基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

一种基于小波变换的心电去噪算法

目的:去除心电信号采集过程中混入的工频干扰、肌电干扰和基线漂移等噪声信号,并能有效的保留心电特征信息.方法:通过小波变换将含噪的心电信号分解并重构得到不同尺度下的细节信号,在中小尺度上选取不同的门限值,并在QRS波群信息多的尺度上计算获得信息窗,对该尺度的信息窗内外采用不同的门限处理方式,在大尺度上直接重构出要去除的基线信息.结果:采用MIT/BIH Arrhythmia Database中的数据对算法进行了仿真验证,实现了三种主要干扰的去除,较好的保留了心电特征信息.结论:本方法效果较好,为后续的特征点识别奠定了基础.     

基于集合经验模态分解和小波收缩算法的自适应心电信号去噪问题研究

本文将集合经验模态分解(EEMD)与小波软阈值去噪算法相结合,提出了一种新的心电图信号去噪EEMD-WS算法.算法首先对信号进行EEMD分解得到有限个固有模态函数(IMF);其次,根据实际含噪心电信号中各成分的特性,将所有IMF分为低阶含噪、中阶有用信号和高阶含基线漂移三类,对于低阶含噪IMF利用IMF能量变化分界点自适应地确定含噪IMF个数,随后对其利用小波收缩算法中的启发式软阈值选择算法进行去噪;对于高阶含基线漂移IMF根据其自身是否包含周期信息自适应地判断并去除与基线漂移关系密切的IMF.最后通过将滤除噪声的低阶IMF、中阶有用信号重构达到抑制噪声和去除基线漂移的目的.仿真信号和MIT-BIH心电数据库真实心电信号实验显示,EEMD-WS算法不仅能够克服小波去噪算法不能去除基线漂移的不足,而且能够比常用的EMD-WS算法更好地提高消噪效果,总体去噪性能优于传统算法.     

基于小波的心电信号有效性检测方法

目的：检测采集到的信号是否为有效心电信号,提高后续心电诊断和分析的准确率。方法：将采集到的信号进行预处理,即去噪处理,主要抑制基线漂移,50Hz工频及其谐波干扰和肌电干扰;取滑动窗长度为4s,检测该段内信号是否有效。为了验证算法的准确率及对不同心电波形是否具有普遍适用性,对MIT-BIH Arrhythmia Database中48个记录,CU及MIT-BIH Noise Stress Test Database中部分记录进行了仿真、验证。结果：仿真实验证明该方法能正确区分有效和无效信号,错检率较低,实现简单,适合实时处理。结论：本方法准确率高,能减少后续心电诊断和分析的计算量并提高准确率,特别是对室颤检测,符合心电分析的要求。     

强噪声,高基漂下的QRS检测器

本文描述了一种基于两进小波变换（ＤＹＷＴ）的ＱＲＳ波检测器。小波尺度的选择是基于心电信号的频谱的特点,并根据多尺度选择方法判决检测心电ＱＲＳ波,实验结果表明,对于在有强大的噪声和严重的基线漂移干扰下的心电信号能够有效的识别。     

表面肌电信号采集与降噪处理

目的:本文以设计的表面肌电(sEMG)信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法:结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果:通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论:所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

表面肌电信号采集与降噪处理

目的：本文以设计的表面~g（sEMG）信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法：结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果：通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论：所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

基于小波变换心电信号消噪方法研究

小波变换是近年来兴起的热门信号处理技术,是一种非常有用的信号处理工具。本文阐述了连续小波去噪和离散小波去噪的原理,分析了基于小波去噪的几种不同方法（其中包括小波分解与重构,小波变换阈值法,小波变换模极大值法,以及它与独立分量分析相结合去除噪声的方法等）。通过检测和验证,表明该方法能较好的实现心电信号的消噪,都取得了较好的效果;同时,比较了每种方法的不足和缺陷。基于小波变换心电信号消噪的研究进展较快,通过多种方法结合运用进行消噪并取得了很好的效果,展望了利用基于小波变换心电信号消噪的前景。     

基于独立元分析和非线性指数分析的脑电信号中伪迹分量的自动去除

脑电(electroencephalography,EEG)信号中不可避免地存在着眼动、心跳、肌电信号以及线性噪声等伪迹干扰,这些伪迹的存在极大地影响了脑电信号分析的准确性,因此在进行脑电信号分析前需要去除伪迹干扰。为了有效地去除伪迹,结合独立元分析和非线性指数分析,提出一种自动识别并去除脑电信号中伪迹分量的方法。该方法还可同时用于提取脑电信号中的基本节律如!波等。相应的模拟与实际脑电数据的实验结果表明所提议的方法具有很好的识别和去除脑电信号伪迹分量的性能。     

心电、呼吸信号采集分析系统的研制

设计一种用于移动监护系统的生理信息采集及预处理装置。该装置以ARM为核心,包括低功耗的双路心电信号放大、滤波、抗基线漂移电路。实现了心电信号的采集、预处理、简单分析及从心电信号中提取呼吸信号等功能。     

Removal of ECG interference from surface respiratory electromyography

The signal to noise ratio (SNR) of surface respiratory electromyography signal is very low. Indeed EMG signal is contaminated by different types of noise especially the cardiac artefact ECG. This article explores the problem of removing ECG artefact from respiratory EMG signal. The new method uses an adaptive structure with an electrocardyographic ECG reference signal carried out by wavelet decomposition. The proposed algorithm requires only one channel to both estimating the adaptive filter input reference noise and the respiratory EMG signal. This new technique demonstrates how two steps will be combined: the first step decomposes the signal with forward discrete wavelet transform into sub-bands to get the wavelet coefficients. Then, an improved soft thresholding function was applied. And the ECG input reference signal is reconstructed with the transformed coefficients whereas, the second uses an adaptive filter especially the LMS one to remove the ECG signal. After trying statistical as well as mathematical analysis, the complete investigation ensures that all details and steps make proof that our rigorous method is appropriate. Compared to the results obtained using previous techniques, the results achieved using the new algorithm show a significant improvement in the efficiency of the ECG rejection.     

一种滤除医学影像噪声的混合滤波算法

目的：医学影像在获取、存储、传输过程中会不同程度地受到噪声污染,这极大影像了其在临床诊疗中的应用。为了有效地滤除医学影像噪声,提出了一种混合滤波算法。方法：该算法首先将含有高斯和椒盐噪声的图像进行形态学开运算,然后对开运算后的图像进行二维小波分解,得到高频和低频小波分解系数。保留低频系数不变,将高频系数经过维纳滤波器进行滤波,最后进行小波系数重构。结果：采用该混合滤波算法、小波阚值去噪、中值滤波、维纳滤波分别对含有混合噪声的医学影像分别进行滤除噪声处理,该滤波算法去噪后影像的PSNR值明显高于其他三种方法。结论：该混合滤波算法是一种较为有效的医学影像噪声滤除方法。     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram

Electrocardiogram (ECG) is an important bioelectrical signal used to asses the cardiac state of a patient. It consists of a recurrent wave sequence of P-wave, QRS-complex and T-wave associated with each beat. The QRS-complex is the prominent feature of the ECG. This paper presents a simple method using K-means clustering algorithm for the detection of QRS-complexes in ECG signal. Digital filters are used to remove the power line interference and baseline wander present in the ECG signal. K-means algorithm is used to classify QRS and non-QRS-region in the ECG signal. The performance of the algorithm is validated using dataset-3 of the CSE multi-lead measurement library. Detection rate of 98.66% is obtained. The percentage of false positive and false negative is 1.14% and 1.34% respectively. The mean and standard deviation of the errors between automatic and manual annotations is calculated to validate the delineation performance of the algorithm. The onsets and offsets of the detected QRS-complexes are found well within the tolerance limits as specified by the CSE library.     

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is reconstructed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10 dB at SNR 5 dB, and most of the improvement SNR are better than other methods at least 1 dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9 km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively.     

Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram

The automatic detection of electrocardiogram (ECG) waves namely P, QRS and T-wave is important to cardiac disease diagnosis. This paper presents an application of support vector machine (SVM) as a classifier for the delineation of ECG wave components in the 12-lead ECG signal. Digital filtering techniques are used to remove power line interference and baseline wander present in the ECG signal. Gradient of the filtered ECG signal is used as a feature for the detection of QRS-complexes, P- and T-waves. The performance of the algorithm is validated using original 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate is achieved. The percentage of false positive and false negative detection is low. The method successfully detects all kind of morphologies of QRS-complexes, P- and T-waves. The onsets and offsets of the detected QRS-complexes, P- and T-waves are found to be within the tolerance limits given in CSE library.     

Electrocardiogram signal quality assessment using an artificially reconstructed target lead

In real applications, even the most accurate electrocardiogram (ECG) analysis algorithm, based on research databases, might breakdown completely if a quality measurement technique is not applied precisely before the analysis. The major concentration of this study is to describe and develop a reliable ECG signal quality assessment technique. The proposed algorithm includes three major stages: preprocessing, energy-concavity index (ECI) analysis and a correlation-based examination subroutine. The preprocessing step includes the removal of baseline wanders and high-frequency disturbances. The quality measurement based on ECI includes two separate stages according to the energy and concavity of the ECG signal. The correlation-based quality measurement step is mainly established by using the correlation between ECG leads estimated by applying a suitably trained neural network. The operating characteristics of the proposed ECI are sensitivity (Se) of 77.04% with a positive predictive value (PPV) of 90.53% for detecting high-energy noise. The correlation-based technique achieved the best scores (Se = 100%; PPV = 98.92%) for detecting high-energy noise and for recognising any other kind of disturbances (Se = 92.36%; PPV = 94.77%). Although ECI analysis acts effectively against high-energy disturbances, very poor performance is obtained in cases where the energy of the disturbances is not considerable. However, the correlation-based method is able to find all kinds of disturbances. For officially evaluating the proposed algorithm, an entry was sent to the Computing-in-Cardiology Challenge 2011 on 27 February 2012; a final score (accuracy) of 93.60% was achieved.     

SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Among all electrocardiogram (ECG) components, the QRS complex is the most significant feature. This paper presents a new algorithm for recognition of QRS complexes in the electrocardiogram (ECG) based on support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. SVM is used as a classifier to delineate QRS and non-QRS regions. Algorithm performance was evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.3% of the detection rate. The percentage of false positive and false negative was 12.4 and 0.7% respectively. It could function reliably even under the condition of poor signal quality of the ECG signal.