WO2017148451A1 - 一种基于平稳小波变换滤除肌电干扰的方法和系统 - Google Patents

一种基于平稳小波变换滤除肌电干扰的方法和系统 Download PDF

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WO2017148451A1
WO2017148451A1 PCT/CN2017/079424 CN2017079424W WO2017148451A1 WO 2017148451 A1 WO2017148451 A1 WO 2017148451A1 CN 2017079424 W CN2017079424 W CN 2017079424W WO 2017148451 A1 WO2017148451 A1 WO 2017148451A1
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coefficient matrix
swd
wavelet transform
detail coefficient
signal
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郑慧敏
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深圳竹信科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/10—Image enhancement or restoration using non-spatial domain filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20064—Wavelet transform [DWT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac

Definitions

  • the present invention relates to the field of processing of electrocardiographic signals, and more particularly to a method and system for filtering myoelectric interference based on stationary wavelet transform.
  • the ECG signals collected by biosensors contain various kinds of noise, including myoelectric interference, baseline drift and power frequency interference.
  • myoelectric interference is a more representative one.
  • Noise caused by human activity and muscle tremors, which can have a large impact on the accuracy of the extracted feature extraction of the ECG signal, which needs to be filtered out.
  • the object of the present invention is to provide a method and system for filtering myoelectric interference based on stationary wavelet transform, which uses a stationary wavelet transform and a preset threshold function to process the sampled original ECG signal, and filter out the myoelectric interference.
  • the useful signal is retained to the utmost, the calculation amount is small, the signal reduction degree is high, and the filtering effect is good.
  • the present invention provides a method for filtering myoelectric interference based on a stationary wavelet transform, comprising the following steps:
  • An inverse stationary wavelet transform is performed on the detail coefficient matrix swd' and the approximate coefficient matrix swa to obtain an electrocardiographic signal that filters out the myoelectric interference signal.
  • the preset threshold function is: Where X is an element of the detail coefficient matrix swd, Y is an element of the detail coefficient matrix swd', sgn is a sign function, ⁇ is a free factor, and ⁇ is a threshold.
  • threshold ⁇ is:
  • i is the number of rows of the detail coefficient matrix swd'
  • median is a median function
  • W ij is the element on the i-th row and the j-th column of the detail coefficient matrix swd'
  • N is the number of sampling points.
  • the value of the free factor ⁇ is 2.5.
  • the value of the sampling point N is 4096.
  • the performing stationary wavelet transform on the original ECG signal is specifically: performing 5-layer stationary wavelet transform on the original ECG signal by using a db4 wavelet base.
  • a stationary wavelet transform is performed on the original electrocardiographic signal according to the number of layers and the wavelet base.
  • the present invention provides a system for filtering myoelectric interference based on stationary wavelet transform, comprising:
  • An acquisition unit configured to acquire an original ECG signal
  • a wavelet transform unit configured to perform a stationary wavelet transform on the original electrocardiographic signal to obtain a detail coefficient matrix swd and an approximate coefficient matrix swa;
  • a coefficient adjustment unit configured to re-assign the detail coefficient matrix swd according to a preset threshold function, to obtain an assigned detail coefficient matrix swd';
  • An inverse transform unit is configured to perform an inverse stationary wavelet transform on the detail coefficient matrix swd′ and the approximate coefficient matrix swa to obtain an electrocardiographic signal that filters out the myoelectric interference signal.
  • the coefficient further includes:
  • a threshold function setting unit configured to set the preset threshold function, where the preset threshold function is: Where X is the element of the detail coefficient matrix swd, median is the median function, Y is the element of the detail coefficient matrix swd', ⁇ is the free factor, and ⁇ is the threshold.
  • system further comprises:
  • a free factor setting unit configured to set a value of the free factor ⁇ in the preset threshold function, where the value of ⁇ is 2.5;
  • a threshold setting unit configured to set a calculation formula of the threshold ⁇ in the preset threshold function, the threshold ⁇ is: Where i is the number of rows of the detail coefficient matrix swd', W ij is the element on the i-th row and the j-th column of the detail coefficient matrix swd', N is the number of sampling points, and the value of N is 4096.
  • system further comprises:
  • a transform layer number setting unit configured to set a layer number of the stationary wavelet transform, the number of layers is 5;
  • the wavelet base setting unit is configured to set a wavelet base of the stationary wavelet transform, and the wavelet base is db4.
  • the present invention relates to a method and system for filtering myoelectric interference based on stationary wavelet transform, comprising acquiring an original electrocardiographic signal, performing a stationary wavelet transform on the original electrocardiographic signal, and obtaining a detail coefficient matrix swd and an approximate coefficient matrix swa,
  • the detail coefficient matrix swd is re-executed according to a preset threshold function Assigning, obtaining the assigned detail coefficient matrix swd', performing an inverse stationary wavelet transform on the detail coefficient matrix swd' and the approximate coefficient matrix swa, and obtaining an electrocardiogram signal for filtering the myoelectric interference signal;
  • the present invention adopts a stationary wavelet transform and
  • the preset threshold function processes the original ECG signal sampled, and preserves the useful signal while filtering the myoelectric interference. The calculation amount is small, the signal reduction degree is high, and the filtering effect is good.
  • FIG. 1 is a flowchart of a method of a first embodiment of a method for filtering myoelectric interference based on stationary wavelet transform according to an embodiment of the present invention
  • FIG. 2 is a flowchart of a method of a second embodiment of a method for filtering myoelectric interference based on stationary wavelet transform according to an embodiment of the present invention
  • FIG. 3 is a structural block diagram of a first embodiment of a system for filtering myoelectric interference based on stationary wavelet transform according to an embodiment of the present invention
  • FIG. 4 is a block diagram showing the structure of a second embodiment of the system for filtering myoelectric interference based on stationary wavelet transform of the present invention.
  • FIG. 1 is a flow chart of a method of a first embodiment of a method for filtering myoelectric interference based on stationary wavelet transform according to the present invention.
  • the method comprises:
  • the ECG signal is a routine indicator for monitoring human health.
  • the EMG noise is a typical noise. Sound, myoelectric interference is caused by human activity and muscle fibrillation, the frequency is about 5Hz-2K Hz, and severe myoelectric interference is distributed at 10Hz-300HZ. Compared with the myoelectric interference noise, the frequency range of the ECG signal is 0.05HZ-100HZ, and most of the energy is concentrated between 0.25HZ-30HZ.
  • Wavelet transform has the advantages of time-frequency, asymptotic optimality and spatial adaptability, and has gradually become the mainstream algorithm for ECG signal noise processing.
  • the stationary wavelet transform has small computational complexity and good denoising effect.
  • the original ECG signal is decomposed by wavelet to obtain the detail coefficient matrix swd and the approximate coefficient matrix swa. Due to the frequency characteristics of the ECG signal and the EMG interference noise, the influence of the EMG interference noise on the signal after the stationary wavelet transform is mainly reflected in the signal.
  • the detail coefficient matrix swd the amplitude of the wavelet coefficients corresponding to the useful signals in the original ECG signal is large but the number is small, and the wavelet coefficients corresponding to the myoelectric interference noise are uniformly distributed, and the number is large but the amplitude is small.
  • each coefficient in swd is re-assigned according to a given threshold function, and the assigned coefficient matrix swd' is obtained.
  • the signal reconstruction of the detail coefficient matrix swd' obtained after the re-evaluation and the inverse transformation of the approximate coefficient matrix swa can obtain an ECG signal that filters out the myoelectric interference noise.
  • the method for filtering the myoelectric interference based on the stationary wavelet transform includes acquiring the original ECG signal, performing a stationary wavelet transform on the original ECG signal, and obtaining a detail coefficient matrix swd and an approximate coefficient matrix swa.
  • the detail coefficient matrix swd is re-assigned according to a preset threshold function, and a new detail coefficient matrix swd' is obtained, and the detail coefficient matrix swd' and the approximate coefficient matrix swa are subjected to inverse stationary wavelet transform, and the EMG interference signal is filtered out.
  • ECG signal the invention adopts stationary wavelet transform and preset threshold function to process the sampled original ECG signal, and removes the useful signal while filtering the myoelectric interference, the calculation amount is small, and the signal reduction degree is high. The filtering effect is good.
  • FIG. 2 is a flow chart of a method of a second embodiment of a method for filtering myoelectric interference based on stationary wavelet transform according to the present invention.
  • the method comprises:
  • Determining the number of layers of the stationary wavelet transform is 5 layers according to the sampling frequency of the original electrocardiographic signal, and selecting db4 as the wavelet base of the stationary wavelet transform according to the characteristics of the original electrocardiographic signal.
  • the above preset threshold function is: Where X is an element of the detail coefficient matrix swd, Y is an element of the detail coefficient matrix swd', ⁇ is a free factor, ⁇ is a value of 2.5, and sgn(X) is a sign function.
  • ⁇ is the threshold and its calculation formula is: i is the number of rows of the detail coefficient matrix swd', median(
  • the threshold ⁇ is a function of the variable i, and each row of the detail coefficient matrix swd′ corresponds to a threshold.
  • ) represents the median value of the element of the i-th row in the detail coefficient matrix swd′, where N is The number of sampling points, the value of N is 4096.
  • the value is re-assigned by the threshold function.
  • the threshold function curve is smoothly smoothed at the threshold while maximally maintaining the amplitude of the wavelet coefficients generated by the useful signal in the ECG signal.
  • x_filter iswt(swa, swd'), thereby obtaining an electrocardiographic signal that filters out myoelectric interference.
  • the scheme adopts an improved threshold function, which smoothes the transition at the threshold, while maximally maintaining the amplitude of the wavelet coefficients generated by the useful signal in the ECG signal, avoiding the hard threshold function not at the threshold Continuously causing a breakpoint generated by the signal during reconstruction, and a distortion phenomenon caused by the soft threshold function decreasing the threshold value in the amplitude, resulting in a distortion of the signal during reconstruction, such that the detail coefficient matrix swd' and the approximate coefficient obtained by the threshold function
  • the matrix swa performs signal reconstruction, it can filter the myoelectric interference signal to the greatest extent while retaining the useful signal in the ECG signal.
  • the present embodiment is based on the method of smoothing wavelet transform to filter myoelectric interference, selecting the appropriate wavelet transform layer number and wavelet basis to perform stationary wavelet transform based on the acquired characteristics of the original ECG signal, and obtaining the detail coefficient matrix swd and Approximating the coefficient matrix swa, re-assigning the detail coefficient matrix swd according to the improved threshold function, obtaining a new detail coefficient matrix swd', and then reconstructing the ECG signal by inverse transforming the detail coefficient matrix swd' and the approximate coefficient matrix swa;
  • the scheme selects the stationary wavelet transform to calculate a small amount, and the improved threshold function of the sampling processes the coefficient matrix of the wavelet transform.
  • the threshold function has the advantages of the soft threshold function and the hard threshold function, and the maximum degree of the EMG interference is filtered out. The useful signal is retained, the signal is highly restored, and the filtering effect is good.
  • FIG. 3 is a structural block diagram of a system for filtering myoelectric interference based on stationary wavelet transform of the present invention.
  • the system comprises:
  • the acquiring unit 01 is configured to acquire an original ECG signal
  • the original ECG signal of the human body is collected by the corresponding sensor, and then sent to a terminal such as a computer or a single chip microcomputer for signal processing.
  • the wavelet transform unit 02 is configured to perform a stationary wavelet transform on the original ECG signal to obtain a detail coefficient matrix swd and an approximate coefficient matrix swa;
  • the coefficient adjustment unit 03 is configured to re-assign the detail coefficient matrix swd according to a preset threshold function to obtain an assigned detail coefficient matrix swd';
  • the inverse transform unit 04 is configured to perform an inverse stationary wavelet transform on the detail coefficient matrix swd′ and the approximate coefficient matrix swa to obtain an ECG signal that filters out the myoelectric interference signal.
  • the system can be applied to wearable devices that monitor human health and can also be applied to medical devices.
  • the system for filtering the myoelectric interference based on the stationary wavelet transform is performed, and the original ECG signal is collected by the sensor, and the original ECG signal is subjected to stationary wavelet transform to obtain a detail coefficient matrix swd and an approximate coefficient matrix swa,
  • the detail coefficient matrix swd is re-assigned according to a preset threshold function to obtain a new detail coefficient matrix swd′, and the detailed coefficient wavelet swd′ and the approximate coefficient matrix swa are subjected to inverse stationary wavelet transform to obtain an EMG interference signal.
  • the ECG signal uses the stationary wavelet transform and the preset threshold function to process the sampled original ECG signal, while filtering the myoelectric interference while retaining the useful signal to the maximum, the calculation amount is small, the signal reduction degree High, good filtering effect.
  • Fig. 4 is a block diagram showing the structure of a system for filtering myoelectric interference based on stationary wavelet transform of the present invention.
  • system further includes:
  • the threshold function setting unit 05 is configured to set the preset threshold function, and the preset threshold function is: Where X is an element of the detail coefficient matrix swd, Y is an element of the detail coefficient matrix swd', ⁇ is a free factor, and ⁇ is a threshold.
  • the detail coefficient matrix swd embodies the amplitude characteristics of the myoelectric interference noise and the useful signal in the original ECG signal. Based on this, the improved threshold function is used to re-assign the detail coefficient matrix swd, and the wavelet coefficient corresponding to the useful signal is retained. The wavelet coefficient corresponding to the small myoelectric interference noise obtains a new detail coefficient matrix swd'.
  • a free factor setting unit 06 configured to set a value of the free factor ⁇ in the preset threshold function, where the value of ⁇ is 2.5;
  • the threshold setting unit 07 is configured to set a calculation formula of the threshold ⁇ in the preset threshold function, and the threshold ⁇ is: Where i is the number of rows of the detail coefficient matrix swd', median is a median function, W ij is the element on the i-th row and the j-th column of the detail coefficient matrix swd', N is the number of sampling points, and the value of N It is 4096.
  • a layer number setting unit 08 configured to set the number of layers of the stationary wavelet transform, the number of layers is 5;
  • the wavelet base setting unit 09 is configured to set a wavelet base of the stationary wavelet transform, and the wavelet base is db4.
  • the present embodiment is based on a stationary wavelet transform to filter out the myoelectric interference system.
  • an appropriate wavelet transform layer number and a wavelet basis are selected for stationary wavelet transform to obtain a detail coefficient matrix swd and Approximating the coefficient matrix swa, re-assigning the detail coefficient matrix swd according to the improved threshold function, obtaining a new detail coefficient matrix swd', and then passing the detail coefficient
  • the matrix swd' and the approximate coefficient matrix swa perform inverse transformation to reconstruct the ECG signal; the scheme selects the stationary wavelet transform to calculate a small amount, and simultaneously samples the improved threshold function to process the coefficient matrix of the wavelet transform, the threshold function has a soft threshold
  • the advantages of the function and the hard threshold function are to preserve the useful signal to the greatest extent while filtering the myoelectric interference, and the signal has a high degree of reduction and a good filtering effect.

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Abstract

一种基于平稳小波变换滤除肌电干扰的方法和系统，包括获取原始心电信号（S101），对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa（S102），对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd'（S103），对所述细节系数矩阵swd'和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号（S104）；采用平稳小波变换和预设的阈值函数对采样得到的原始心电信号进行处理，在滤除肌电干扰的同时最大限度的保留有用信号，计算量小，信号还原度高，滤波效果好。

Description

一种基于平稳小波变换滤除肌电干扰的方法和系统 技术领域
本发明涉及心电信号的处理领域，尤其涉及一种基于平稳小波变换滤除肌电干扰的方法和系统。
背景技术
在医学检测领域，通过生物传感器采集来的心电信号中包含各种各样的噪声，主要有肌电干扰、基线漂移和工频干扰等，其中，肌电干扰是比较有代表意义的一种噪声，由人体活动及肌肉颤动所引起，该噪声会对采样得到的心电信号的特征提取的准确度造成很大的影响，需要将其滤除。
发明内容
本发明的目的在于提出一种基于平稳小波变换滤除肌电干扰的方法和系统，采用平稳小波变换和预设的阈值函数对采样得到的原始心电信号进行处理，在滤除肌电干扰的同时最大限度的保留有用信号，计算量小，信号还原度高，滤波效果好。
为达此目的，本发明采用以下技术方案：
一方面，本发明提出一种基于平稳小波变换滤除肌电干扰的方法，包括如下步骤：
获取原始心电信号；
对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa；
对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
其中，所述预设的阈值函数为：
Figure PCTCN2017079424-appb-000001
其中，X为所述细节系数矩阵swd的元素，Y为所述细节系数矩阵swd′的元素，sgn为符号函数，α为自由因子，γ为阈值。
其中，所述阈值γ为：
Figure PCTCN2017079424-appb-000002
其中，i为所述细节系数矩阵swd′的行数，median为中值函数，Wij为所述细节系数矩阵swd′第i行第j列上的元素，N为采样点数。
其中，所述自由因子α的取值为2.5。
其中，所述采样点数N的取值为4096。
其中，对所述原始心电信号进行平稳小波变换具体为：采用db4小波基对所述原始心电信号进行5层平稳小波变换。
根据所述层数和所述小波基对所述原始心电信号进行平稳小波变换。
另一方面，本发明提出一种基于平稳小波变换滤除肌电干扰的系统，包括：
获取单元，用于获取原始心电信号；
小波变换单元，用于对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa；
系数调整单元，用于对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
逆变换单元，用于对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
其中，所述系数还包括：
阈值函数设置单元，用于设置所述预设的阈值函数，所述预设的阈值函数为：
Figure PCTCN2017079424-appb-000003
其中，X为所述细节系数矩阵swd的元素，median为中值函数，Y为所述细节系数矩阵swd′的元素，α为自由因子，γ为阈值。
其中，所述系统还包括：
自由因子设置单元，用于设置所述预设的阈值函数中自由因子α的取值，α的取值为2.5；
阈值设置单元，用于设置所述预设的阈值函数中阈值γ的计算公式，所述阈值γ为：
Figure PCTCN2017079424-appb-000004
其中，i为所述细节系数矩阵swd′的行数，Wij为所述细节系数矩阵swd′第i行第j列上的元素，N为采样点数，N的取值为4096。
其中，所述系统还包括：
变换层数设置单元，用于设置平稳小波变换的层数，所述层数为5；
小波基设置单元，用于设置平稳小波变换的小波基，所述小波基为db4。
本发明的技术方案带来的有益效果为：
本发明一种基于平稳小波变换滤除肌电干扰的方法和系统，包括获取原始心电信号，对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa，对所述细节系数矩阵swd按照预设的阈值函数重新 赋值，得到赋值后的细节系数矩阵swd′，对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号；本发明采用平稳小波变换和预设的阈值函数对采样得到的原始心电信号进行处理，在滤除肌电干扰的同时最大限度的保留有用信号，计算量小，信号还原度高，滤波效果好。
附图说明
图1是本发明具体实施方式提供的基于平稳小波变换滤除肌电干扰的方法的第一个实施例的方法流程图；
图2是本发明具体实施方式提供的基于平稳小波变换滤除肌电干扰的方法的第二个实施例的方法流程图；
图3是本发明具体实施方式提供的基于平稳小波变换滤除肌电干扰的系统的第一个实施例的结构方框图；
图4是本发明基于平稳小波变换滤除肌电干扰的系统的第二个实施例的结构方框图。
具体实施方式
下面结合附图并通过具体实施方式来进一步说明本发明的技术方案。
实施例一
参见图1，图1是本发明基于平稳小波变换滤除肌电干扰的方法的第一个实施例的方法流程图。
在第一实施例中，该方法包括：
S101，获取原始心电信号；
心电信号是监测人体健康的常规指标，在通过传感器检测人体的心电信号时，不可避免的包含各种各样的噪声，其中肌电干扰噪声是比较典型的噪 声，肌电干扰是由人体活动及肌肉颤动所引起的，频率约为5Hz-2K Hz，而严重的肌电干扰又分布在10Hz-300HZ。同肌电干扰噪声相比，心电信号的频率范围为0.05HZ-100HZ，其中，大部分的能量集中在0.25HZ-30HZ之间。从该肌电干扰噪声和心电信号的频率范围上可以发现，肌电信号的频率分布范围广且与心电信号有较大范围的频率叠加，在心电信号的预处理阶段，需要有效的滤除肌电干扰，以免对心电信号的特征提取产生影响。
S102，对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa；
小波变换具有时频性、渐进最优性和空间适应性等优势，逐步成为了心电信号噪声处理的主流算法，其中平稳小波变换计算量小，去噪效果好。
原始的心电信号经过小波分解后得到细节系数矩阵swd和近似系数矩阵swa，由于心电信号和肌电干扰噪声自身的频率特性，经平稳小波变换后肌电干扰噪声对信号的影响主要体现在细节系数矩阵swd中，原始的心电信号中有用信号对应的小波系数幅值较大但数目较少，而肌电干扰噪声对应的小波系数分布一致，个数较多但幅值小。
S103，对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
基于上述细节系数矩阵swd的特征，对swd中的每个系数按照给定的阈值函数去重新赋值，得到赋值后的系数矩阵swd′。
S104，对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
对重新赋值之后得到的细节系数矩阵swd′和近似系数矩阵swa逆变换进行信号重构，就可以得到滤除了肌电干扰噪声的心电信号。
综上，本实施例基于平稳小波变换滤除肌电干扰的方法，包括获取原始心电信号，对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa，对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到新的细节系数矩阵swd′，对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号；本发明采用平稳小波变换和预设的阈值函数对采样得到的原始心电信号进行处理，在滤除肌电干扰的同时最大限度的保留有用信号，计算量小，信号还原度高，滤波效果好。
实施例二
参见图2，图2是本发明基于平稳小波变换滤除肌电干扰的方法第二个实施例的方法流程图。
在第二实施例中，该方法包括：
S201，获取原始心电信号；
S202，采用db4小波基对所述原始心电信号进行5层平稳小波变换；
根据所述原始心电信号的采样频率确定平稳小波变换的层数为5层，根据所述原始心电信号的特征选取db4作为所述平稳小波变换的小波基。
在小波基的选取过程中考虑如下因素：a.若支集长度太大不利于实时性；b.与待分析的原始信号相似性太差会造成原始信号在重构后有失真现象，且能量不集中；c.对称性也是小波基函数在选取上要考虑的一个重要因素，对称性不好会造成原始信号在重构后有相移的存在。综合上述限制因素的考虑，最终选取db4(Daubechies Wavelet，多贝西小波)作为该方案平稳小波变换的小波基，4为小波的阶数，其对称性好，且与心电信号的P-QRS-T波有一定的相似性。
S203，对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
上述预设的阈值函数为：
Figure PCTCN2017079424-appb-000005
其中，X为所述细节系数矩阵swd的元素，Y为所述细节系数矩阵swd′的元素，α为自由因子，α的取值为2.5，sgn(X)为符号函数，
Figure PCTCN2017079424-appb-000006
γ为阈值，其计算公式为：
Figure PCTCN2017079424-appb-000007
i为所述细节系数矩阵swd′的行数，median(|Wij|)为中值函数，Wij为所述细节系数矩阵swd′第i行第j列上的元素。
阈值γ为变量i的函数，细节系数矩阵swd′的每一行对应一个阈值，当行数i一定时，median(|Wij|)表示细节系数矩阵swd′中第i行元素的中值，N为采样点数，N的取值为4096。
基于上述细节系数矩阵swd的特征，通过阈值函数对其重新赋值，当细节系数矩阵swd中的元素小于阈值γ时，保留原值，即保留原始的心电信号中有用信号对应的小波系数；当细节系数矩阵swd中的元素大于等于阈值γ时，将其重新赋值，即减小肌电干扰噪声对应的小波系数。通过上述改进的阈值函数对细节系数矩阵进行处理之后，使得阈值函数曲线在阈值处过渡平滑，同时又能最大程度地保持心电信号中的有用信号所产生的小波系数的幅值。
S204，对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
令上述原始心电信号为X；
对该原始心电信号X进行平稳小波变换的具体计算公式为：(swa，swd)＝SWT(X，db4，5)；
对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换的具体计算公式为：x_filter＝iswt(swa，swd′)，从而得到滤除了肌电干扰的心电信号。
本方案采用改进的阈值函数，该阈值函数曲线在阈值处过渡平滑，同时又能最大程度地保持心电信号中的有用信号所产生的小波系数的幅值，避免了硬阈值函数在阈值处不连续而造成信号在重构时产生的断点，以及软阈值函数在幅值上降低阈值而导致信号在重构时产生的失真现象，使得通过该阈值函数得到的细节系数矩阵swd′和近似系数矩阵swa进行信号重构时，可以最大程度的滤除肌电干扰信号，同时保留心电信号中的有用信号。
综上，本实施例基于平稳小波变换滤除肌电干扰的方法，基于获取的原始心电信号的特征选择合适的小波变换层数和小波基对其进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa，根据改进的阈值函数对细节系数矩阵swd重新赋值，得到新的细节系数矩阵swd′，然后通过细节系数矩阵swd′和近似系数矩阵swa进行逆变换重构出心电信号；本方案选取平稳小波变换计算量小，同时采样改进的阈值函数对小波变换的系数矩阵进行处理，该阈值函数兼具软阈值函数和硬阈值函数的优点，在滤除肌电干扰的同时最大程度的保留有用信号，信号还原度高，滤波效果好。
实施例三
参见图3，图3是本发明基于平稳小波变换滤除肌电干扰的系统的结构方框图。
在第三实施例中，该系统包括：
获取单元01，用于获取原始心电信号；
通过相应的传感器采集到人体的原始心电信号，然后送到计算机、单片机等终端进行信号处理。
小波变换单元02，用于对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa；
系数调整单元03，用于对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
逆变换单元04，用于对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
该系统可以应用于监测人体健康的穿戴式设备中，同时也可以应用于医疗设备中。
综上，本实施例基于平稳小波变换滤除肌电干扰的系统，通过传感器采集原始心电信号，对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa，对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到新的细节系数矩阵swd′，对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号；本系统采用平稳小波变换和预设的阈值函数对采样得到的原始心电信号进行处理，在滤除肌电干扰的同时最大限度的保留有用信号，计算量小，信号还原度高，滤波效果好。
实施例四
参见图4，图4是本发明基于平稳小波变换滤除肌电干扰的系统的结构方框图。
在第三实施例的基础上，该系统还包括：
阈值函数设置单元05，用于设置所述预设的阈值函数，所述预设的阈值函数为：
Figure PCTCN2017079424-appb-000008
其中，X为所述细节系数矩阵swd的元素，Y为所述细节系数矩阵swd′的元素，α为自由因子，γ为阈值。
其中，细节系数矩阵swd体现了肌电干扰噪声和原始心电信号中有用信号的幅值特征，基于此采用改进的阈值函数对细节系数矩阵swd重新赋值，保留上述有用信号对应的小波系数，减小肌电干扰噪声对应的小波系数，得到新的细节系数矩阵swd′。
自由因子设置单元06，用于设置所述预设的阈值函数中自由因子α的取值，α的取值为2.5；
阈值设置单元07，用于设置所述预设的阈值函数中阈值γ的计算公式，所述阈值γ为：
Figure PCTCN2017079424-appb-000009
其中，i为所述细节系数矩阵swd′的行数，median为中值函数，Wij为所述细节系数矩阵swd′第i行第j列上的元素，N为采样点数，N的取值为4096。
变换层数设置单元08，用于设置平稳小波变换的层数，所述层数为5；
小波基设置单元09，用于设置平稳小波变换的小波基，所述小波基为db4。
综上，本实施例基于平稳小波变换滤除肌电干扰的系统，基于获取的原始心电信号的特征选择合适的小波变换层数和小波基对其进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa，根据改进的阈值函数对细节系数矩阵swd重新赋值，得到新的细节系数矩阵swd′，然后通过细节系数 矩阵swd′和近似系数矩阵swa进行逆变换重构出心电信号；本方案选取平稳小波变换计算量小，同时采样改进的阈值函数对小波变换的系数矩阵进行处理，该阈值函数兼具软阈值函数和硬阈值函数的优点，在滤除肌电干扰的同时最大程度的保留有用信号，信号还原度高，滤波效果好。
以上结合具体实施例描述了本发明的技术原理。这些描述只是为了解释本发明的原理，而不能以任何方式解释为对本发明保护范围的限制。基于此处的解释，本领域的技术人员不需要付出创造性的劳动即可联想到本发明的其它具体实施方式，这些方式都将落入本发明的保护范围之内。

Claims (10)

  1. 一种基于平稳小波变换滤除肌电干扰的方法，其特征在于，包括如下步骤：
    获取原始心电信号；
    对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa；
    对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
    对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
  2. 根据权利要求1所述的方法，其特征在于，所述预设的阈值函数为：
    Figure PCTCN2017079424-appb-100001
    其中，X为所述细节系数矩阵swd的元素，Y为所述细节系数矩阵swd′的元素，sgn为符号函数，α为自由因子，γ为阈值。
  3. 根据权利要求2所述的方法，其特征在于，所述阈值γ为：
    Figure PCTCN2017079424-appb-100002
    其中，i为所述细节系数矩阵swd′的行数，median为中值函数，Wij为所述细节系数矩阵swd′第i行第j列上的元素，N为采样点数。
  4. 根据权利要求2所述的方法，其特征在于，所述自由因子α的取值为2.5。
  5. 根据权利要求3所述的方法，其特征在于，所述采样点数N的取值为4096。
  6. 根据权利要求1所述的方法，其特征在于，对所述原始心电信号进行平稳小波变换具体为：采用db4小波基对所述原始心电信号进行5层平稳小波变换。
  7. 一种基于平稳小波变换滤除肌电干扰的系统，其特征在于，包括：
    获取单元，用于获取原始心电信号；
    小波变换单元，用于对所述原始心电信号进行平稳小波变换，得到细节系数矩阵swd和近似系数矩阵swa；
    系数调整单元，用于对所述细节系数矩阵swd按照预设的阈值函数重新赋值，得到赋值后的细节系数矩阵swd′；
    逆变换单元，用于对所述细节系数矩阵swd′和近似系数矩阵swa进行逆平稳小波变换，得到滤除了肌电干扰信号的心电信号。
  8. 根据权利要求7所述的系统，其特征在于，所述系统还包括：
    阈值函数设置单元，用于设置所述预设的阈值函数，所述预设的阈值函数为：
    Figure PCTCN2017079424-appb-100003
    其中，X为所述细节系数矩阵swd的元素，α为自由因子，Y为所述细节系数矩阵swd′的元素，γ为阈值。
  9. 根据权利要求8所述的系统，其特征在于，所述系统还包括：
    自由因子设置单元，用于设置所述预设的阈值函数中自由因子α的取值，α的取值为2.5；
    阈值设置单元，用于设置所述预设的阈值函数中阈值γ的计算公式，所述阈值γ为：
    Figure PCTCN2017079424-appb-100004
    其中，i为所述细节系数矩阵swd′的行数，median为中值函数，Wij为所述细节系数矩阵swd′第i行第j列 上的元素，N为采样点数，N的取值为4096。
  10. 根据权利要求7所述的系统，其特征在于，所述系统还包括：
    变换层数设置单元，用于设置平稳小波变换的层数，所述层数为5；
    小波基设置单元，用于设置平稳小波变换的小波基，所述小波基为db4。
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