WO2019047165A1 - 滤除工频干扰信号的控制方法及系统 - Google Patents

滤除工频干扰信号的控制方法及系统 Download PDF

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WO2019047165A1
WO2019047165A1 PCT/CN2017/101065 CN2017101065W WO2019047165A1 WO 2019047165 A1 WO2019047165 A1 WO 2019047165A1 CN 2017101065 W CN2017101065 W CN 2017101065W WO 2019047165 A1 WO2019047165 A1 WO 2019047165A1
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signal
frequency interference
power frequency
amplitude
interference signal
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PCT/CN2017/101065
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English (en)
French (fr)
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张通胜
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曼森伯格（深圳）科技发展有限公司
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Priority to US16/645,311 priority Critical patent/US11540763B2/en
Priority to JP2020535280A priority patent/JP7069320B2/ja
Priority to PCT/CN2017/101065 priority patent/WO2019047165A1/zh
Priority to AU2017430855A priority patent/AU2017430855B2/en
Priority to EP17924386.0A priority patent/EP3679860B1/en
Publication of WO2019047165A1 publication Critical patent/WO2019047165A1/zh

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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
    • A61B5/72—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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • 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
    • A61B5/7217—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise originating from a therapeutic or surgical apparatus, e.g. from a pacemaker
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R13/00—Arrangements for displaying electric variables or waveforms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
    • G01R29/26—Measuring noise figure; Measuring signal-to-noise ratio

Definitions

  • the solution belongs to the technical field of signal filtering, and particularly relates to a control method and system for filtering power frequency interference signals.
  • ECG measurement is a commonly used physiological information indicator in clinical practice.
  • the power frequency alternating current interference usually exists in the human body ECG signal detected by the hospital using the ECG detection equipment.
  • Complex electrical environments can cause significant interference, which can be identified as a sine wave with a frequency of 50/60 Hz and its higher harmonics, such as in Asia or Europe, where the frequency of domestic electricity is 50 Hz; in North America, This frequency is 60 Hz.
  • Conventional ECG frequencies range from 0.05 to 100 Hz
  • high frequency QRS waves have a frequency of 80 to 300 Hz
  • ventricular late potentials have frequency ranges up to 500 Hz. Therefore, for areas where 50 Hz AC is applied, the power frequency interference corresponding to the ECG measurement is from the fundamental wave to 2 times, 6 times up to the 10th harmonic. Therefore, power frequency interference is an important source of interference for ECG measurement and an obstacle to ECG analysis.
  • the frequency of the alternating current is not fixed, it always fluctuates above and below the nominal frequency, sometimes up to ⁇ 2 Hz.
  • ECG detection equipment is often equipped with filtering techniques such as notch filters as a basic option for instrument operators to filter out power frequency interference.
  • filtering techniques such as notch filters
  • the commonly used linear filtering technique will be difficult to reliably filter out the interference signal.
  • the notch method implemented by the band-stop technique often causes attenuation oscillation after a large impact disturbance to form a new interference.
  • FIG. 1 is a schematic diagram of a comparison of simulated waveforms of an electrocardiogram signal before and after filtering by a notch filter in the prior art, where the left side is a simulated waveform of an electrocardiogram signal that has not been filtered by a notch filter, and the right side is an electrocardiographic signal that is filtered by a notch filter. Simulate the waveform.
  • the large QRS wave acts as a large shock wave interference.
  • the ringing effect occurs when the steep QRS complex passes through the filter (the small oscillation behind the spike in the right figure is the ringing effect).
  • the ringing effect (the oscillating wave with a small amplitude) affects the observation of the corresponding part of the real ECG signal.
  • the narrower the bandwidth of the trap the more severe the ringing effect. Relaxing the bandwidth of the notch will lose more signals.
  • Such contradictions are particularly prominent in many applications.
  • the ST segment just after the end of the QRS wave has an amplitude of only 3 to 25 microvolts, and the frequency range is 40 to 500 Hz, covering the 10th harmonic of the 50 Hz alternating current frequency. Any trapping of the trap caused by the QRS wave will mask the true signal. Therefore, in view of the potential problem of the trap, in the international standard for measuring ventricular late potential, it is clearly stated that no filtering technique implemented by any hardware or software is allowed. Because the corresponding filtering technology can not be used to suppress power frequency interference, the clinical measurement application of ventricular late potential is also inconvenient.
  • Another method for filtering power frequency interference is also based on the interference signal provided by one or more reference channels.
  • the interference component corresponding to each signal channel is obtained by least squares calculation.
  • the former uses power frequency interference as a component, while the latter analyzes power frequency interference as an independent source to filter out power frequency interference.
  • the existing bandpass filter is used to filter the signal in the linear time period from the end of the previous QRS wave to the current QRS wave, to find the power frequency interference, and then Push to the subsequent time period corresponding to the current QRS wave and subtract it from the signal to eliminate power frequency interference.
  • the power frequency interference detected by the band pass filter also has a bandwidth problem. That is, a part of the signal is also lost as interference. If it is too wide, although it can adapt to changes in power frequency interference at any time, the corresponding signal loss is more; if it is too narrow, it cannot adapt to the change of interference frequency.
  • the band-pass filter is replaced with a thinning filter, which increases the complexity and the signal loss corresponding to the higher harmonic frequency is more Big.
  • the existing signal filtering techniques for eliminating power frequency interference have various defects as described above, resulting in signal distortion.
  • This type of distortion is particularly acute in high-frequency ECG analysis, such as high-frequency QRS analysis and ventricular late potential measurement.
  • the purpose of the solution is to provide a control method and system for filtering power frequency interference signals, aiming at solving the existing signal filtering technology, which has a ringing effect due to the use of the trap filter, resulting in signal distortion or even flooding signals.
  • the problem More generally, the solution disclosed herein solves the shortcomings of various existing notch techniques, especially for the measurement of ventricular late potential and the analysis of high frequency QRS waves, and provides a new technique for band rejection filtering.
  • the first aspect of the solution provides a control method for filtering a power frequency interference signal, and the control method includes:
  • parameters of the power frequency interference signal in the ECG signal of the rectified preset segment including frequency, amplitude, and phase;
  • the second aspect of the present invention provides a control system for filtering a power frequency interference signal, the control system comprising:
  • a rectifier module configured to rectify an initial ECG signal of the preset segment
  • a parameter obtaining module configured to acquire a parameter of a power frequency interference signal in the rectified ECG signal, where the parameter includes a frequency Rate, amplitude and phase;
  • a sine wave building module configured to construct a sine wave according to the frequency, the amplitude, and the phase
  • And filtering a module, configured to subtract the preset segment of the initial ECG signal from the sine wave signal, and output a waveform signal.
  • a third aspect of the present invention provides an electrocardiographic signal measuring apparatus comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the processor executes The steps of the above control method are implemented when the computer program is described.
  • a fourth aspect of the present invention provides a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the above control method.
  • the present invention provides a control method and system for filtering a power frequency interference signal, the control method comprising: first rectifying an initial ECG signal of a preset section of each channel; and then acquiring a rectified preset section The frequency, amplitude and phase of the power frequency interference signal in the ECG signal, which is obtained by weighting the signal-to-noise ratio of each channel to obtain the optimal power frequency interference signal of the system and estimating the frequency and phase, and constructing the sine according to the amplitude of each channel. Wave; finally, the preset segment of the initial ECG signal is subtracted from the sine wave signal to output a waveform signal.
  • FIG. 1 is a schematic diagram of comparison of simulated waveforms of ECG signals before and after filtering by using a trap filter in the prior art.
  • FIG. 2 is a flow chart of steps of a method for controlling a power frequency interference signal according to an embodiment of the present disclosure.
  • FIG. 3 is a schematic structural diagram of a module of a control system for filtering a power frequency interference signal according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic diagram of an electrocardiographic signal measuring device provided by an embodiment of the present solution.
  • the present invention provides a control method and a control system for filtering a power frequency interference signal, the control method comprising: first segmenting each channel of the multi-channel ECG signal with a QRS wave as a center, and rectifying the segmented signal Such nonlinear processing doubles the frequency of the original power frequency interference signal, for example, 50 Hz to 100 Hz, thereby improving the accuracy of detecting the interference frequency; and then obtaining the ECG of each channel of the rectified preset section.
  • the frequency, amplitude and phase of the power frequency interference signal in the signal are combined to find the best frequency and phase value, and the sine wave is constructed according to the frequency, amplitude and phase; finally, the preset segment (signal) of the initial ECG signal is The sine wave signal constructed by the channel is subtracted, Output waveform signal. Thereby, the effect of filtering out the power frequency interference signal is achieved, and the ringing effect is not generated, so that the measurement is more accurate.
  • the word signal not only represents the signal in the usual sense, but also represents the power frequency interference to be extracted.
  • FIG. 2 is a flow chart of a method for controlling a power frequency interference signal according to an embodiment of the present disclosure. For convenience of description, only parts related to the embodiment of the present solution are shown, which are as follows:
  • control method for filtering a power frequency interference signal comprising the following steps:
  • the initial ECG signal is the ECG signal of the human body detected by the ECG detection device. Since the initial ECG signal is rectified, the frequency (the fundamental wave and all harmonics) of the original power frequency interference signal is raised by one. Times, for example, for a 50 Hz fundamental frequency power frequency interference signal, the frequency becomes 100 Hz after rectification. Segmented signals of the same length of time contain twice as many periodic signals (including power frequency interference signals). Previous studies have shown that the higher the frequency, the more cycles are included in the same time period, which increases the accuracy of the estimated frequency.
  • the frequency, amplitude and phase of the power frequency interference signal in the ECG signal of the rectified preset segment are obtained based on the RAW-STEM method.
  • the frequency is divided by 2 to obtain the estimated power frequency interference of the current channel in the current time period.
  • the frequency value of the signal is obtained.
  • the standard deviation SD of the preset segment data is calculated.
  • the ratio of the amplitude of the preset segment data extraction to the standard deviation is defined as the signal-to-noise ratio of the channel, from which the accuracy of the estimated frequency, amplitude, and phase can be determined. The higher the ratio, the more accurate the estimated parameters.
  • all the channels are a database composed of power frequency interference signals of all preset segment data at all times, and the database is an estimated value of the above three parameters of frequency, amplitude and phase, wherein The exact reliability of the amplitude estimate is measured by the ratio of the amplitude estimate to the standard deviation of the channel during that time period.
  • the estimated frequencies in the best SNR are weighted and averaged, as described in equation (1), to further optimize the accuracy of the parameter estimation:
  • L is the number of channels with the highest signal-to-noise ratio selected in the initial ECG signal
  • an is the amplitude of the power-frequency interference signal of the nth channel estimated by the RAW-STEM method
  • SDn is the nth The standard deviation of the channel at the current time
  • fn is the frequency estimate of the power frequency interference signal of the current channel at the current time
  • fo is the current estimated frequency of the power frequency interference signal of all channels of the entire measurement system.
  • L 3 signals, and modern magnetoencephalography equipment, the number of channels will be as high as 306.
  • the number of channels is small, for example, the traditional ventricular late potential measurement has only three channels, then L goes to all channels.
  • the above formula implies the assumption that the higher the signal-to-noise ratio, the more reliable the estimated frequency, and therefore the higher the contribution to the estimate of the optimal frequency.
  • the weight is used to find the optimal frequency. For the background noise and physiological information, the estimation of the parameters of the power frequency interference signal always changes on both sides of the correct value. Therefore, such a weighted average further optimizes the accuracy of the parameter estimation.
  • the amplitude of each channel at each moment is a relatively complex physical quantity. Assuming that the spatial position of the interference source is unchanged relative to the multi-channel measurement system, then the ratio of the fundamental wave and any subharmonic amplitude of the power frequency interference signal observed by each channel remains unchanged. Therefore, select the time when the signal-to-noise ratio of all channels is the highest, and obtain the amplitude values of each channel at these moments, and then use the weighting method of formula (1), and fn replace the corresponding amplitude estimation values of each channel to calculate each channel. The ratio of the magnitude.
  • the amplitude of the remaining lower signal to noise ratio channels at that time can be found.
  • the QRS wave is replaced by the frequency corresponding to the time window as in the above formula (1), and the amplitude and phase are determined by the RAW-STEM method acting on the preset segment signal.
  • Amplitude-frequency spectral analysis ie, FFT
  • FFT Amplitude-frequency spectral analysis
  • the optimal amplitude estimation of the power frequency interference signal of the preset section is determined by the formula (2):
  • the waveform signal after filtering the power frequency interference signal can be obtained, thereby realizing filtering of the power frequency interference signal of the ECG signal. effect.
  • the method before the rectifying the ECG signal of the preset segment, the method further includes the following steps:
  • the ECG signals of each channel are segmented around each QRS wave, and each segment is defined as starting from the end of the previous QRS wave and starting from the beginning of the next QRS wave. Thus, except for the signal after the first QRS wavefront and the last QRS wave, the linear zone between all QRS waves is reused.
  • QRS wave refers to the largest amplitude group in normal ECG, reflecting the whole process of ventricular depolarization.
  • the normal ventricular depolarization begins in the middle of the interventricular septum and depolarizes from the left to the right, so the QRS complex first presents a small downward q wave.
  • the normal chest lead QRS complex is more constant.
  • the normal adult QRS group time is 0.06 ⁇ 0.10s, and the infants and young children are 0.04 ⁇ 0.08s, which gradually approach adulthood with age.
  • the principle of the above control method for filtering the power frequency interference signal is specifically: the first step is to replace the nonlinear QRS wave with the mean value of the segment signal, and find the three parameters of the sine wave and reconstruct the three parameters. a sine wave obtained in the step; in the second step, the sine wave reconstructed in the first step is cut corresponding to the portion of the QRS wave, and the QRS wave of the segment signal is replaced, and then the QRS wave is replaced.
  • the signal estimates the frequency, amplitude and phase of the power frequency interference it contains, and reconstructs the power frequency interference sine wave signal according to the new parameters just obtained, and defines the reconstructed interference signal as S2;
  • the third step is to reconstruct the previous step
  • the sine wave is cut corresponding to the interference signal of the QRS band to replace the signal being analyzed, and then analyzes three basic parameters of the power frequency interference sine wave included in the segment signal, namely frequency, amplitude and Phase, and reconstruct the power frequency interference sine wave with these three basic parameters, and define the reconstructed interference signal as S3; in the fourth step, first calculate the percentage change of the interference sine wave estimated by the above two steps, That is: RMS(S3-S2)/RMS(S2) ⁇ 100%, whether it is less than or equal to a certain preset value, for example, 0.1%.
  • the power frequency electrical interference is regarded as a kind of signal, and other components such as white noise and electrocardiographic signals are used as analysis of power frequency interference. noise.
  • Three parameters of the electrical interference sine wave (power frequency interference signal) are extracted: frequency, amplitude and phase to reconstruct the sine wave interference. The frequency varies from the fundamental (50hz in China) to all harmonic changes in the upper frequency range of the measurement range.
  • the above control method can be used as post-processing of the initial ECG signal, or can be solidified into a digital signal processing chip and directly applied to the measuring instrument for real-time analysis and processing; of course, it can be applied to biomedical multi-channel measurement. All fields such as EEG, magnetoencephalography, etc. can also be applied to all measurement and control fields involving power frequency electrical interference.
  • the above control method can be applied to the power frequency filtering replacement technology in the ECG monitoring instrument involved in the common electrocardiogram, or applied to the ventricular late potential examination, or applied to the high frequency QRS wave analysis technology.
  • FIG. 3 shows a module structure of a control system for filtering power frequency interference signals provided by an embodiment of the present solution. For convenience of description, only parts related to the embodiments of the present solution are shown, which are as follows:
  • the above control system for filtering power frequency interference signals includes:
  • the rectifier module 201 is configured to rectify the initial ECG signal of the preset segment.
  • the initial ECG signal is the ECG signal of the human body detected by the ECG detection device. Since the initial ECG signal is rectified, the frequency (the fundamental wave and all harmonics) of the original power frequency interference signal is raised by one. Times, for example, for a 50 Hz fundamental frequency power frequency interference signal, the frequency becomes 100 Hz after rectification.
  • the same segmentation signal contains twice as many periodic signals (including power frequency interference signals). Therefore, the higher the frequency, the more cycles are included in the same time period, which improves the accuracy of the estimated frequency.
  • the parameter obtaining module 202 is configured to acquire parameters of the power frequency interference signal in the ECG signal of the rectified preset segment, where the parameters include frequency, amplitude, and phase.
  • the frequency, amplitude and phase of the power frequency interference signal in the ECG signal of the rectified preset segment are obtained based on the RAW-STEM method.
  • the frequency is divided by 2 to obtain the estimated power frequency interference of the current channel in the current time period.
  • the frequency value of the signal is obtained.
  • the standard deviation SD of the preset segment data is calculated.
  • the ratio of the amplitude of the preset segment data extraction to the standard deviation is defined as the signal-to-noise ratio of the channel, and the accuracy of the estimated frequency, amplitude, and phase can be determined. The higher the ratio, the more accurate the estimated parameters.
  • the channels are a database composed of power frequency interference signals of all preset segment data at all times, and the database is an estimated value of the above three parameters of frequency, amplitude and phase, wherein The exact reliability of the amplitude estimate is measured by the ratio of the amplitude estimate to the standard deviation.
  • the estimated frequencies in the best SNR are weighted and averaged, as described in equation (1), to further optimize the accuracy of the parameter estimation:
  • L is the number of channels with the highest signal-to-noise ratio selected in the initial ECG signal
  • an is the amplitude of the power-frequency interference signal of the nth channel estimated by the RAW-STEM method
  • SDn is the nth The standard deviation of the channel at the current time
  • fn is the frequency estimate of the power frequency interference signal of the current channel at the current time
  • fo is the current estimated frequency of the power frequency interference signal of all channels of the entire measurement system.
  • L 3 signals, and modern magnetoencephalography equipment, the number of channels will be as high as 306.
  • the above formula implies the assumption that the channel with a high signal-to-noise ratio, the more reliable the estimated frequency, the higher the contribution to the estimation of the optimal frequency.
  • the weight is used to find the optimal frequency. For the background noise and physiological information, the estimation of the parameters of the power frequency interference signal always changes on both sides of the correct value. Therefore, such a weighted average further optimizes the accuracy of the parameter estimation.
  • the amplitude of each channel at each moment is a relatively complex physical quantity. Assuming that the spatial position of the interference source is unchanged relative to the multi-channel measurement system, then the ratio of the fundamental wave and any subharmonic amplitude of the power frequency interference signal observed by each channel remains unchanged. Therefore, select the time when the signal-to-noise ratio of all channels is the highest, and obtain the amplitude values of each channel at these moments, and then use the weighting method of formula (1), and fn replace the corresponding amplitude estimation values of each channel to calculate each channel. The ratio of the magnitude.
  • the amplitude of the remaining lower signal to noise ratio channels at that time can be found.
  • the QRS wave is replaced by the frequency corresponding to the time window as in the above formula (1), and the amplitude and phase are determined by the RAW-STEM method acting on the preset segment signal.
  • Amplitude-frequency spectral analysis ie, FFT
  • FFT Amplitude-frequency spectral analysis
  • the optimal amplitude estimation of the power frequency interference signal of the preset section is determined by the formula (2):
  • the sine wave construction module 203 is configured to construct a sine wave according to frequency, amplitude and phase.
  • a sine wave model is established to compare with the waveform of the initial ECG signal.
  • the filtering module 204 is configured to subtract the preset segment of the initial ECG signal from the sine wave signal to output a waveform signal.
  • the waveform signal after filtering the power frequency interference signal can be obtained, thereby realizing filtering of the power frequency interference signal of the ECG signal. effect.
  • the foregoing control system further includes:
  • the preset section dividing module is configured to select a plurality of preset sections in the initial ECG signal, and each preset section uses two adjacent QRS waves as reference objects, starting from the end of the first QRS wave, The beginning of the two QRS waves is defined for the endpoint.
  • QRS wave refers to the largest amplitude group in normal ECG, reflecting the whole process of ventricular depolarization.
  • the normal ventricular depolarization begins in the middle of the interventricular septum and depolarizes from the left to the right, so the QRS complex first presents a small downward q wave.
  • the normal chest lead QRS complex is more constant.
  • the normal adult QRS group time is 0.06 ⁇ 0.10s, and the infants and young children are 0.04 ⁇ 0.08s, which gradually approach adulthood with age.
  • the power frequency electrical interference is regarded as a kind of signal, and other components such as white noise, signal, and the like are used as noise. Extract three parameters of electrical interference sine wave (power frequency interference signal): frequency, amplitude and phase. The frequency varies from the fundamental (50hz in China) to all harmonic changes in the upper frequency range of the measurement range.
  • FIG. 4 shows the internal unit structure of the leakage protection chip U2 in the leakage protection circuit provided by the present solution. For the convenience of description, only the parts related to the embodiment of the present solution are shown, which are as follows:
  • the leakage protection chip U2 includes a power-on activation unit 1081, a zero-cross detection unit 1082, a high-power drive MOS unit 108, a field effect transistor 1084, a voltage sampling unit 1085, an oscillation circuit control unit 1086, and an abnormality protection.
  • the power-on starting unit 1081 is connected to the power terminal VCC for starting the operation of the entire leakage protection chip U2.
  • the zero-crossing detecting unit 1082 is connected to the voltage detecting terminal VS for making a zero-bit transition when the waveform is converted from the positive half cycle to the negative half cycle.
  • the high-power driving MOS unit 1083 is connected to the first reference terminal DRN1 and the second reference terminal DRN2 for driving the internal FET 1084 (shown by MOS in FIG. 3); the voltage sampling unit 1085 is connected to the current detecting terminal CS.
  • the oscillation circuit control unit 1086 is connected to the oscillation terminal CT for signal oscillation of the received electrical signal; the abnormal protection reset unit 1087 is connected to the reset terminal RST for resetting the leakage protection chip U2.
  • the grounding detection loop unit 1088 is connected to the ground GND for detecting the ground loop; the over-temperature protection unit 1089 is connected to the power-on starting unit 1081 for over-temperature protection; and the NAND gate control logic unit 1090 and zero-crossing detecting unit 1082, voltage sampling unit 1085 and oscillation loop control unit 1086 for performing logical processing on electrical signals; driving unit 1091 and NAND gate System logic unit 1090, unit 1087 and the abnormality Protection reset FET 1084 is connected, for outputting a drive signal to the FET 1084.
  • FIG. 4 is a schematic diagram of an electrocardiographic signal measuring device provided by an embodiment of the present solution.
  • the electrocardiographic signal measuring apparatus 6 of this embodiment includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and operable on the processor 60, such as an electrocardiogram. Signal processing program.
  • the processor 60 executes the computer program 62, the steps in the above various control method embodiments are implemented, such as steps S101 to S104 shown in FIG. 2.
  • the processor 60 executes the computer program 62, the functions of the modules/units in the above various device embodiments are implemented, such as the functions of the modules 201 to 204 shown in FIG.
  • the computer program 62 can be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to complete This program.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing a particular function for describing the execution of the computer program 62 in the ECG signal measuring device 6.
  • the computer program 62 can be divided into a synchronization module, a summary module, an acquisition module, and a return module (modules in a virtual device), and the specific functions of each module are as follows:
  • the ECG signal measuring device 6 may be a computing device such as a desktop computer, a notebook, a palmtop computer, or a cloud server.
  • the ECG signal measuring device may include, but is not limited to, a processor 60, a memory 61. It will be understood by those skilled in the art that FIG. 4 is merely an example of the electrocardiographic signal measuring device 6, and does not constitute a limitation on the electrocardiographic signal measuring device 6, and may include more or less components than those illustrated, or may combine some
  • the components, or different components, such as the ECG signal measuring device may also include input and output devices, network access devices, buses, and the like.
  • the processor 60 may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 61 may be an internal storage unit of the electrocardiographic signal measuring device 6, such as a hard disk or a memory of the electrocardiographic signal measuring device 6.
  • the memory 61 may also be an external storage device of the electrocardiographic signal measuring device 6, such as a plug-in hard disk equipped with the ECG signal measuring device 6, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • SMC smart memory card
  • secure digital device Secure Digital, SD
  • the memory 61 may also include both an internal storage unit of the ECG signal measuring device 6 and an external storage device.
  • the memory 61 is used to store the computer program and other programs and data required by the ECG signal measuring device.
  • the memory 61 can also be used to temporarily store data that has been output or is about to be output.
  • the embodiment of the present invention provides a control method and system for filtering a power frequency interference signal, where the control method includes: first rectifying an initial ECG signal of a preset segment of each channel; The frequency, amplitude and phase of the power frequency interference signal in the rectified preset segment of the ECG signal, which is obtained by weighting the signal-to-noise ratio of each channel to obtain the optimal power frequency interference signal of the system and estimating the frequency and phase, and combining The amplitude of each channel is constructed as a sine wave; finally, the preset segment of the initial ECG signal is subtracted from the sine wave signal to output a waveform signal.
  • each functional unit and module in the foregoing system may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be implemented by hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application.
  • the disclosed device/terminal device and method may be implemented in other manners.
  • the device/terminal device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division.
  • there may be another division manner for example, multiple units.
  • components may be combined or integrated into another system, or some features may be omitted or not performed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.
  • 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, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present solution may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present solution implements all or part of the processes in the foregoing embodiment, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented when the program is executed by the processor. .
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
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Abstract

一种滤除工频干扰信号的控制方法及系统，该控制方法包括：首先对每个通道的预设区段的心电信号进行整流；接着获取整流后的预设区段的心电信号中工频干扰信号的频率、幅度以及相位，其根据每个通道信噪比加权求得系统的最优工频干扰信号并估计频率和相位，以及结合每个通道的幅度构建正弦波；最后将心电信号的预设区段与重建的正弦波信号做相减处理，从而获得滤除工频干扰的心电波形信号。由此达到了滤除工频干扰信号的效果，且不产生振铃效应，使得测量更为精准，因此解决了现有的信号滤除技术存在着因采用陷波器滤波会产生振铃效应，导致信号失真的问题。

Description

滤除工频干扰信号的控制方法及系统 技术领域
本方案属于信号滤除技术领域，特别是涉及一种滤除工频干扰信号的控制方法及系统。
背景技术
心电测量是临床上常用的生理信息指标。医院采用心电检测设备所检测到的人体心电信号中通常存在工频交流电干扰。复杂的用电环境会引起明显的干扰，该种干扰可认定为频率50/60Hz的正弦波以及其高次谐波，例如在亚洲或者欧洲，生活用电的频率为50Hz；而在北美地区，该频率则为60Hz。常规的心电频率范围在0.05到100赫兹之间，高频QRS波的频率是80到300Hz，而心室晚电位的频率范围则高达500Hz。因此，对应用50Hz交流电的地区，心电测量对应的工频干扰，分别从基波高到2次、6次直至10次谐波。因此，工频干扰是心电测量一个重要干扰来源和心电分析的障碍。特别是，交流电的频率并不是固定不变的，它总是在标称频率上下波动，有时高达±2Hz.
在实际应用中，心电检测设备常装备陷波器等滤波技术，作为仪器操作者的一个基本选项，来滤除工频电干扰。但是，如果所处环境带来大的非平稳的冲击干扰，常用的线性滤波技术将难以可靠滤除干扰信号。例如，利用带阻技术实现的陷波方法，经常会在大的冲击干扰后引起衰减振荡，形成新的干扰。图1是现有技术中采用陷波器滤波前后心电信号仿真波形的对比的示意图，左边是未通过陷波器滤波的心电信号仿真波形，右边是通过陷波器滤波后的心电信号仿真波形。这里，大的QRS波起到了大的冲击波干扰的作用。由此可得，陡峭的QRS复合波通过滤波器后，会发生振铃效应(右图中尖峰后面的微小振荡即为振铃效应)。而振铃效应(幅度较小的振荡波)会影响了对相应部分真实心电信号的观测。
特别是，在上述图1中，陷波器的带宽越窄，振铃效应越严重。而放宽陷波器的带宽，则会损失更多的信号。这样的矛盾，在很多应用中，尤为突出。例如，对于反映心肌梗塞的心室晚电位，恰好位于QRS波结束后的ST段，其幅度只有3～25微伏，而频率范围为40～500Hz，涵盖到50Hz交流电频率的10次谐波。任何因QRS波引起的陷波器振铃都会掩盖真实的信号。因此，鉴于陷波器的潜在问题，在国际测量心室晚电位的标准中，明确规定，不许使用任何硬件或者软件实现的滤波技术。由于不能采用相应滤波技术来抑制工频干扰，心室晚电位的临床测量应用也受到诸多不便。
为了克服常用带阻滤波器在信号或者干扰幅度突变时引起的振铃现象，发展了很多新的方法来改善滤波器的性能。其中一大类技术是应用自适应滤波方法来实现窄带阻陷波。该方法需要一道专用的信号通道来采集工频干扰，为自适应滤波器提供参考信号。虽然也 有采用仪器内部产生的参考信号来实现自适应滤波的方法，但是，自适应滤波技术在干扰变化时，总存在一个滞后的时间适应过程。这是技术本身的问题。
另外一类滤除工频干扰的方法也是基于一路或多路参考通道提供的干扰信号，利用交流电干扰与心电本身呈现的正交特性，通过最小二乘法计算来获取各个信号通道对应的干扰分量来滤除工频干扰。类似地，PCA和ICA方法，前者是将工频干扰作为一个分量，而后者是将工频干扰作为一个独立源来分析，以滤除工频干扰。
还有一大类实现窄带滤波的技术，是利用现有的带通滤波器，对前一个QRS波结束到目前QRS波开始前的线性时间段内的信号，进行滤波，找到工频干扰，然后外推到随后的当前QRS波对应的时间段，与信号相减，以消除工频干扰。尽管这样的方法能克服QRS波后的振铃现象，但是，这种方法有两个潜在的问题，第一，利用带通滤波器检查到的工频干扰，也存在带宽问题。即，也会将信号的一部分作为干扰而损失掉。如果太宽，尽管能适应工频干扰随时的变化，但是，对应的信号损失则越多；如果太窄，又不能适应干扰频率的变化。尤其是，当需要对工频干扰的高次谐波也进行抑制时，带通滤波器就要用疏状滤波器来替代，使其复杂性提高，而且高次谐波频率对应的信号损失更大。
因此，现有的消除工频干扰的信号滤除技术存在着上述各种各样的缺陷，从而导致信号失真的问题。该种失真在高频心电分析中尤显严重，例如高频QRS波分析和心室晚电位测量。
技术问题
本方案的目的在于提供一种滤除工频干扰信号的控制方法及系统，旨在解决现有的信号滤除技术存在着因采用陷波器滤波会产生振铃效应，导致信号失真甚至淹没信号的问题。更一般的说，这里披露的方案，解决现有各种陷波技术存在的缺陷，特别是为心室晚电位的测量和高频QRS波的分析，提供一种新的带阻滤波技术手段。
技术解决方案
本方案第一方面提供了一种滤除工频干扰信号的控制方法，所述控制方法包括：
对预设区段的初始心电信号进行整流；
获取整流后的预设区段的心电信号中工频干扰信号的参数，所述参数包括频率、幅度以及相位；
根据所述频率、所述幅度以及所述相位构建正弦波；
将所述初始心电信号的预设区段与所述正弦波信号做相减处理，输出波形信号。
本方案第二方面提供了一种滤除工频干扰信号的控制系统，所述控制系统包括：
整流模块，用于对预设区段的初始心电信号进行整流；
参数获取模块，用于获取整流后的心电信号中工频干扰信号的参数，所述参数包括频 率、幅度以及相位；
正弦波构建模块，用于根据所述频率、所述幅度以及所述相位构建正弦波；
滤除模块，用于将所述初始心电信号的预设区段与所述正弦波信号做相减处理，输出波形信号。
本方案第三方面提供了一种心电信号测量装置，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上述控制方法的步骤。
本方案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上述控制方法的步骤。
有益效果
本方案提供了一种滤除工频干扰信号的控制方法及系统，该控制方法包括：首先对每个通道的预设区段的初始心电信号进行整流；接着获取整流后的预设区段的心电信号中工频干扰信号的频率、幅度以及相位，其根据每个通道信噪比加权求得系统的最优工频干扰信号并估计频率和相位，以及结合每个通道的幅度构建正弦波；最后将初始心电信号的预设区段与正弦波信号做相减处理，输出波形信号。由此达到了滤除工频干扰信号的效果，且不产生振铃效应，使得测量更为精准，因此解决了现有的信号滤除技术存在着因采用陷波器滤波会产生振铃效应，导致信号失真的问题。
附图说明
图1为现有技术中采用陷波器滤波前后心电信号仿真波形的对比示意图。
图2为本方案实施例提供的一种滤除工频干扰信号的控制方法的步骤流程图。
图3为本方案实施例提供的一种滤除工频干扰信号的控制系统的模块结构示意图。
图4是本方案实施例提供的心电信号测量装置的示意图。
具体实施方式
为了使本方案要解决的技术问题、技术方案及有益效果更加清楚明白，以下结合附图及实施例，对本方案进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本方案，并不用于限定本方案。
本方案提供一种滤除工频干扰信号的控制方法及控制系统，该控制方法包括：首先对多通道心电信号的每一个通道以QRS波为中心进行分段，对分段的信号进行整流，这样的非线性处理使得原工频干扰信号的频率提高了一倍，例如50赫兹变成100赫兹，从而提高检测干扰频率的准确性；接着获取整流后的预设区段各个通道的心电信号中工频干扰信号的频率、幅度以及相位，再综合找到最佳频率和相位值，并根据频率、幅度以及相位构建正弦波；最后将初始心电信号的预设区段(信号)与各通道构建的正弦波信号做相减处理， 输出波形信号。由此达到了滤除工频干扰信号的效果，且不产生振铃效应，使得测量更为精准。在本方案的表述过程中，信号一词不仅代表通常意义上的信号，还代表要检测提取的工频干扰。
为了说明本方案所述的技术方案，下面通过具体实施例来进行说明。
图2示出了本方案实施例提供的一种滤除工频干扰信号的控制方法的步骤流程，为了便于说明，仅示出了与本方案实施例相关的部分，详述如下：
上述一种滤除工频干扰信号的控制方法，该控制方法包括以下步骤：
S101.对预设区段的初始心电信号进行整流。
初始心电信号为心电检测设备所检测到的人体的心电信号，由于对初始心电信号进行整流，则使得原本的工频干扰信号的频率(基波和所有谐波)升高了一倍，例如：对于50Hz基频的工频干扰信号，整流后其频率就变成了100Hz。同样时间长度的分段信号，就包含了多一倍的周期信号(也包括工频干扰信号)。以前的研究表明，频率越高，同样时间段内包含的周期就越多，从而提高了估算频率的准确性。
S102.获取整流后的预设区段的心电信号中工频干扰信号的参数，所述参数包括频率、幅度以及相位。
基于RAW-STEM方法获取整流后的预设区段的心电信号中工频干扰信号的频率、幅度以及相位，当然，该频率除以2，即得到估算的当前通道在当前时段的工频干扰信号的频率值。同时，计算出该预设区段数据的标准方差SD。该预设区段数据提取的幅度与标准方差之比定义为该通道的信噪比，据此可判断所估算的频率、幅度以及相位的准确性。其比值越高，所估算的参数越准确。
由于初始心电信号有多个通道，则所有通道在所有时刻即所有预设区段数据的工频干扰信号组成的数据库，该数据库就是上述频率、幅度以及相位三个参数的估计值，其中，幅度估计值的准确可靠性是用幅度估计值与该通道在该时段的标准方差的比值来衡量的。
利用提取的幅度与标准方差之比值，作为加权系数，对信噪比最好的数个通道中估算的频率进行加权平均，如公式(1)所述，从而进一步优化参数估算的准确性：
Figure PCTCN2017101065-appb-000001
式中，L是初始心电信号中所选出的具有最高信噪比的通道数，an是利用RAW-STEM方法估算出的第n个通道的工频干扰信号的幅度，SDn是第n个通道在当前时刻的标准方差，fn是当前通道在当前时刻的工频干扰信号的频率估算值，而fo则是整个测量系统所有通道工频干扰信号的当前估算频率。L∈3(心室晚电位测量)或者L∈12(传统心电测量)。 对于临床上的常规脑电图，L＝3道信号，而现代的脑磁图设备，该通道数将高达306个。当然，如果通道数较少例如传统的心室晚电位测量只有三个通道，那么，L就去所有通道数。
上述公式隐含了这样的假设：信噪比高的通道，其估算的频率越可靠，因而对最优频率的估计值贡献越高。用加权来求得最佳频率，对于背景噪声和生理信息对工频干扰信号的参数的估算，总是在正确值两侧变化。因此，这样的加权平均，进一步优化参数估算的准确性。
各个通道在每个时刻的幅度，是相对复杂的一个物理量。假设干扰源相对于多通道测量系统的空间位置不变，那么，各个通道所观察到的工频干扰信号的基波和任意次谐波的幅度，其比例保持不变。因此，选择所有通道信噪比最高的那些时刻，求得各个通道在该些时刻的幅度值，进而利用公式(1)的加权方式，fn代之以各个通道相应的幅度估计值，计算各个通道幅度的比例值。因此，在任意时刻，利用该比例关系和该时刻的一个或数个最高信噪比提供的幅度值，即可求出其余较低信噪比通道在该时刻的幅度。根据RAW-STEM的算法，将QRS波代之以该时间窗口对应的频率如上述公式(1)，而幅度和相位则是RAW-STEM方法作用于该预设区段信号来确定的。针对重建的信号，进行幅度-频率谱分析(即FFT)。然后，在该幅度-频率谱中找到对应公式(1)确定的频率值f_0。一般来说，f_0不一定恰好与分立的幅度-频率谱的频率点重合。设f_0相邻前后两点的幅度为a和b，其中a大于b，那么，可求得f_0与a、b两幅度中较大者的频率差Δbin。这样，该预设区段的工频干扰信号的最优幅度估计值为公式(2)所确定：
Figure PCTCN2017101065-appb-000002
而对前述各个通道的相位估计值，有两种可能，一是所有通道趋于一个值，二是所有通道的相位趋于两个相差180°的两个值(例如有些差动输入的放大通道和生物磁信号测量的情况)。不论是哪种情况，把公式(1)中的fn代之以趋于一致的各个通道的相位估计值，估算出准确的相位，其采用以下公式(3)：
Figure PCTCN2017101065-appb-000003
而整流后的预设区段的心电信号的初始相位
Figure PCTCN2017101065-appb-000004
由RAW-STEM算法确定，信噪比是由最新估算的幅度An和该段信号中QRS波被估算的波形替换后的方差SDn决定的。
根据上述求得的频率、幅度以及相位的公式进行重复估算，如果第i+1次估算的所有通道的幅度值中最大的变化和第i次估计相比，小于一个预选设置的值，例如0.1％，则结束该步骤的迭代运算。由此，得出的结果即最大化的接近最优数据，对整体滤除的方案起到精度性更高的效果。
S103.根据频率、幅度以及相位构建正弦波。
结合上述获取及估算得到的频率、幅度以及相位，重建工频干扰正弦波波型，以便与初始心电信号的波形进行对比。
S104.将初始心电信号的预设区段与重建的正弦波信号做相减处理，输出波形信号。
将初始心电信号的预设区段(信号)减去正弦波信号，即可得到滤除工频干扰信号后的波形信号，由此实现了对心电信号的工频干扰信号进行滤除的效果。
上述仅仅是对初始心电信号的其中一个预设区段进行说明，其原理可拓展到初始心电信号的全部区段。
作为本方案一实施例，上述对预设区段的心电信号进行整流之前还包括步骤：
S100.对各个通道的心电信号以每一个QRS波为中心进行分段，各段的定义为从前一次QRS波的结束为起点，下一个QRS波的开始为终点。如此，除过第一个QRS波前和最后一个QRS波后的信号，所有的QRS波之间的线性区局都得到重复使用。
其中，QRS波是指正常心电图中幅度最大的波群，反映心室除极的全过程。正常心室除极始于室间隔中部，自左向右方向除极，故QRS波群先呈现一个小向下的q波。正常胸导联QRS波群形态较恒定。正常成人QRS波群时间为0.06～0.10s，婴儿与幼童为0.04～0.08s，随年龄增长逐渐接近成人。
因此，上述滤除工频干扰信号的控制方法的原理具体为：第一步是将非线性的QRS波代之以该段信号的均值，求其所述的正弦波的三个参数并重建该步骤获得的正弦波；第二步，将第一步重建的正弦波对应于QRS波的部分切割下来，以其代替所述该段信号的QRS波，然后对所述替代了QRS波的这段信号估算其所包含的工频干扰的频率、幅度和相位，并依据刚得到的新的参数来重建工频干扰正弦波信号，定义该重建的干扰信号为S2；第三步，将前一步重建的正弦波对应于QRS波段的干扰信号切割下来，用以代替正在分析处理的这段信号，然后分析提取该段信号中所包含的工频干扰正弦波的三个基本参数，即频率、幅度和相位，并以这三个基本参数重建工频干扰正弦波，并定义该重建的干扰信号为S3；第四步，首先计算前述两步估算的干扰正弦波的变化的百分比，即：RMS(S3-S2)/RMS(S2)×100％，是否小于等于某一个预先设定的值例如0.1％。如果小于预先设置的值，即为收敛，结束该段信号的分析处理；如果不小于或等于预先设定的值，则重复前述步骤，直到满足收敛条件。对每一个心电通道的QRS分段做同样的迭代估算，从而获得所有心电 通道的工频个干扰正弦波参数。
作为本方案一实施例，针对分析提取工频电干扰，为叙述方便，将工频电干扰看作一种信号，其它分量例如：白噪声、心电信号等，都作为分析工频干扰时的噪声。提取电干扰正弦波(工频干扰信号)的三个参数：频率，幅度和相位从而重建正弦波干扰。其中频率从基波(在中国是50hz)到测量范围频率上限的所有谐波变化。
作为本方案一实施例，上述控制方法可作为初始心电信号的后处理，也可固化到数字信号处理芯片中直接应用于测量仪器进行实时分析处理；当然，可应用到生物医学多通道测量的所有领域例如：脑电图、脑磁图等，也可应用于到所有涉及到工频电干扰的测量控制领域。并且上述控制方法可应用于常用心电图中涉及的心电监护仪器中的工频滤波替代技术，或者应用于心室晚电位检查，更或者应用于高频QRS波分析技术当中。
图3示出了本方案实施例提供的一种滤除工频干扰信号的控制系统的模块结构，为了便于说明，仅示出了与本方案实施例相关的部分，详述如下：
上述一种滤除工频干扰信号的控制系统，包括：
整流模块201，用于对预设区段的初始心电信号进行整流。
初始心电信号为心电检测设备所检测到的人体的心电信号，由于对初始心电信号进行整流，则使得原本的工频干扰信号的频率(基波和所有谐波)升高了一倍，例如：对于50Hz基频的工频干扰信号，整流后其频率就变成了100Hz。同样的分段信号，就包含了多一倍的周期信号(也包括工频干扰信号)。因此，频率越高，同样时间段内包含的周期就越多，从而提高了估算频率的准确性。
参数获取模块202，用于获取整流后的预设区段的心电信号中工频干扰信号的参数，所述参数包括频率、幅度以及相位。
基于RAW-STEM方法获取整流后的预设区段的心电信号中工频干扰信号的频率、幅度以及相位，当然，该频率除以2，即得到估算的当前通道在当前时段的工频干扰信号的频率值。同时，计算出该预设区段数据的标准方差SD。该预设区段数据提取的幅度与标准方差之比定义为该通道的信噪比，可判断所估算的频率、幅度以及相位的准确性。其比值越高，所估算的参数越准确。
由于初始心电信号有多个通道，则所有通道在所有时刻即所有预设区段数据的工频干扰信号组成的数据库，该数据库就是上述频率、幅度以及相位三个参数的估计值，其中，幅度估计值的准确可靠性是用幅度估计值与标准方差的比值来衡量的。
利用提取的幅度与标准方差之比值，作为加权系数，对信噪比最好的数个通道中估算的频率进行加权平均，如公式(1)所述，从而进一步优化参数估算的准确性：
Figure PCTCN2017101065-appb-000005
式中，L是初始心电信号中所选出的具有最高信噪比的通道数，an是利用RAW-STEM方法估算出的第n个通道的工频干扰信号的幅度，SDn是第n个通道在当前时刻的标准方差，fn是当前通道在当前时刻的工频干扰信号的频率估算值，而fo则是整个测量系统所有通道工频干扰信号的当前估算频率。L∈3(心室晚电位测量)或者L∈12(传统心电测量)。对于临床上的常规脑电图，L＝3道信号，而现代的脑磁图设备，该通道数将高达306个。
上述公式隐含了这样的假设：信噪比高的通道，其估算的频率越可靠，因而对最优频率的估计贡献越高。用加权来求得最佳频率，对于背景噪声和生理信息对工频干扰信号的参数的估算，总是在正确值两侧变化。因此，这样的加权平均，进一步优化参数估算的准确性。
各个通道在每个时刻的幅度，是相对复杂的一个物理量。假设干扰源相对于多通道测量系统的空间位置不变，那么，各个通道所观察到的工频干扰信号的基波和任意次谐波的幅度，其比例保持不变。因此，选择所有通道信噪比最高的那些时刻，求得各个通道在该些时刻的幅度值，进而利用公式(1)的加权方式，fn代之以各个通道相应的幅度估计值，计算各个通道幅度的比例值。因此，在任意时刻，利用该比例关系和该时刻的一个或数个最高信噪比提供的幅度值，即可求出其余较低信噪比通道在该时刻的幅度。根据RAW-STEM的算法，将QRS波代之以该时间窗口对应的频率如上述公式(1)，而幅度和相位则是RAW-STEM方法作用于该预设区段信号来确定的。针对重建的信号，进行幅度-频率谱分析(即FFT)。然后，在该幅度-频率谱中找到对应公式(1)确定的频率值f_0。一般来说，f_0不一定恰好与分立的幅度-频率谱的频率点重合。设f_0相邻前后两点的幅度为a和b，其中a大于b，那么，可求得f_0与a、b两幅度中较大者的频率差Δbin。这样，该预设区段的工频干扰信号的最优幅度估计值为公式(2)所确定：
Figure PCTCN2017101065-appb-000006
而对前述各个通道的相位估计值，有两种可能，一是所有通道趋于一个值，二是所有通道的相位趋于两个相差180°的两个值。不论是哪种情况，把公式(1)中的fn代之以趋于一致的各个通道的相位估计值，估算出准确的相位，其采用以下公式(3)：
Figure PCTCN2017101065-appb-000007
而整流后的预设区段的心电信号的初始相位
Figure PCTCN2017101065-appb-000008
由RAW-STEM算法确定，信噪比是由最新估算的幅度An和该段信号中QRS波被估算的波形替换后的方差SDn决定的。
根据上述求得的频率、幅度以及相位的公式进行重复估算，如果第i+1次估算的所有通道的幅度值中最大的变化和第i次估计相比，小于一个预选设置的值，例如0.1％，则结束该步骤的迭代运算。由此，得出的结果即最大化的接近最优数据，对整体滤除的方案起到精度性更高的效果。
正弦波构建模块203，用于根据频率、幅度以及相位构建正弦波。
结合上述获取及估算得到的频率、幅度以及相位，建立正弦波模型，以便与初始心电信号的波形进行对比。
滤除模块204，用于将初始心电信号的预设区段与正弦波信号做相减处理，输出波形信号。
将初始心电信号的预设区段(信号)减去正弦波信号，即可得到滤除工频干扰信号后的波形信号，由此实现了对心电信号的工频干扰信号进行滤除的效果。
上述仅仅是对初始心电信号的其中一个预设区段进行说明，其原理可拓展到初始心电信号的全部区段。
作为本方案一实施例，上述控制系统还包括：
预设区段分设模块，用于在初始心电信号选取多个预设区段，各个预设区段以相邻两个QRS波为参照物，以第一个QRS波的结束为起点，第二个QRS波的开始为终点进行定义。
其中，QRS波是指正常心电图中幅度最大的波群，反映心室除极的全过程。正常心室除极始于室间隔中部，自左向右方向除极，故QRS波群先呈现一个小向下的q波。正常胸导联QRS波群形态较恒定。正常成人QRS波群时间为0.06～0.10s，婴儿与幼童为0.04～0.08s，随年龄增长逐渐接近成人。
作为本方案一实施例，针对测量的初始心电信号中，将工频电干扰看作一种信号，其它分量例如：白噪声、信号等，都作为噪声。提取电干扰正弦波(工频干扰信号)的三个参数：频率，幅度和相位。其中频率从基波(在中国是50hz)到测量范围频率上限的所有谐波变化。
图4示出了本方案提供的一种漏电保护电路中漏电保护芯片U2的内部单元结构，为了便于说明，仅示出了与本方案实施例相关的部分，详述如下：
作为本方案一实施例，上述漏电保护芯片U2包括上电启动单元1081、过零检测单元1082、大功率驱动MOS单元108、场效应管1084、电压采样单元1085、振荡回路控制单元1086、异常保护复位单元1087、接地检测回路单元1088、与非门控制逻辑单元1090、驱动单元1091以及过温保护单元1089；
上电启动单元1081接电源端VCC，用于启动整个漏电保护芯片U2的工作；过零检测单元1082接电压检测端VS，用于当波形从正半周向负半周转换时，经过零位时作出检测；大功率驱动MOS单元1083接第一参考端DRN1和第二参考端DRN2，用于驱动内部的场效应管1084(图3采用MOS表示)的通断；电压采样单元1085接电流检测端CS，用于对电压信号进行采样；振荡回路控制单元1086接振荡端CT，用于对接收的电信号进行信号振荡；异常保护复位单元1087接复位端RST，用于对漏电保护芯片U2进行复位操作；接地检测回路单元1088与接地端GND相连接，用于对接地回路进行检测；过温保护单元1089与上电启动单元1081相连接，用于起到过温保护作用；与非门控制逻辑单元1090与过零检测单元1082、电压采样单元1085以及振荡回路控制单元1086，用于对电信号进行逻辑处理；驱动单元1091与与非门控制逻辑单元1090、异常保护复位单元1087以及场效应管1084相连接，用于输出驱动信号给场效应管1084。
图4示出了本方案实施例提供的心电信号测量装置的示意图。如图4所示，该实施例的心电信号测量装置6包括：处理器60、存储器61以及存储在所述存储器61中并可在所述处理器60上运行的计算机程序62，例如心电信号处理程序。所述处理器60执行所述计算机程序62时实现上述各个控制方法实施例中的步骤，例如图2所示的步骤S101至S104。或者，所述处理器60执行所述计算机程序62时实现上述各装置实施例中各模块/单元的功能，例如图3所示模块201至204的功能。
示例性的，所述计算机程序62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61中，并由所述处理器60执行，以完成本方案。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62在所述心电信号测量装置6中的执行过程。例如，所述计算机程序62可以被分割成同步模块、汇总模块、获取模块、返回模块(虚拟装置中的模块)，各模块具体功能如下：
所述心电信号测量装置6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。所述心电信号测量装置可包括，但不仅限于，处理器60、存储器61。本领域技术人员可以理解，图4仅仅是心电信号测量装置6的示例，并不构成对心电信号测量装置6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，例如所述心电信号测量装置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60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61可以是所述心电信号测量装置6的内部存储单元，例如心电信号测量装置6的硬盘或内存。所述存储器61也可以是所述心电信号测量装置6的外部存储设备，例如所述心电信号测量装置6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61还可以既包括所述心电信号测量装置6的内部存储单元也包括外部存储设备。所述存储器61用于存储所述计算机程序以及所述心电信号测量装置所需的其他程序和数据。所述存储器61还可以用于暂时地存储已经输出或者将要输出的数据。
综上所述，本方案实施例提供了一种滤除工频干扰信号的控制方法及系统，该控制方法包括：首先对每个通道的预设区段的初始心电信号进行整流；接着获取整流后的预设区段的心电信号中工频干扰信号的频率、幅度以及相位，其根据每个通道信噪比加权求得系统的最优工频干扰信号并估计频率和相位，以及结合每个通道的幅度构建正弦波；最后将初始心电信号的预设区段与正弦波信号做相减处理，输出波形信号。由此达到了滤除工频干扰信号的效果，且不产生振铃效应，使得测量更为精准，因此解决了现有的信号滤除技术存在着因采用陷波器滤波会产生振铃效应，导致信号失真的问题。
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方案的范围。
在本方案所提供的实施例中，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，在本方案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，本方案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电载波信号和电信信号。
以上所述实施例仅用以说明本方案的技术方案，而非对其限制；尽管参照前述实施例对本方案进行了详细的说明，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方案各实施例技术方案的精神和范围，均应包含在本方案的保护范围之内。

Claims (12)

  1. 一种滤除工频干扰信号的控制方法，其特征在于，所述控制方法包括：
    对预设区段内的初始心电信号进行整流；
    获取整流后的预设区段的心电信号中工频干扰信号的参数，所述参数包括频率、幅度以及相位；
    根据所述频率、所述幅度以及所述相位构建正弦波；
    将所述心电信号的预设区段与所述正弦波信号做相减处理，输出心电波形信号。
  2. 如权利要求1所述的控制方法，其特征在于，所述对预设区段的心电信号进行整流之前还包括：
    在所述初始心电信号选取多个预设区段，各个所述预设区段以每一个QRS波为中心，其相邻两个QRS波为参照物，以前一个所述QRS波的结束为起点，后一个所述QRS波的开始为终点进行定义。
  3. 如权利要求1所述的控制方法，其特征在于，获取所述心电信号中工频干扰信号的最佳频率由以下公式确定：
    Figure PCTCN2017101065-appb-100001
    其中，L为所述初始心电信号的多个通道中所具有最高信噪比的通道数，an是估算出的第n个所述通道的工频干扰信号的幅度，SDn为第n个所述通道在当前时刻的标准方差，fn为当前所述通道在当前时刻的工频干扰信号的频率估算值，以及f_0则是所有所述通道的工频干扰信号的当前估算频率。
  4. 如权利要求3所述的控制方法，其特征在于，获取所述心电信号中各通道工频干扰信号的幅度由以下公式确定：
    Figure PCTCN2017101065-appb-100002
    其中，Δbin为设所述f_0相邻前后两点的幅度为a和b，由f_0与a、b两幅度中较大者的频率差。
  5. 如权利要求4所述的控制方法，其特征在于，获取所述心电信号中工频干扰信号的相位由以下公式确定：
    Figure PCTCN2017101065-appb-100003
    其中，
    Figure PCTCN2017101065-appb-100004
    为整流后的所述预设区段的心电信号的初始相位。
  6. 一种滤除工频干扰信号的控制系统，其特征在于，所述控制系统包括：
    整流模块，用于对预设区段的初始心电信号进行整流；
    参数获取模块，用于获取整流后的预设区段的心电信号中工频干扰信号的参数，所述参数包括频率、幅度以及相位；
    正弦波构建模块，用于根据所述频率、所述幅度以及所述相位构建正弦波；
    滤除模块，用于将所述初始心电信号的预设区段与所述正弦波信号做相减处理，输出波形信号。
  7. 如权利要求6所述的控制系统，其特征在于，所述控制系统还包括：
    预设区段分设模块，用于在所述初始心电信号选取多个预设区段，各个所述预设区段以每一个QRS波为中心，其相邻两个QRS波为参照物，以前一个所述QRS波的结束为起点，后一个所述QRS波的开始为终点进行定义。
  8. 如权利要求6所述的控制系统，其特征在于，所述参数获取模块中获取所述心电信号中工频干扰信号的频率由以下公式确定：
    Figure PCTCN2017101065-appb-100005
    其中，L为所述初始心电信号的多个通道中所具有最高信噪比的通道数，an是估算出的第n个所述通道的工频干扰信号的幅度，SDn为第n个所述通道在当前时刻的标准方差，fn为当前所述通道在当前时刻的工频干扰信号的频率估算值，以及f_0则是所有所述通道的工频干扰信号的当前估算频率。
  9. 如权利要求8所述的控制系统，其特征在于，所述参数获取模块中获取所述心电信号中工频干扰信号的幅度由以下公式确定：
    Figure PCTCN2017101065-appb-100006
    其中，Δbin为设所述f_0相邻前后两点的幅度为a和b，由f_0与a、b两幅度中较大者的频率差。
  10. 如权利要求9所述的控制系统，其特征在于，所述参数获取模块中获取所述心电信号中工频干扰信号的相位由以下公式确定：
    Figure PCTCN2017101065-appb-100007
    其中，
    Figure PCTCN2017101065-appb-100008
    为第n道信号在所述预设区段的工频干扰的的初始相位。
  11. 一种心电信号测量装置，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前述计算机程序，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5任一项所述控制方法的步骤。
  12. 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5任一项所述控制方法的步骤。
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112327234A (zh) * 2020-10-29 2021-02-05 厦门大学 可转位刀具切削动态信号的工频干扰高精度补偿方法
JP2021145987A (ja) * 2020-03-19 2021-09-27 株式会社リコー 雑音低減装置及び雑音低減方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115444436B (zh) * 2022-08-24 2024-10-11 上海诺诚电气股份有限公司 一种微弱表面肌电信号中的工频干扰消除方法
CN115603779B (zh) * 2022-10-08 2024-05-24 华北电力大学 基于改进分集拷贝的直流电力线载波抗干扰方法及装置
CN116148779B (zh) * 2023-04-19 2023-08-25 隔空（上海）智能科技有限公司 一种工频滤波方法、系统、存储介质及电子设备

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104783780A (zh) * 2015-04-13 2015-07-22 深圳市飞马与星月科技研究有限公司 心电信号除噪方法及装置
CN105790729A (zh) * 2016-03-23 2016-07-20 深圳市理邦精密仪器股份有限公司 一种使用czt和自适应滤波技术的工频滤波方法和装置
CN106125604A (zh) * 2016-06-28 2016-11-16 东华理工大学 一种心电信号预处理系统
CN106667439A (zh) * 2016-12-30 2017-05-17 包磊 一种心电信号的降噪方法及装置

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3552386A (en) 1968-12-23 1971-01-05 Hewlett Packard Co Arrhythmia detecting apparatus and method
JPH0630908A (ja) * 1992-03-27 1994-02-08 Nippon Koden Corp 微小心電図計測装置
US6351664B1 (en) * 1999-11-12 2002-02-26 Ge Medical Systems Information Technologies, Inc. Method of removing signal interference from sampled data and apparatus for effecting the same
US7894885B2 (en) * 2007-05-02 2011-02-22 Biosense Webster, Inc. Coherent signal rejection in ECG
JP5468346B2 (ja) * 2009-10-01 2014-04-09 有限会社メイヨー 生体電気信号中の交流電源雑音の除去装置
CN103040459B (zh) 2013-01-05 2014-06-04 西安交通大学 一种多通道微弱生理信息记录系统中工频干扰的高保真滤除方法
US9585621B2 (en) * 2013-03-25 2017-03-07 Iliya Mitov Technique for real-time removal of power line interference in ECG
WO2015171804A1 (en) * 2014-05-08 2015-11-12 Draeger Medical Systems, Inc. Detecting artifacts in a signal

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104783780A (zh) * 2015-04-13 2015-07-22 深圳市飞马与星月科技研究有限公司 心电信号除噪方法及装置
CN105790729A (zh) * 2016-03-23 2016-07-20 深圳市理邦精密仪器股份有限公司 一种使用czt和自适应滤波技术的工频滤波方法和装置
CN106125604A (zh) * 2016-06-28 2016-11-16 东华理工大学 一种心电信号预处理系统
CN106667439A (zh) * 2016-12-30 2017-05-17 包磊 一种心电信号的降噪方法及装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3679860A4 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021145987A (ja) * 2020-03-19 2021-09-27 株式会社リコー 雑音低減装置及び雑音低減方法
JP7439599B2 (ja) 2020-03-19 2024-02-28 株式会社リコー 情報処理装置及び雑音低減方法
CN112327234A (zh) * 2020-10-29 2021-02-05 厦门大学 可转位刀具切削动态信号的工频干扰高精度补偿方法

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