WO2023045109A1 - 一种扩频调制的电极接触阻抗在线测量装置和方法 - Google Patents

一种扩频调制的电极接触阻抗在线测量装置和方法 Download PDF

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WO2023045109A1
WO2023045109A1 PCT/CN2021/137615 CN2021137615W WO2023045109A1 WO 2023045109 A1 WO2023045109 A1 WO 2023045109A1 CN 2021137615 W CN2021137615 W CN 2021137615W WO 2023045109 A1 WO2023045109 A1 WO 2023045109A1
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sequence
digitized
electrode
analog waveform
contact impedance
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French (fr)
Inventor
周小猛
李光林
邓新平
杨子健
李向新
田岚
张浩诗
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Shenzhen Institute of Advanced Technology of CAS
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/20—Measuring earth resistance; Measuring contact resistance, e.g. of earth connections, e.g. plates
    • G01R27/205—Measuring contact resistance of connections, e.g. of earth connections
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/251—Means for maintaining electrode contact with the body
    • A61B5/257—Means for maintaining electrode contact with the body using adhesive means, e.g. adhesive pads or tapes
    • A61B5/259—Means for maintaining electrode contact with the body using adhesive means, e.g. adhesive pads or tapes using conductive adhesive means, e.g. gels
    • 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
    • A61B5/72—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

Definitions

  • the present invention relates to the technical field of electrophysiological detection, and more specifically, to an on-line measurement device and method for electrode contact impedance of spread spectrum modulation.
  • Electrophysiological signals such as human ECG, EMG, and EEG contain various physiological or psychological activity information of key tissues such as the human heart, neuromuscular, and brain.
  • information such as the health status of the human body, the location of the disease, and the movement intention can be obtained. It has important application value in clinical medicine, health monitoring, rehabilitation engineering, etc., and has been widely used. .
  • the electrodes When collecting electrophysiological signals, the electrodes are attached to the surface of the skin, and there is contact impedance between them and the skin. Human body movement, breathing or the decline in the performance of the conductive paste will cause changes in the contact impedance, which in turn will cause the baseline drift of the electrophysiological signal and interfere with the acquisition process. Therefore, it is necessary to measure the contact between the electrode and the skin while the electrophysiological signal is being collected. Impedance, get the theoretical value of baseline drift, and then use algorithms such as adaptive filtering to eliminate this interference. In addition, in order to judge whether the collected signal is valid, it is necessary to measure the contact impedance between the electrode and the skin to determine whether the electrode has fallen off. These applications all require a device and method for real-time online measurement of electrode contact impedance during the process of electrophysiological signal acquisition, and at the same time, the measurement device and method cannot cause obvious interference to the acquisition process.
  • the electrode contact impedance is usually measured according to Ohm's law, that is, the voltage/current excitation is applied to the electrode circuit under test, and the contact impedance is calculated by measuring the response current/voltage of the electrode.
  • Ohm's law that is, the voltage/current excitation is applied to the electrode circuit under test
  • the contact impedance is calculated by measuring the response current/voltage of the electrode.
  • the voltage amplitude output by the excitation source is usually high, generally hundreds to thousands of millivolts, which is much higher than the amplitude of the human body's electrophysiological signal at most several millivolts. Therefore, when performing impedance measurement and electrophysiological signal acquisition at the same time, the impedance measurement source will cause significant spectrum aliasing and noise interference to the signal acquisition, resulting in a decrease in the quality of the acquired signal.
  • the frequency band of the impedance measurement source is generally set to be much higher than the frequency band of the electrophysiological signal, and then the high-frequency response signal of the impedance measurement and the electrophysiological signal are respectively extracted through high-speed AD sampling and software and hardware filtering. Or it can be solved by switching the switch so that the measurement and collection are performed alternately in time.
  • these solutions need to add special chips, high-speed AD sampling, software and hardware filtering and other circuits, which increase the cost and the effect is not ideal.
  • the purpose of the present invention is to overcome the defects of the above-mentioned prior art, and provide a method and device for online measurement of electrode contact impedance by spread spectrum modulation.
  • a spread spectrum modulated electrode contact impedance online measuring device includes: a microcontroller, an m-sequence generation module, a DA conversion module, an AD sampling module and a voltage dividing resistor, wherein:
  • the m-sequence generation module is used to generate digital m-sequences
  • the DA conversion module is used to convert the digitized m-sequence into an m-sequence analog waveform with a set amplitude and frequency;
  • the voltage dividing resistor is used to divide the m-sequence analog waveform, and the m-sequence analog waveform after the voltage division is injected into the electrode circuit for measuring the electrophysiological signal;
  • the AD sampling module is used to collect electrophysiological signals including m-sequence analog waveforms after voltage division, and obtain sampling data;
  • the microcontroller is used to calculate the cross-correlation function between the sampling data and the digitized m-sequence to obtain the contact impedance between the electrode and the measurement target.
  • a spread spectrum modulated electrode contact impedance online measurement method includes the following steps:
  • the present invention has the advantage that the m-sequence is used as the signal source for electrode contact impedance measurement, and the contact impedance information that may be submerged under the electrophysiological signal and noise energy density is highlighted, while the electrical Physiological signals and noise are extended to a very wide frequency band, so that the acquisition of electrophysiological signals and the measurement of electrode contact impedance can be carried out simultaneously without interfering with each other.
  • the present invention reduces hardware cost and is more convenient to implement, and solves the problems of significant interference to electrophysiological signal acquisition and complicated hardware implementation in the prior art when measuring electrode contact impedance.
  • FIG. 1 is a schematic diagram of an electrode contact impedance online measurement device based on spread spectrum modulation according to an embodiment of the present invention
  • Fig. 2 is the flowchart of the electrode contact impedance online measurement method based on spread spectrum modulation according to an embodiment of the present invention
  • Fig. 3 is a schematic diagram of an m-sequence and its autocorrelation function according to an embodiment of the present invention.
  • the proposed on-line electrode contact impedance measurement device based on spread spectrum modulation includes a microcontroller, a DA converter, an AD sampling module, electrodes, voltage dividing resistors (such as R3, R4), etc., wherein the microcontroller It can further include m (Maximal Length) sequence generation module, cross-correlation operation module and signal acquisition module, etc.
  • the microcontroller is used to control the m-sequence generation module to generate digitized m-sequences, control the DA converter to convert the digitized m-sequences into m-sequence analog waveforms, and the microcontroller also receives and processes the sampled data from the AD sampling circuit (i.e., the AD sampling module), Perform cross-correlation operations and other processing on the data.
  • Microcontrollers can be single-chip microcomputers, field programmable logic gate arrays (FPGA, Field Programmable Gate Array), digital signal processors (DSP, Digital Signal Processor) and other devices that can implement specific logic functions through user programming.
  • the m-sequence generation module is used to generate the digitized m-sequence of a specific period.
  • the m-sequence can be generated by a microcontroller by shifting the multi-stage linear feedback shift register by software, and the m-sequence to be used can also be stored in non-volatile memory such as flash memory (Flash Memory) in advance , obtained by looking up the table when used.
  • non-volatile memory such as flash memory (Flash Memory) in advance , obtained by looking up the table when used.
  • the DA conversion module is used to convert the digitized m-sequence into an m-sequence analog waveform of a specific amplitude and frequency under the control of the microcontroller, and send the waveform to the electrode circuit as the excitation voltage for electrode contact impedance measurement.
  • the electrode loop is used for collecting or detecting electrophysiological signals of the measurement target.
  • resistors R3, R4, together with electrode contact impedances R1, R2, are used to divide the voltage of the m-sequence analog waveform, so that the waveform amplitude after voltage division is much lower than the amplitude of the electrophysiological signal, so as to avoid the generated
  • the m-sequences interfere with the acquisition of electrophysiological signals.
  • the AD sampling module simultaneously collects the electrophysiological signals conducted by the human body through contact impedances R1, R2 and electrodes, as well as the m-sequence analog waveforms after voltage division at both ends of the contact impedances R1 and R2.
  • the digital signal obtained after AD sampling is sent to the microprocessor for further calculation and processing.
  • the microcontroller receives the sampling data of the AD sampling module, which contains the m-sequence analog waveform after the weak amplitude voltage division, calculates the cross-correlation function between the data and the digitized m-sequence generated by the m-sequence generation module, and extracts the cross-correlation function peak. Since the human electrophysiological signal and the generated m-sequence are independent of each other, the cross-correlation function value is very small; while the m-sequence analog waveform in the sampling data is very close to the digital m-sequence, and the correlation coefficient is very high, so the peak value of the cross-correlation function depends on m Amplitude of the sequence analog waveform. When the resistances R3 and R4 are constant, the peak is proportional to the sum of the contact resistances R1 and R2. By calculating the peak value and converting it, the contact impedance between the two electrodes of the same sampling channel and the skin can be obtained.
  • the working process of the provided on-line measurement device for electrode contact impedance based on spread spectrum modulation includes the following steps.
  • Step S210 the m-sequence generating module generates a digitized m-sequence.
  • a microcontroller when measuring the contact impedance of the electrodes, a microcontroller is firstly used to control the m-sequence generation module to generate a digitized m-sequence.
  • the m sequence has excellent binary autocorrelation characteristics similar to noise.
  • a typical m-sequence is shown in Figure 3(a), which has equal positive and negative amplitudes and repeats periodically.
  • the autocorrelation function waveform of positive and negative shifts within a period is shown in Figure 3(b), which is also periodic and is the same as the period of the m-sequence. The function obtains the maximum value when the sequence shift is zero, and the value depends on the magnitude of the m sequence; when the shift is other values, it is the negative reciprocal of the sequence length, and the longer the sequence, the smaller the value.
  • the m-sequence can be generated by a multi-stage linear feedback shift register. Under the action of the clock signal, the shift register shifts continuously, and the output is fed back to the input through a certain functional relationship to generate an m-sequence with a fixed symbol rate and period. The number of stages of the shift register determines the period and noise suppression capability of the m-sequence.
  • the microprocessor for calculating the cross-correlation function has higher computing and storage capabilities, usually 10 to 12 levels are selected, and the corresponding m-sequence cycle is 1023 to 4095 bits.
  • tools such as Matlab can be used to generate digitized m-sequences in advance, stored in the flash memory of the microprocessor, and the current value of m-sequences to be output to the DA conversion module can be obtained by looking up the table during use.
  • step S220 the DA conversion module converts the digitized m-sequence into an m-sequence analog waveform of a specific amplitude and frequency, so that the divided analog waveform is much lower than the amplitude of the electrophysiological signal.
  • the DA conversion module converts the digitized m-sequence into an m-sequence analog waveform of a specific amplitude and frequency, so that after the waveform is divided by resistance, it is much lower than
  • the amplitude of the electrophysiological signal for example, is below ⁇ 50 microvolts. But this voltage should be higher than the minimum resolution of the AD sampling module so that it can be sampled correctly.
  • the microcontroller can dynamically adjust the amplitude of the m-sequence analog waveform output by the DA conversion module, so that it is always much lower than the amplitude of the electrophysiological signal after being divided.
  • the microcontroller controls the frequency of the analog waveform converted by DA at the same time, so that the waveform can be sampled by the AD sampling module without distortion. Usually, the frequency of the waveform can be set to a quarter of the AD sampling frequency.
  • Step S230 injecting the m-sequence analog waveforms into the electrode loop after being divided by resistors.
  • Divider resistors R3 and R4 provide an additional input path for electrophysiological signals. In order to avoid this input path from causing a significant impact on the input impedance, R3 and R4 should choose a larger resistance value, usually above 100M. Its resistance value should be accurately measured in advance to accurately calculate the contact resistance of the electrode.
  • the m-sequence analog waveform Um(t) is used as the signal source for impedance measurement, applied to the electrode circuit, and after being divided by the resistance, a voltage Uc(t) is generated at both ends of the contact impedance R1 and R2, expressed as:
  • the contact impedance R1+R2 when the electrode is in normal contact with the skin is much smaller than R3+R4, the amplitude of Uc(t) is much lower than the amplitude of the electrophysiological signal, and its influence on the acquisition of the electrophysiological signal can be ignored.
  • Us(t) is the superimposed signal
  • Ue(t) is the electrophysiological signal
  • n(t) is the noise.
  • step S240 the AD sampling module collects electrophysiological signals including m-sequence analog waveforms after voltage division.
  • the AD sampling module collects the superimposed signal Us(t) using the sampling frequency required for collecting electrophysiological signals, and sends the sampled data to the microcontroller.
  • step S250 the microcontroller obtains the contact impedance between the electrode and the human body by calculating the cross-correlation function between the AD sampling data and the digitized m-sequence.
  • the microcontroller calculates the cross-correlation function Rsm( ⁇ ) between the sampling data and the digitized m-sequence Um(t) generated by the m-sequence generation module, expressed as:
  • T is the period of m sequence.
  • the calculated cross-correlation function is the cross-correlation function of the m-sequence analog waveform Uc(t), electrophysiological signal Ue(t), noise n(t) and the digitized m-sequence Um(t) respectively after voltage division Sum. Both Ue(t) and n(t) are uncorrelated with Um(t), and Rem( ⁇ ) and Rnm( ⁇ ) are both small, approximately the negative reciprocal of the m-sequence period. When the m-sequence period is long, it can be considered Rsm( ⁇ ) ⁇ Rcm( ⁇ ). Through calculation, because Uc(t) is completely correlated with Um(t), the peak value of their cross-correlation function Rcm( ⁇ ) is the product of Uc(t) and Um(t), namely:
  • Rmax is the peak value of the cross-correlation function Rcm( ⁇ )
  • Uc and Um are the amplitudes of Uc(t) and Um(t) respectively.
  • the cross-correlation function Rsm( ⁇ ) of the m-sequence concentrates the weakly dispersed energy of Uc(t) at one point, and expresses the contact impedance information buried under the energy density of electrophysiological signals and noise through this point.
  • the longer the period of the m-sequence the stronger the ability of information extraction and noise suppression.
  • the electrophysiological signal and noise are not correlated with Um(t), and are extended to a wide frequency band.
  • the m-sequence analog waveform can be divided to a value much lower than the amplitude of the electrophysiological signal, so that the electrophysiological signal The reason why acquisition and electrode contact impedance measurement are performed simultaneously without interfering with each other.
  • the m-sequence with a longer period is more beneficial to information extraction and noise suppression, the longer the period, the lower the time resolution of the contact impedance calculation results, and it also requires a microprocessor with higher data storage and computing capabilities.
  • the sampling data Us(t) of 1 to 2 cycles can be intercepted from the AD sampling module to participate in the cross-correlation calculation. For example, a cycle of digitized m-sequence Um(t) is pre-stored in the microprocessor.
  • the digitized m-sequence after zero-padded is cyclically shifted.
  • the step size is 1, the number of shifts is the same as the length of the sampled data, and then the inner product of each shifted digitized m-sequence and the sampled data is calculated, and then divided by the cycle of the m-sequence to obtain the cross-correlation under the shift function value.
  • a shift-add multiplier when performing multiplication in the inner product, using a shift-add multiplier saves hardware resources more, and its implementation steps are: for n-bit multipliers and multiplicands, define the lowest bit to the highest bit as the 0th To the n-1th bit; start judging from the 0th bit of the multiplier, if it is 1, shift the multiplicand by 0 bits to the left, if it is 0, do not process, and judge to the highest bit in turn; after all shifts The multiplicands of are added together to get the multiplication result. After calculating the value of the cross-correlation function, the contact impedance of the electrode can be calculated according to the peak value of the function.
  • Step S260 judging whether the amplitude of the waveform after voltage division is within an appropriate range.
  • Step S270 adjusting the amplitude of the m-sequence analog waveform output by the DA conversion module.
  • the contact impedance between the electrode and the skin may change over time, resulting in the excessively large m-sequence analog waveform Uc(t) after voltage division, which interferes with the acquisition of electrophysiological signals; or causes Uc(t) is too small to be accurately collected by the AD sampling module.
  • the microcontroller can dynamically adjust the amplitude of the m-sequence analog waveform Um(t) output by the DA conversion module according to the measured contact impedance, so that the amplitude of the waveform Uc(t) after voltage division is always maintained at a suitable value. within range.
  • the microcontroller can close the electrophysiological signal acquisition of this channel or send a message to remind the user to check the electrode connection.
  • the AD sampled data can be directly regarded as the collected data of electrophysiological signals, without processing such as high-pass filtering similar to the prior art.
  • the m-sequence generating module is independent of the microcontroller, or other voltage dividing circuits are used instead of resistor voltage dividing, etc.
  • the present invention calculates the cross-correlation function between the AD sampling data and the digitized m-sequence, and then obtains the contact impedance of the electrode; uses the m-sequence generation circuit, the DA conversion module and the voltage divider circuit to dynamically generate m-sequences with weak amplitudes Analog waveform.
  • the present invention has at least the following technical effects
  • the present invention uses the m-sequence as the signal source of the electrode contact impedance measurement, and highlights the contact impedance information that may be submerged under the electrophysiological signal and noise energy density, and extends the electrophysiological signal and noise to a wide range In the frequency band, the amplitude of the signal source can be set much lower than that of the electrophysiological signal, so that the acquisition of the electrophysiological signal and the measurement of the electrode contact impedance can be carried out simultaneously without interfering with each other.
  • the present invention compared with the existing electrophysiological signal acquisition equipment, the present invention only increases the DA conversion module and the resistance on the hardware, and the key modules such as m-sequence generation and impedance measurement can be realized by software, so compared with the prior art, the impedance measurement is used Chip or high-speed AD sampling circuit solution, the invention reduces hardware cost and is more convenient to realize. And it has been verified by simulation test, the principle and method are feasible.
  • the present invention can be a system, method and/or computer program product.
  • a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present invention.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disc read only memory
  • DVD digital versatile disc
  • memory stick floppy disk
  • mechanically encoded device such as a printer with instructions stored thereon
  • a hole card or a raised structure in a groove and any suitable combination of the above.
  • computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
  • Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or Source or object code written in any combination, including object-oriented programming languages—such as Smalltalk, C++, Python, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages.
  • Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as via the Internet using an Internet service provider). connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, field programmable gate array (FPGA), or programmable logic array (PLA)
  • FPGA field programmable gate array
  • PDA programmable logic array
  • These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that when executed by the processor of the computer or other programmable data processing apparatus , producing an apparatus for realizing the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific way, so that the computer-readable medium storing instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks in flowcharts and/or block diagrams.
  • each block in a flowchart or block diagram may represent a module, a portion of a program segment, or an instruction that includes one or more Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by means of hardware, implementation by means of software, and implementation by a combination of software and hardware are all equivalent.

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Abstract

本发明公开了一种扩频调制的电极接触阻抗在线测量装置和方法。该装置包括：微控制器、m序列生成模块、DA转换模块、AD采样模块和分压电阻，其中：m序列生成模块用于生成数字化m序列；DA转换模块用于将数字化m序列转换为设定幅值和频率的m序列模拟波形；分压电阻用于对m序列模拟波形进行分压，且分压后的m序列模拟波形被注入到测量电生理信号的电极回路；AD采样模块用于采集包含分压后m序列模拟波形的电生理信号，获得采样数据；微控制器用于计算所述采样数据与所述数字化m序列的互相关函数，获得电极与测量目标的接触阻抗。本发明易于实现，并且阻抗测量和信号采集可以同时进行而互不干扰。

Description

一种扩频调制的电极接触阻抗在线测量装置和方法 技术领域
本发明涉及电生理检测技术领域，更具体地，涉及一种扩频调制的电极接触阻抗在线测量装置和方法。
背景技术
人体心电、肌电、脑电等电生理信号蕴含人体心脏、神经肌肉、大脑等关键组织的多种生理或心理活动信息。对人体电生理信号进行采集、分析和处理，可获知人体的健康状况、疾病部位、运动意图等信息，在临床医学、健康监护、康复工程等方面均具有重要的应用价值，得到了广泛的应用。
电生理信号采集时，电极贴附于皮肤表面，与皮肤之间存在接触阻抗。人体运动、呼吸或导电膏效能减退等都会导致接触阻抗的变化，进而导致电生理信号的基线漂移，对采集过程造成干扰，为此，需要在电生理信号采集的同时，测量电极与皮肤的接触阻抗，得到基线漂移的理论值，然后使用自适应滤波等算法消除此干扰。此外，为评判采集的信号是否有效，需要通过测量电极与皮肤的接触阻抗来判断电极是否脱落。这些应用都需要一种在电生理信号采集的过程中，实时在线测量电极接触阻抗的装置和方法，同时该测量装置和方法不能对采集过程造成明显的干扰。
目前，通常根据欧姆定律测量电极接触阻抗，即对被测电极回路施加电压/电流激励，通过测量电极的响应电流/电压来计算得到接触阻抗。总体上具体实施方式有两种：一是使用专用阻抗测量芯片将激励的施加与响应的测量集成于一个芯片中，如专利申请CN107049299A、CN202589521U使用AD5933芯片；二是使用分立的电压源、电流源以及电压电流采集测量回路，如专利申请CN104684470A、CN104490387A。
为准确测量接触阻抗，激励源输出的电压幅值通常较高，一般为数百至上千毫伏，远高于至多几毫伏的人体电生理信号的幅值。因此，在同时进行阻抗测量和电生理信号采集时，阻抗测量源会给信号采集造成显著的 频谱混叠和噪声干扰，导致采集的信号质量下降。现有技术一般将阻抗测量源的频带设置为远高于电生理信号的频带，然后通过高速AD采样和软硬件滤波来分别提取阻抗测量的高频响应信号和电生理信号。或者通过开关切换，使测量和采集在时间上交替进行来解决。但这些解决方法需要增加专用芯片、高速AD采样、软硬件滤波等电路，增加了成本且效果不够理想。
发明内容
本发明的目的是克服上述现有技术的缺陷，提供一种扩频调制的电极接触阻抗在线测量方法和装置。
根据本发明的第一方面，提供一种扩频调制的电极接触阻抗在线测量装置。该装置包括：微控制器、m序列生成模块、DA转换模块、AD采样模块和分压电阻，其中：
m序列生成模块用于生成数字化m序列；
DA转换模块用于将数字化m序列转换为设定幅值和频率的m序列模拟波形；
分压电阻用于对m序列模拟波形进行分压，且分压后的m序列模拟波形被注入到测量电生理信号的电极回路；
AD采样模块用于采集包含分压后m序列模拟波形的电生理信号，获得采样数据；
微控制器用于计算所述采样数据与所述数字化m序列的互相关函数，获得电极与测量目标的接触阻抗。
根据本发明的第二方面，提供一种扩频调制的电极接触阻抗在线测量方法。该方法包括以下步骤：
生成数字化m序列；
将所述数字化m序列转换为设定幅值和频率的m序列模拟波形；
对所述m序列模拟波形进行分压，并将分压后的m序列模拟波形注入到电极回路，该电极回路用于对测量目标进行电生理信号检测；
采集包含分压后m序列模拟波形的电生理信号，获得采样数据；
计算所述采样数据与所述数字化m序列的互相关函数，获得电极与测量目标的接触阻抗。
与现有技术相比，本发明的优点在于，将m序列作为电极接触阻抗测量的信号源，将本可能淹没在电生理信号和噪声能量密度之下的接触阻抗信息突出显示出来，而将电生理信号、噪声扩展到很宽的频带上，使得电生理信号采集与电极接触阻抗测量可同时进行而互不干扰。此外，本发明降低了硬件成本、实现更方便，解决了现有技术在测量电极接触阻抗时，会对电生理信号采集造成显著干扰以及硬件实现较为复杂的问题。
通过以下参照附图对本发明的示例性实施例的详细描述，本发明的其它特征及其优点将会变得清楚。
附图说明
被结合在说明书中并构成说明书的一部分的附图示出了本发明的实施例，并且连同其说明一起用于解释本发明的原理。
图1是根据本发明一个实施例的基于扩频调制的电极接触阻抗在线测量装置的示意图；
图2是根据本发明一个实施例的基于扩频调制的电极接触阻抗在线测量方法的流程图；
图3是根据本发明一个实施例的m序列及其自相关函数示意图。
具体实施方式
现在将参照附图来详细描述本发明的各种示例性实施例。应注意到：除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本发明的范围。
以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本发明及其应用或使用的任何限制。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为说明书的一部分。
在这里示出和讨论的所有例子中，任何具体值应被解释为仅仅是示例 性的，而不是作为限制。因此，示例性实施例的其它例子可以具有不同的值。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
参见图1所示，所提出的基于扩频调制的电极接触阻抗在线测量装置包括微控制器、DA转换器、AD采样模块、电极、分压电阻(如R3、R4)等，其中微控制器可进一步包含m(Maximal Length)序列生成模块、互相关运算模块和信号采集模块等。
微控制器用于控制m序列生成模块生成数字化m序列，控制DA转换器将数字化的m序列转换为m序列模拟波形，微控制器还接收并处理AD采样电路(即AD采样模块)的采样数据，对数据进行互相关运算等处理。微控制器可以是单片机、现场可编程逻辑门阵列(FPGA，Field Programmable Gate Array)、数字信号处理器(DSP，Digital Signal Processor)等可通过用户编程实现特定的逻辑功能的器件。
m序列生成模块用于生成特定周期的数字化m序列。在一个实施例中，m序列可由微控制器通过对多级线性反馈移位寄存器进行软件移位来生成，也可预先将待使用的m序列存储于闪存(Flash Memory)等非易失存储器中，使用时通过查表得到。
DA转换模块用于在微控制器的控制下，将数字化m序列转换为特定幅值和频率的m序列模拟波形，并将该波形作为电极接触阻抗测量的激励电压，发送至电极回路中，该电极回路用于对测量目标进行电生理信号采集或检测。
在图1中，电阻R3、R4与电极接触阻抗R1、R2一起，用于给m序列模拟波形分压，使得分压后的波形幅值远低于电生理信号的幅值，以避免所生成的m序列对电生理信号采集造成干扰。
AD采样模块同时采集人体通过接触阻抗R1、R2以及电极传导过来的电生理信号，以及接触阻抗R1和R2两端经过分压后的m序列模拟波形。AD采样后得到的数字信号被送入至微处理器中，以进行进一步的运算处理。
微控制器接收AD采样模块的采样数据，该数据包含有幅值微弱的分压后m序列模拟波形，计算该数据与m序列生成模块生成的数字化m序列的互相关函数，提取互相关函数的峰值。由于人体电生理信号与生成的m序列相互独立，互相关函数值很小；而采样数据中的m序列模拟波形与数字化m序列十分接近，相关系数很高，故互相关函数的峰值取决于m序列模拟波形的幅值。当电阻R3和R4恒定时，该峰值与接触阻抗R1和R2之和成正比。通过计算该峰值，并经过换算后，即可得到同一采样通道的两个电极与皮肤的接触阻抗。
具体地，结合图2和图1所示，所提供的基于扩频调制的电极接触阻抗在线测量装置的工作过程包括以下步骤。
步骤S210，m序列生成模块生成数字化m序列。
在本发明实施例中，测量电极的接触阻抗时，首先使用微控制器控制m序列生成模块生成数字化m序列。
m序列作为伪随机噪声(PN，Pseudorandom Noise)序列的一种，具有类似于噪声的优良的二值自相关特性。一个典型的m序列如图3(a)所示，它正负幅值相等，周期性重复。一个周期内正负移位的自相关函数波形如图3(b)所示，它也具有周期性，且与m序列的周期相同。函数在序列移位为零时取得极大值，该值取决于m序列的幅值；移位为其它值时为序列长度的负倒数，序列越长，其值越小。其他的信号如人体电生理信号与m序列不相关，它和m序列在任意移位时的互相关函数均很小。m序列的这一特性，使得它能将湮没在噪声下的有用信息提取出来。
在一个实施例中，m序列可由多级线性反馈移位寄存器生成。在时钟信号的作用下，移位寄存器不断移位，并将输出通过一定的函数关系反馈给输入，产生码元速率和周期固定的m序列。移位寄存器的级数决定m序列的周期及噪声抑制能力。级数越高，周期越长，阻抗测量精度越高，抗干扰性越好，且分压后的m序列模拟波形幅值可取得越低，对电生理信号采集的影响越小，但同时需要计算互相关函数的微处理器具有更高的运算和存储能力，通常选择10至12级，对应的m序列周期为1023位至4095位。
可选地，也可事先利用Matlab等工具生成数字化的m序列，存储于微处理器的闪存中，使用时通过查表得到待输出至DA转换模块的m序列当前值。
步骤S220，DA转换模块将数字化m序列转换为特定幅值和频率的m序列模拟波形，使得分压后的模拟波形远低于电生理信号的幅值。
DA转换模块在微控制器的控制下，将数字化m序列转换为特定幅值和频率的m序列模拟波形，使得该波形经过电阻分压后，施加于P电极和N电极两端时远低于电生理信号的幅值，如在±50微伏以下。但该电压应高于AD采样模块的最小分辨率，以使得它能被正确地采样。在进行阻抗测量时，微控制器可通过动态调整DA转换模块输出的m序列模拟波形的幅值，使之被分压后始终远低于电生理信号的幅值。微控制器同时控制DA转换的模拟波形的频率，使得该波形能不失真地被AD采样模块所采样，通常可将波形的频率设置为AD采样频率的四分之一。
步骤S230，将m序列模拟波形经过电阻分压后注入至电极回路。
分压电阻R3和R4为电生理信号提供了一条额外的输入通路，为避免此输入通路对输入阻抗造成显著影响，R3和R4应选取较大的阻值，通常可取100M以上。应预先准确地测量其阻值，以准确计算电极的接触阻抗。
m序列模拟波形Um(t)作为阻抗测量的信号源，施加至电极回路，被电阻分压后，在接触阻抗R1和R2两端产生电压Uc(t)，表示为：
Figure PCTCN2021137615-appb-000001
因电极与皮肤正常接触时的接触阻抗R1+R2远小于R3+R4，Uc(t)的幅值远低于电生理信号的幅值，可忽略它对电生理信号采集的影响。
叠加了分压后m序列模拟波形Uc(t)和电生理信号的P电极和N电极两端电压可表示为
Us(t)＝Uc(t)+Ue(t)+n(t)   (2)
其中Us(t)为叠加后的信号，Ue(t)为电生理信号，n(t)为噪声。
步骤S240，AD采样模块采集包含分压后m序列模拟波形的电生理信号。
在该步骤中，AD采样模块使用采集电生理信号所需要的采样频率，采集叠加后的信号Us(t)，并将采样数据发送至微控制器中。
步骤S250，微控制器通过计算AD采样数据与数字化m序列的互相关函数，得到电极与人体的接触阻抗。
具体地，微控制器计算采样数据与m序列生成模块生成的数字化m序列Um(t)的互相关函数Rsm(τ)，表示为：
Figure PCTCN2021137615-appb-000002
其中T为m序列的周期。由上式可知，计算得到的互相关函数为分压后m序列模拟波形Uc(t)、电生理信号Ue(t)、噪声n(t)分别与数字化m序列Um(t)的互相关函数之和。Ue(t)及n(t)均和Um(t)不相关，Rem(τ)和Rnm(τ)均很小，近似为m序列周期的负倒数，当m序列周期较长时，可认为Rsm(τ)≈Rcm(τ)。通过计算可得，因Uc(t)与Um(t)完全相关，它们的互相关函数Rcm(τ)的峰值即为Uc(t)和Um(t)幅值的乘积，即：
Uc＝Rmax/Um   (4)
其中，Rmax为互相关函数Rcm(τ)的峰值，Uc、Um分别为Uc(t)和Um(t)的幅值。
在Um(t)、R3、R4恒定时，Uc(t)的幅值取决于电极接触阻抗R1和R2。因此，通过计算AD采样数据与数字化m序列的互相关函数，获取函数的峰值，即可得到接触阻抗R1和R2两端的电压幅值，进而通过m序列模拟波形的幅值和电阻R3、R4的阻值，计算出电极的接触阻抗，即：
Figure PCTCN2021137615-appb-000003
通过以上分析可知，m序列的互相关函数Rsm(τ)将Uc(t)微弱分散的能量集中到一点上，将湮没在电生理信号和噪声能量密度之下的接触阻抗信息通过该点表现出来，m序列周期越长，信息提取和噪声抑制能力越强。电生理信号、噪声与Um(t)不相关，被扩展到很宽的频带上，通过这种方式，可将m序列模拟波形分压至远低于电生理信号的幅值，使得电生理信号采 集与电极接触阻抗测量同时进行而互不干扰的原因。
虽然周期较长的m序列对信息提取和噪声抑制更有利，但周期越长，接触阻抗计算结果的时间分辨率越低，同时也需要微处理器具有更高的数据存储与运算能力。计算接触阻抗时，可根据微处理器的内存容量和处理速度，从AD采样模块中截取1～2个周期的采样数据Us(t)参与互相关运算。例如，一个周期的数字化m序列Um(t)预先存储在微处理器中，运算前需先对其补零至与采样数据长度相等，再将补零后的数字化m序列循环移位，移位步长为1，移位次数与采样数据的长度相同，然后计算每个移位后的数字化m序列与采样数据的内积，再除以m序列的周期，即得到该移位下的互相关函数值。优选地，在进行内积中的乘法运算时，使用移位相加乘法器较为节省硬件资源，其实现步骤为：对于n位的乘数和被乘数，定义最低位至最高位为第0至第n-1位；从乘数的第0位开始判断，若为1，将被乘数左移0位，若为0，则不进行处理，依次判断至最高位；将所有移位后的被乘数相加，即得到相乘结果。计算出互相关函数值后，即可根据函数的峰值计算得到电极的接触阻抗。在计算互相关函数值时，因采样数据通常较为微弱，可先不除以m序列的周期，最后计算得到接触阻抗时再除，以免因数据过小而损失精度。
步骤S260，判断分压后波形的幅值是否在合适的范围。
判断分压后波形的幅值是否在合适的范围，如判断为是，则继续执行步骤S230，若判定为否，则执行步骤S270。
步骤S270，调整DA转换模块输出的m序列模拟波形的幅值。
电生理信号采集和接触阻抗测量过程中，电极与皮肤的接触阻抗可能会随时间推移而发生改变，导致分压后m序列模拟波形Uc(t)过大，干扰电生理信号的采集；或导致Uc(t)过小，无法被AD采样模块准确地采集。此时可由微控制器根据测得的接触阻抗大小，动态调整DA转换模块输出的m序列模拟波形Um(t)的幅值，使得分压后波形Uc(t)的幅值始终保持在合适的范围内。若无法将Uc(t)调整至合适范围，原因可能为电极脱落造成的接触阻抗过高，此时可由微控制器关闭此通道的电生理信号采集或发出信息提示使用人员检查电极连接情况。
若无需从AD采样数据中计算电极的接触阻抗，只需要采集的电生理信号数据。由于采样数据中包含的分压后m序列模拟波形Uc(t)幅值微弱，故可直接将AD采样数据视作电生理信号采集数据，无需进行类似于现有技术的高通滤波等处理。
需说明的是，在不违背本发明精神和范围的前提下，本领域技术人员可对上述实施例进行适当的改变或变型。例如，将m序列生成模块独立于微控制器，或者采用其他的分压电路替代电阻分压等。
综上所述，本发明通过计算AD采样数据与数字化m序列的互相关函数，进而得到电极的接触阻抗；使用m序列生成电路、DA转换模块和分压电路，动态生成幅值微弱的m序列模拟波形。相对于现有技术，本发明至少具有以下技术效果
1)、本发明将m序列作为电极接触阻抗测量的信号源，将本可能淹没在电生理信号和噪声能量密度之下的接触阻抗信息突出显示出来，而将电生理信号、噪声扩展到很宽的频带上，从而可将信号源的幅值设定得远低于电生理信号的幅值，使得电生理信号采集与电极接触阻抗测量可同时进行而互不干扰。
2)、本发明相对于现有电生理信号采集设备在硬件上仅增加DA转换模块和电阻，而m序列生成、阻抗测量等关键模块均可通过软件实现，因而相对于现有技术使用阻抗测量芯片或高速AD采样电路的方案，本发明降低了硬件成本、实现更方便。并且已通过仿真测试验证，原理和方法均可行。
本发明可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本发明的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、 可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本发明操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++、Python等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本发明的各个方面。
这里参照根据本发明实施例的方法、装置(系统)和计算机程序产品 的流程图和/或框图描述了本发明的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
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系统、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、程序段或指令的一部分，所述模块、程序段或指令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。在有些作为替换的实现中，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。对于本领域技术人员来说公知的是，通过硬件方式实现、通过软件方式实现以及通过软件和硬件结合的方式实现都是等价的。
以上已经描述了本发明的各实施例，上述说明是示例性的，并非穷尽 性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。本发明的范围由所附权利要求来限定。

Claims (10)

  1. 一种扩频调制的电极接触阻抗在线测量装置，包括：微控制器、m序列生成模块、DA转换模块、AD采样模块和分压电阻，其中：
    m序列生成模块用于生成数字化m序列；
    DA转换模块用于将数字化m序列转换为设定幅值和频率的m序列模拟波形；
    分压电阻用于对m序列模拟波形进行分压，且分压后的m序列模拟波形被注入到测量电生理信号的电极回路；
    AD采样模块用于采集包含分压后m序列模拟波形的电生理信号，获得采样数据；
    微控制器用于计算所述采样数据与所述数字化m序列的互相关函数，获得电极与测量目标的接触阻抗。
  2. 根据权利要求1所述的装置，其特征在于，所述数字化m序列利用多级线性反馈移位寄存器生成，在时钟信号的作用下，移位寄存器不断移位，并将输出通过设定的函数关系反馈给输入，进而产生码元速率和周期固定的数字化m序列。
  3. 根据权利要求1所述的装置，其特征在于，所述数字化m序列预先生成并存储于所述微处理器的闪存中，使用时通过查表得到待输出至所述DA转换模块的m序列当前值。
  4. 根据权利要求1所述的装置，其特征在于，所述微控制器采用以下公式计算采样数据与数字化m序列的互相关函数：
    Figure PCTCN2021137615-appb-100001
    其中，T为m序列的周期，Um(t)是数字化m序列，Uc(t)是分压后m序列模拟波形，Ue(t)是电生理信号，n(t)是噪声，Us(t)是采样数据，其叠加了分压后m序列模拟波形Uc(t)和电生理信号的P电极和N电极两端电压，Ue(t)是电生理信号。
  5. 根据权利要求4所述的装置，其特征在于，通过计算AD采样数据 与数字化m序列的互相关函数，获取函数的峰值，得到与测量目标的接触阻抗R1和R2两端的电压幅值，进而通过m序列模拟波形的幅值和分压电阻的阻值，计算出电极的接触阻抗。
  6. 根据权利要求3所述的装置，其特征在于，对于预先存储在微处理器中的数字化m序列，运算前需先对其补零至与采样数据长度相等，再将补零后的数字化m序列循环移位，移位步长为1，移位次数与采样数据的长度相同，然后计算每个移位后的数字化m序列与采样数据的内积，再除以m序列的周期，得到该移位下的互相关函数值。
  7. 根据权利要求1所述的装置，其特征在于，对于互相关函数的计算，在进行内积的乘法运算时，使用移位相加乘法器执行。
  8. 根据权利要求1所述的装置，其特征在于，所述微处理还用于执行：
    判断分压后波形的幅值是否在合适的范围；
    如判断为否，则调整所述DA转换模块输出的m序列模拟波形的幅值。
  9. 一种扩频调制的电极接触阻抗在线测量方法，包括以下步骤：
    生成数字化m序列；
    将所述数字化m序列转换为设定幅值和频率的m序列模拟波形；
    对所述m序列模拟波形进行分压，并将分压后的m序列模拟波形注入到电极回路，该电极回路用于对测量目标进行电生理信号检测；
    采集包含分压后m序列模拟波形的电生理信号，获得采样数据；
    计算所述采样数据与所述数字化m序列的互相关函数，获得电极与测量目标的接触阻抗。
  10. 一种计算机可读存储介质，其上存储有计算机程序，其中，该程序被处理器执行时实现根据权利要求9所述的方法的步骤。
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