的滤波器参数，针对不同的生物信号，可以获取不同的低通或者带通滤波器的参数。
考虑到需要在单片机上实现，本申请可以优先采用有限脉冲响应数字滤波器，该滤波器是非递归滤波器，它的当前输出信号值仅仅与当前输入信号值和N-1个以前的输入信号值有关，与输出无关，它具有运算速度快、运算误差小等特点，非常适合单片机系统。在频域分析上信号经过滤波器的过程实际上是频域特性相乘的关系，可以通过软件仿真获得滤波器响应系数表，而输入信号的时间采样值在实际系统中已知，所以可以采用离散信号线性卷积运算来完成整个滤波过程。
连续信号的线性卷积公式如下：
Figure PCTCN2019124333-appb-000001
由于在单片机系统中信号是通过数模转换单元以固定时间间隔采样获得，信号在幅值与时间上都是离散的，上述公式的离散定义如下：
Figure PCTCN2019124333-appb-000002
针对实际人体信号测量时，一般来讲，我们要处理的信号都是有限点数。并且我们是以数组形式来存放他们的数值的，所以脚标引用一般都不会有负值，最小起点都是0。
Figure PCTCN2019124333-appb-000003
Figure PCTCN2019124333-appb-000004
y(n)＝f 0g[n]+f 1g[n-1]+…+f Ng[n-N]         (5)
对所述有效分量进行过滤的步骤，具体可以如图5所示，包括：
在步骤S501中，初始化索引i，j为0，滤波器阶数N，将滤波器参数存入数组f，其中i为数组f的索引，j为数组g的索引；
通过滤波器参数设计后，可以得到滤波器参数，将所得到的滤波器参数存入数组f。g为离散的采样信号的有效分量数据。
在步骤S502中，获取有效分量数据存入数组g，并记录存入的数据的当前索引为j；
所述有效分量数据在采样过程中实时更新，其下标j也根据滤波处理过程中相应的更新，在初始化完成时，j＝0。
在步骤S503中，将数组f与数组g相应的索引数据相乘后累加，得到过滤后的数据；
其中，滤波参数数组f与有效分量数组g的索引相应，是指有效分量数组中的元素越近，对应的滤波参数越新。
在步骤S504中，将j+1后与滤波器阶数N求余，将求余结果赋值为j， 并将数组f循环向前移位，当获取到新的有效分量数据时，返回步骤S2。
如果没有获取到新的有效分量数据则结束处理，如果获取到新的有效分量，则将j+1与滤波器除数N求余，将求余结果赋值给变量j，并将滤波器参数的数组f循环向前移位，即下标为1的元素的值，赋值给下标为0元素，下标为0的元素的值，赋值给下标为N的元素。然后重新进行步骤S502，进行新的滤波计算。图2所述的采样，经过提取有效分量并经过滤波处理后即可得到如图6所示的交流信号。
在步骤S104中，根据过滤后的数据的斜率计算所述生物信号的参数值。
在获取处理后的数据后，每获得一个数据可以计算一下此数据在整个数据曲线中的斜率，记录每个数据在整个数据曲线中的斜率的正负变化。根据所记录的两个相邻的、均由正到负的变化时刻，或者均由负到正的变化时刻，即为一次心跳周期，根据所述心跳周期可以确定每分钟心率值，即将所述心跳周期的倒数乘以60。整个计算过程中全程使用定点数运算，无递归调用，运算时间固定，可以由低功耗的单片机等处理器完成。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
图7为本申请实施例提供的一种生物信号的分析处理装置的结构示意图，详述如下：
所述生物信号的分析处理装置包括：
采样单元701，用于通过传感器获取生物信号的采样数据；
有效分量提取单元702，用于确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的 有效分量；
有效分量过滤单元703，用于根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
参数值计算单元704，用于根据过滤后的数据的斜率计算所述生物信号的参数值。
优选的，所述有效分量提取单元包括：
第一移位子单元，用于将当前的采样数据左移N1位，得到第一移位数据；
第二移位子单元，用于由所述第一移位数据减去上一次采样的累计数据，并对得到的差值右移N2位，得到第二移位数据；
第三移位子单元，用于将所述第二移位数据与上一次采样的累计数据相加后并右移N1位，得到当前采样的直流分量，其中N1、N2为自然数，且N1大于N2；
或者还包括交流分量确定子单元，用于根据当前的采样数据和直流分量的差值确定交流分量。
图7所述生物信号的分析处理装置，与图1所述的生物信号的分析处理方法对应。
图8是本申请一实施例提供的生物信号的分析处理设备的示意图。如图8所示，该实施例的生物信号的分析处理设备8包括：处理器80、存储器81以及存储在所述存储器81中并可在所述处理器80上运行的计算机程序82，例如生物信号的分析处理程序。所述处理器80执行所述计算机程序82时实现上述各个生物信号的分析处理方法实施例中的步骤。或者，所述处理器80执行所述计算机程序82时实现上述各装置实施例中各模块/单元的功能。
示例性的，所述计算机程序82可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器81中，并由所述处理器80执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序82在所述生物信号的分析处理设备8中的执行过程。例如，所述计算机程序82可以被分割成：
采样单元，用于通过传感器获取生物信号的采样数据；
有效分量提取单元，用于确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量；
有效分量过滤单元，用于根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
参数值计算单元，用于根据过滤后的数据的斜率计算所述生物信号的参数值。
所述生物信号的分析处理设备8可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述生物信号的分析处理设备可包括，但不仅限于，处理器80、存储器81。本领域技术人员可以理解，图8仅仅是生物信号的分析处理设备8的示例，并不构成对生物信号的分析处理设备8的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述生物信号的分析处理设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器80可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列 (Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器81可以是所述生物信号的分析处理设备8的内部存储单元，例如生物信号的分析处理设备8的硬盘或内存。所述存储器81也可以是所述生物信号的分析处理设备8的外部存储设备，例如所述生物信号的分析处理设备8上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器81还可以既包括所述生物信号的分析处理设备8的内部存储单元也包括外部存储设备。所述存储器81用于存储所述计算机程序以及所述生物信号的分析处理设备所需的其他程序和数据。所述存储器81还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详 述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元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， 本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (10)

  1. 一种生物信号的分析处理方法，其特征在于，所述高效的数据分析处理方法包括：
    通过传感器获取生物信号的采样数据；
    确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量；
    根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
    根据过滤后的数据的斜率计算所述生物信号的参数值。
  2. 根据权利要求1所述的生物信号的分析处理方法，其特征在于，所述确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量的步骤包括：
    将当前的采样数据左移N1位，得到第一移位数据；
    由所述第一移位数据减去上一次采样的累计数据，并对得到的差值右移N2位，得到第二移位数据；
    将所述第二移位数据与上一次采样的累计数据相加后并右移N1位，得到当前采样的直流分量，其中N1、N2为自然数，且N1大于N2；
    或者还包括根据当前的采样数据和直流分量的差值确定交流分量。
  3. 根据权利要求2所述的生物信号的分析处理方法，其特征在于，传感器采集的采样数据为16位，所述预设位数为32位，所述N1为15，所述N2为4。
  4. 根据权利要求1所述的生物信号的分析处理方法，其特征在于，所述根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确 定的数字滤波器进行卷积运算，对所述有效分量进行过滤的步骤包括：
    S1，初始化索引i，j为0，滤波器阶数N，将滤波器参数存入数组f，其中i为数组f的索引，j为数组g的索引；
    S2，获取有效分量数据存入数组g，并记录存入的数据的当前索引为j；
    S3，将数组f与数组g相应的索引数据相乘后累加，得到过滤后的数据；
    S4，将j+1后与滤波器阶数N求余，将求余结果赋值为j，并将数组f循环向前移位，当获取到新的有效分量数据时，返回步骤S2。
  5. 根据权利要求1所述的生物信号的分析处理方法，其特征在于，所述根据过滤后的数据的斜率计算所述生物信号的参数值的步骤包括：
    根据过滤后的数据生成数据曲线，计算当前数据在所述数据曲线上的斜率；
    根据相邻两点的斜率正负值变化点计算所述特征信号的变化周期或频率。
  6. 根据权利要求1所述的特征信号的分析处理方法，其特征在于，所述通过传感器获取生物信号的采样数据的步骤包括：
    当所述传感器为模拟传感器时，所述模拟传感器采集的模拟信号通过噪声过滤电路过滤处理和/或放大电路的放大处理后的预处理信号，由模数转换电路根据单片机的控制指令，周期性的将所述预处理信号转换为数字信号。
  7. 一种生物信号的分析处理装置，其特征在于，所述高效的数据分析处理装置包括：
    采样单元，用于通过传感器获取生物信号的采样数据；
    有效分量提取单元，用于确定上一次采样时的累计数据，根据所述累计数据和所述采样数据，通过预定位数的定点整数方式提取所述采样数据中的有效分量；
    有效分量过滤单元，用于根据传感器所采集的信号确定数字滤波器参数，通过对离散的有效分量与所确定的数字滤波器进行卷积运算，对所述有效分量进行过滤；
    参数值计算单元，用于根据过滤后的数据的斜率计算所述生物信号的参数值。
  8. 根据权利要求7所述的生物信号的分析处理装置，其特征在于，所述有效分量提取单元包括：
    第一移位子单元，用于将当前的采样数据左移N1位，得到第一移位数据；
    第二移位子单元，用于由所述第一移位数据减去上一次采样的累计数据，并对得到的差值右移N2位，得到第二移位数据；
    第三移位子单元，用于将所述第二移位数据与上一次采样的累计数据相加后并右移N1位，得到当前采样的直流分量，其中N1、N2为自然数，且N1大于N2；
    或者还包括交流分量确定子单元，用于根据当前的采样数据和直流分量的差值确定交流分量。
  9. 一种生物信号的分析处理设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至6任一项所述生物信号的分析处理方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至6任一项所述生物信号的分析处理方法的步骤。
PCT/CN2019/124333 2018-12-17 2019-12-10 一种生物信号的分析处理方法、装置及设备 Ceased WO2020125494A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201811542704.6 2018-12-17
CN201811542704.6A CN109512395B (zh) 2018-12-17 2018-12-17 一种生物信号的分析处理方法、装置及设备

Publications (1)

Publication Number Publication Date
WO2020125494A1 true WO2020125494A1 (zh) 2020-06-25

Family

ID=65796099

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/124333 Ceased WO2020125494A1 (zh) 2018-12-17 2019-12-10 一种生物信号的分析处理方法、装置及设备

Country Status (2)

Country Link
CN (1) CN109512395B (zh)
WO (1) WO2020125494A1 (zh)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109512395B (zh) * 2018-12-17 2020-09-25 中国科学院深圳先进技术研究院 一种生物信号的分析处理方法、装置及设备
CN111025015B (zh) * 2019-12-30 2023-05-23 广东电网有限责任公司 一种谐波检测方法、装置、设备和存储介质
CN112286933B (zh) * 2020-10-28 2021-09-14 况客科技（北京）有限公司 数据处理系统
CN117421531B (zh) * 2023-12-14 2024-03-05 深圳和润达科技有限公司 干扰环境下的有效数据智能确定方法及装置
CN118557200A (zh) * 2024-05-16 2024-08-30 天津大学 一种柔性单导脑电肌电同步快速采集贴片及其采集方法

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090259116A1 (en) * 2007-11-14 2009-10-15 Yoram Wasserman Method and Apparatus for Processing a Pulsatile Biometric Signal
CN103069001A (zh) * 2010-06-23 2013-04-24 美敦力迷你迈德公司 具有多探针和电极阵列的传感器系统
CN203153748U (zh) * 2013-03-12 2013-08-28 林祝发 基于音频口通信的血氧仪
CN103622704A (zh) * 2013-12-10 2014-03-12 中国医学科学院生物医学工程研究所 一种宽动态范围的脉搏血氧测量系统
CN104771148A (zh) * 2015-05-10 2015-07-15 瞿浩正 一种基于小波分解与重构的脉搏波提取方法和采集系统
CN108957111A (zh) * 2018-06-06 2018-12-07 青岛鼎信通讯股份有限公司 一种可滤除交流采样中残余直流分量的馈线终端计量方法
CN109512395A (zh) * 2018-12-17 2019-03-26 中国科学院深圳先进技术研究院 一种生物信号的分析处理方法、装置及设备

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6313773B1 (en) * 2000-01-26 2001-11-06 Sonic Innovations, Inc. Multiplierless interpolator for a delta-sigma digital to analog converter
CN201860303U (zh) * 2010-11-25 2011-06-08 上海宇芯微电子有限公司 数字滤波器电路
CN102170276B (zh) * 2011-03-01 2013-08-21 深圳市蓝韵实业有限公司 一种用于超声信号处理的升采样滤波方法
CN102710237B (zh) * 2012-06-15 2015-06-17 成都启臣微电子有限公司 一种一阶数字低通滤波方法、滤波器及电子设备

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090259116A1 (en) * 2007-11-14 2009-10-15 Yoram Wasserman Method and Apparatus for Processing a Pulsatile Biometric Signal
CN103069001A (zh) * 2010-06-23 2013-04-24 美敦力迷你迈德公司 具有多探针和电极阵列的传感器系统
CN203153748U (zh) * 2013-03-12 2013-08-28 林祝发 基于音频口通信的血氧仪
CN103622704A (zh) * 2013-12-10 2014-03-12 中国医学科学院生物医学工程研究所 一种宽动态范围的脉搏血氧测量系统
CN104771148A (zh) * 2015-05-10 2015-07-15 瞿浩正 一种基于小波分解与重构的脉搏波提取方法和采集系统
CN108957111A (zh) * 2018-06-06 2018-12-07 青岛鼎信通讯股份有限公司 一种可滤除交流采样中残余直流分量的馈线终端计量方法
CN109512395A (zh) * 2018-12-17 2019-03-26 中国科学院深圳先进技术研究院 一种生物信号的分析处理方法、装置及设备

Also Published As

Publication number Publication date
CN109512395B (zh) 2020-09-25
CN109512395A (zh) 2019-03-26

Similar Documents

Publication Publication Date Title
WO2020125494A1 (zh) 一种生物信号的分析处理方法、装置及设备
Ieong et al. A 0.83-$\mu {\rm W} $ QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35-$\mu {\rm m} $ CMOS
CN108185996A (zh) 动脉血管年龄估算模型构建方法和装置
CN102755157B (zh) 一种指端血流速度的测量方法
Meddah et al. FPGA‐based system for heart rate monitoring
Chowdhury et al. Photoplethysmogram-based heart rate and blood pressure estimation with hypertension classification
CN116028809A (zh) 一种连续血压的测量模型训练、测量方法及装置
CN110477865A (zh) 一种癫痫发作检测装置、终端设备及存储介质
CN118490186A (zh) 健康监测设备、方法及存储介质
CN105096227A (zh) 一种健康数据的可视化处理方法
Raeiatibanadkooki et al. Real time processing and transferring ECG signal by a mobile phone
Sá et al. Heart disease detection architecture for lead i off-the-person ecg monitoring devices
JP2023035888A (ja) 無線レーダー信号に基づいて心拍データを抽出するための装置及び方法
Ahmed et al. Architecture and design of real-time system for elderly health monitoring
Ahmed et al. Towards smart health monitoring system for elderly people
Mishra et al. Performance evaluation of various window techniques for noise cancellation from ECG signal
CN119732660A (zh) 血流动力学参数的检测方法、终端设备和系统
CN119598338A (zh) 一种基于fpga的呼吸音分类识别加速器设计方法及系统
Bansal et al. Algorithm for online detection of HRV from coherent ECG and carotid pulse wave
CN113616217B (zh) 基线漂移曲线的生成方法和装置
CN110840436A (zh) 获取心电信号的方法、装置、终端和计算机可读存储介质
CN113057611B (zh) 用于输出信息的方法、装置、设备以及存储介质
CN103720462A (zh) 脉搏波信号分析方法和装置
CN115040156A (zh) 基人工智能的心血管疾病诊断方法、系统、设备及介质
CN105726007A (zh) 基于usb接口心电信号的采集系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19898669

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19898669

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 18.11.21)

122 Ep: pct application non-entry in european phase

Ref document number: 19898669

Country of ref document: EP

Kind code of ref document: A1