CN113705354B - Mixed electrocardiosignal motion artifact removal method, system, equipment and readable storage medium - Google Patents
Mixed electrocardiosignal motion artifact removal method, system, equipment and readable storage medium Download PDFInfo
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
- G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
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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
- A61B5/24—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]
- A61B5/346—Analysis of electrocardiograms
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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
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
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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
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
Abstract
The invention discloses a method, a system, equipment and a readable storage medium for removing motion artifacts of a hybrid electrocardiosignal, which are characterized in that the method, the system and the equipment are used for carrying out drying pretreatment on an original electrocardiosignal to be processed, converting the pretreated electrocardiosignal into a corresponding data length required by continuous wavelet transformation, then carrying out continuous wavelet transformation, extracting and removing a QRS waveform signal from the electrocardiosignal after the continuous wavelet transformation based on waveform energy to obtain a signal to be processed; the method comprises the steps of carrying out multi-scale estimation on a signal to be processed to obtain a motion artifact signal, taking the motion artifact signal as a reference signal, carrying out self-adaptive filtering processing to obtain an artifact interference signal, superposing the artifact interference signal and an extracted QRS waveform signal to obtain a final signal after removing the motion artifact, and completing the removal of the motion artifact of the mixed electrocardiosignal.
Description
Technical Field
The present invention relates to a method for reducing noise in electrocardiograph signals, and in particular, to a method, a system, a device and a readable storage medium for removing motion artifacts in hybrid electrocardiograph signals.
Background
Cardiovascular disease has long been one of the major diseases threatening the health of humans, especially the elderly over 50 years old. The Chinese formally enters a medium aging stage, the cardiovascular diseases become more and more popular, and in addition, the urban life with high intensity and high pressure with fast rhythm also enables the occurrence of the cardiovascular diseases to be in a young state. At present, the number of patients suffering from cardiovascular diseases in China is 2.9 hundred million, which accounts for about 20% of the population of China, and the death rate of the cardiovascular diseases is still the first, which is far higher than that of tumors and other diseases, and the death rate is still growing.
Electrocardiographic (ECG) monitoring is used in most patients with cardiovascular disease clinically to detect and diagnose the disease cause and disease extent. However, conventional monitoring devices all require the patient to remain recumbent, limiting the patient's freedom of movement. The wearable electrocardiograph monitoring equipment on the market at present cannot cope with artifact interference caused by complex movement.
The electrocardiosignal is a weak bioelectric signal, and the main frequency band is concentrated in the interval of 0.2 Hz-100 Hz, so that a plurality of interferences exist in the measurement process, such as power frequency interference, baseline drift, myoelectric interference and the like, and the interference signals with relatively fixed frequency can be primarily filtered in a hardware filtering mode, but because motion artifact signals brought by human body motion are generally complex, the amplitude and the frequency are related to the human body motion mode, and therefore, a signal processing method is required to be further introduced for processing.
The signal processing method commonly used at present mainly comprises a regression method, an adaptive filtering method, a wavelet transformation method and the like, but the regression method is easy to produce overestimation and has the problem of bidirectional cross interference; the adaptive filtering method requires a given reference signal; the threshold value of each layer of the wavelet transformation method is complex to determine, so that the existing motion artifact removal method is poor in self-adaption capability, and can only aim at a specific motion artifact, so that the motion artifact interference caused by a complex motion mode of a human body is difficult to adapt.
Disclosure of Invention
The invention aims to provide a method, a system, a device and a readable storage medium for removing motion artifacts of a hybrid electrocardiosignal, so as to overcome the defects in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for removing motion artifacts of a hybrid electrocardiosignal comprises the following steps:
s1, calculating the data length of an original electrocardiosignal to be processed, and performing de-drying pretreatment on the original electrocardiosignal to be processed;
s2, converting the preprocessed electrocardiosignal into corresponding data length required by continuous wavelet transformation, and then carrying out continuous wavelet transformation;
s3, extracting and eliminating QRS waveform signals from the electrocardiosignals after continuous wavelet transformation based on waveform energy to obtain signals to be processed;
s4, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal;
and S5, superposing the artifact interference signal and the extracted QRS waveform signal to obtain a final signal after removing the motion artifact, and completing the removal of the motion artifact of the mixed electrocardiosignal.
Further, three-stage filtering treatment, gaussian smoothing treatment and median filtering are sequentially carried out on the original electrocardiosignal to be processed.
Further, three-stage filtering pretreatment is adopted for the original electrocardiosignal which is not subjected to hardware filtering treatment; the three-stage filtering comprises low-pass filtering, high-pass filtering and notch, and is used for filtering low-frequency baseline interference, high-frequency myoelectric interference and 50Hz power frequency interference on the frequency characteristic of the electrocardiosignal; the Gaussian smoothing process is used for removing high-frequency small-amplitude interference coupled with electrocardiosignal acquisition equipment; the median filtering is used for removing the pulse interference signal.
Further, the three-stage filtering sets a lower limit frequency of 0.1Hz, an upper limit frequency of 100Hz and a notch frequency of 50Hz.
Further, the data length needs to be divided by 2 when the signal is subjected to continuous wavelet transformation M Where M is the order of the continuous wavelet transform.
Further, extracting and rejecting QRS waveforms from continuous wavelet transformed electrocardiograph signals based on waveform energy is specifically:
s3.1, calculating and judging threshold T for each layer of continuous wavelet transformation 1 Greater than T 1 Primary position for QRS waveform;
s3.2, calculating the wave energy E (n) of the QRS waveform over a given length according toThe sampling frequency partitions each layer of the continuous wavelet transformation to obtain the maximum energy E of each partition of each layer max ；
S3.3, transforming each layer of the continuous wavelet according to the maximum energy E of each partition of each layer max Calculating a judgment threshold T 2 Is greater than the judgment threshold T 2 Is the final location of the QRS waveform; setting the coefficients of other positions of each layer of continuous wavelet transformation to zero, and performing continuous wavelet inverse transformation to obtain a QRS waveform signal y qrs And rejecting QRS waveform signal y qrs Is to be processed of the signal y to be processed 0 。
And S4, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing self-adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal r.
Further, the signal y to be processed 0 Performing continuous wavelet transformation and obtaining motion artifact signal r by adopting soft threshold method 0 Motion artifact signal r 0 As a reference signal, performing adaptive recursive least square filtering to obtain a final artifact interference signal r.
A hybrid electrocardiosignal motion artifact removal system comprises a preprocessing module, a waveform extraction module and an artifact removal module;
the preprocessing module is used for calculating the data length of the original electrocardiosignal to be processed, performing de-drying preprocessing on the original electrocardiosignal to be processed, converting the preprocessed electrocardiosignal into the corresponding data length required by continuous wavelet transformation according to the data length of the original electrocardiosignal, and then performing continuous wavelet transformation;
the waveform extraction module is used for extracting and removing the QRS waveform signal from the electrocardiosignal after continuous wavelet transformation based on waveform energy to obtain a signal to be processed, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing self-adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal;
the artifact removing module is used for superposing the artifact interference signal and the extracted QRS waveform signal to obtain a final signal with motion artifacts removed and outputting the final signal.
A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above hybrid electrocardiographic signal motion artifact removal method when executing the computer program.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above hybrid electrocardiographic signal motion artifact removal method.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention relates to a mixed electrocardiosignal motion artifact removing method, which comprises the steps of performing drying pretreatment on an original electrocardiosignal to be processed, converting the pretreated electrocardiosignal into a corresponding data length required by continuous wavelet transformation, then performing continuous wavelet transformation, extracting and removing a QRS waveform signal from the electrocardiosignal after the continuous wavelet transformation based on waveform energy to obtain a signal to be processed; the method combines the advantages that the wavelet transformation does not need to set an artifact reference signal and the adaptive filtering does not need to set a threshold parameter, and greatly improves the self-adaptability of the hybrid method to a complex motion mode of a human body.
Drawings
Fig. 1 is a flowchart of a method for removing motion artifacts of a hybrid electrocardiograph signal according to an embodiment of the present invention.
Figure 2 is an example result of removing motion artifacts from rapid lifting of the right upper limb in an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures:
as shown in FIG. 1, the method for removing motion artifacts of the hybrid electrocardiosignal combines the advantages of no need of setting artifact reference signals in wavelet transformation and no need of setting threshold parameters in adaptive filtering based on continuous wavelet transformation and adaptive filtering theory, greatly improves the adaptability of the hybrid method to complex motion modes of human bodies, and specifically comprises the following steps:
s1, acquiring an original electrocardiosignal to be processed, calculating the data length of the original electrocardiosignal to be processed, and performing de-drying pretreatment on the original electrocardiosignal to be processed;
specifically, three-stage filtering treatment, gaussian smoothing treatment and median filtering are sequentially carried out on the original electrocardiosignal to be treated.
Specifically, three-stage filtering pretreatment is adopted for the original electrocardiosignal which is not subjected to hardware filtering treatment; the three-stage filtering includes low-pass filtering, high-pass filtering, and notch. The three-stage filtering is used for filtering low-frequency baseline interference, high-frequency myoelectricity interference and 50Hz power frequency interference on the electrocardiosignal frequency characteristic, and setting the lower limit frequency of 0.1Hz, the upper limit frequency of 100Hz and the notch frequency of 50Hz. The Gaussian smoothing process is used for removing high-frequency small-amplitude interference coupled with the electrocardiosignal acquisition equipment. The median filtering is used for removing the pulse interference signal.
S2, converting the preprocessed electrocardiosignal into corresponding data length required by continuous wavelet transformation, and then carrying out continuous wavelet transformation; the corresponding length of data conversion is specifically as follows: the data length needs to be divided by 2 when the signal is subjected to continuous wavelet transformation M Where M is the order of the continuous wavelet transform.
S3, extracting and eliminating QRS waveform signals from the electrocardiosignals after continuous wavelet transformation based on waveform energy to obtain signals y to be processed 0 ；
Specifically, extracting and rejecting QRS waveforms from continuous wavelet transformed electrocardiograph signals based on waveform energy specifically includes:
s3.1, performing primary positioning of QRS waveforms: calculating and judging threshold T for each layer of median of continuous wavelet transformation 1 Greater than T 1 Primary position for QRS waveform;
s3.2, calculating wave energy and solving the maximum value of energy of each partition according to sampling frequency partition: computing QRS waveform inWave energy E (n) in a given length, dividing each layer of continuous wavelet transformation according to sampling frequency to obtain maximum energy E of each division of each layer max ；
S3.3, eliminating QRS waveforms: for each layer of continuous wavelet transformation, according to maximum energy E of each partition of each layer max Calculating a judgment threshold T 2 ，T 2 ＝median(E max )-c*1.4826*mad(E max ) C is an influence coefficient, which is greater than a judgment threshold T 2 Is the final location of the QRS waveform. Setting the coefficients of other positions of each layer of continuous wavelet transformation to zero, and performing continuous wavelet inverse transformation to obtain a QRS waveform signal y qrs And rejecting QRS waveform signal y qrs Is to be processed of the signal y to be processed 0 ；
S4, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal r;
specifically, the signal y to be processed 0 Performing continuous wavelet transformation and obtaining motion artifact signal r by adopting soft threshold method 0 Motion artifact signal r 0 As a reference signal, performing adaptive recursive least square filtering to obtain a final artifact interference signal r.
S5, mixing the artifact interference signal r with the extracted QRS waveform signal y qrs And (5) superposing to obtain a final signal after removing the motion artifact, and completing the removal of the motion artifact of the mixed electrocardiosignal.
In one embodiment of the present invention, there is provided a terminal device including a processor and a memory for storing a computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor adopts a Central Processing Unit (CPU), or adopts other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), ready-made programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and the like, which are a computation core and a control core of the terminal, and are suitable for realizing one or more instructions, in particular for loading and executing one or more instructions so as to realize corresponding method flows or corresponding functions; the processor provided by the embodiment of the invention can be used for the operation of the hybrid electrocardiosignal motion artifact removal method.
A hybrid electrocardiosignal motion artifact removal system comprises a preprocessing module, a waveform extraction module and an artifact removal module;
the preprocessing module is used for calculating the data length of the original electrocardiosignal to be processed, performing de-drying preprocessing on the original electrocardiosignal to be processed, converting the preprocessed electrocardiosignal into the corresponding data length required by continuous wavelet transformation according to the data length of the original electrocardiosignal, and then performing continuous wavelet transformation;
the waveform extraction module is used for extracting and removing the QRS waveform signal from the electrocardiosignal after continuous wavelet transformation based on waveform energy to obtain a signal to be processed, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing self-adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal;
the artifact removing module is used for superposing the artifact interference signal and the extracted QRS waveform signal to obtain a final signal with motion artifacts removed and outputting the final signal.
In still another embodiment of the present invention, a storage medium, specifically a computer readable storage medium (Memory), is a Memory device in a terminal device, for storing programs and data. The computer readable storage medium includes a built-in storage medium in the terminal device, provides a storage space, stores an operating system of the terminal, and may also include an extended storage medium supported by the terminal device. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium may be a high-speed RAM memory or a Non-volatile memory (Non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the above-described embodiments that may be used in a hybrid electrocardiographic signal motion artifact removal method.
As shown in fig. 2, the motion artifact schematic diagram caused by the rapid lifting of the right upper limb is removed by adopting the hybrid electrocardiosignal motion artifact removal method disclosed by the invention, so that the motion artifact can be effectively removed, and an accurate multi-motion result can be obtained. The invention calculates the electrocardiosignal data length to be processed; carrying out signal pretreatment; converting the preprocessed electrocardiosignal into corresponding data length required by continuous wavelet transformation, and performing continuous wavelet transformation; extracting and rejecting QRS waveforms based on waveform energy; estimating motion artifact signals in multiple scales; taking the motion artifact signal of continuous wavelet transformation as a reference signal to perform self-adaptive filtering processing; and superposing the processing result and the QRS waveform to obtain a final signal. The method combines the advantages that the wavelet transformation does not need to set an artifact reference signal and the adaptive filtering does not need to set a threshold parameter, thereby greatly improving the adaptability of the hybrid method to the complex motion mode of the human body.
Claims (7)
1. The method for removing the motion artifacts of the mixed electrocardiosignal is characterized by comprising the following steps of:
s1, calculating the data length of an original electrocardiosignal to be processed, and denoising pretreatment is carried out on the original electrocardiosignal to be processed;
s2, converting the preprocessed electrocardiosignal into corresponding data length required by continuous wavelet transformation, and then carrying out continuous wavelet transformation;
the data length needs to be divided by 2 when the signal is subjected to continuous wavelet transformation M Wherein M is the order of the continuous wavelet transform;
the extraction and elimination of the QRS waveform from the electrocardiosignal after continuous wavelet transformation based on waveform energy is specifically as follows:
s3.1, calculating and judging threshold T for each layer of continuous wavelet transformation 1 Greater than T 1 Primary position for QRS waveform;
s3.2, calculating the wave energy of the QRS waveform over a given lengthE(n)，Partitioning each layer of continuous wavelet transformation according to sampling frequency to obtain maximum energy of each partition of each layerE max ；
S3.3, transforming each layer of the continuous wavelet according to the maximum energy of each partition of each layerE max Calculating a judgment threshold T 2 Is greater than the judgment threshold T 2 Is the final location of the QRS waveform; setting the coefficients of other positions of each layer of continuous wavelet transformation to zero, and performing continuous wavelet inverse transformation to obtain QRS waveform signalsy qrs And rejecting QRS waveform signalsy qrs Is to be processed of the signal to be processed of (a)y 0 ；
Signals to be processedy 0 Performing continuous wavelet transformation and obtaining motion artifact signals by adopting soft threshold methodr 0 Motion artifact signalr 0 As a reference signal, performing adaptive recursive least square filtering to obtain a final artifact interference signalr；
S4, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signalr；
S3, extracting and eliminating QRS waveform signals from the electrocardiosignals after continuous wavelet transformation based on waveform energy to obtain signals to be processed;
s4, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal;
and S5, superposing the artifact interference signal and the extracted QRS waveform signal to obtain a final signal after removing the motion artifact, and completing the removal of the motion artifact of the mixed electrocardiosignal.
2. The method for removing motion artifacts from a hybrid electrocardiographic signal according to claim 1, characterized in that the three-stage filtering process, the gaussian smoothing process and the median filtering process are sequentially performed on the original electrocardiographic signal to be processed.
3. The method for removing motion artifacts from a hybrid electrocardiographic signal according to claim 2, characterized in that three-stage filtering pretreatment is adopted for the original electrocardiographic signal which is not subjected to hardware filtering treatment; the three-stage filtering comprises low-pass filtering, high-pass filtering and notch, and is used for filtering low-frequency baseline interference, high-frequency myoelectric interference and 50Hz power frequency interference on the frequency characteristic of the electrocardiosignal; the Gaussian smoothing process is used for removing high-frequency small-amplitude interference coupled with electrocardiosignal acquisition equipment; the median filtering is used for removing the pulse interference signal.
4. A method for removing motion artifacts from a hybrid electrocardiographic signal according to claim 3, wherein the three-stage filtering sets a lower limit frequency of 0.1Hz, an upper limit frequency of 100Hz, and a notch frequency of 50Hz.
5. A hybrid electrocardiograph signal motion artifact removal system based on the hybrid electrocardiograph signal motion artifact removal method of claim 1, comprising a preprocessing module, a waveform extraction module, and an artifact removal module;
the preprocessing module is used for calculating the data length of the original electrocardiosignal to be processed, denoising and preprocessing the original electrocardiosignal to be processed, converting the preprocessed electrocardiosignal into the corresponding data length required by continuous wavelet transformation according to the data length of the original electrocardiosignal, and then carrying out continuous wavelet transformation;
the waveform extraction module is used for extracting and removing the QRS waveform signal from the electrocardiosignal after continuous wavelet transformation based on waveform energy to obtain a signal to be processed, performing multi-scale estimation on the signal to be processed to obtain a motion artifact signal, and performing self-adaptive filtering processing on the motion artifact signal serving as a reference signal to obtain an artifact interference signal;
the artifact removing module is used for superposing the artifact interference signal and the extracted QRS waveform signal to obtain a final signal with motion artifacts removed and outputting the final signal.
6. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 4 when the computer program is executed by the processor.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 4.
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