Wavelet transform-based QRS complex detector

In this paper, we describe a QRS complex detector based on the dyadic wavelet transform (Dy WT) which is robust to time-varying QRS complex morphology and to noise. We design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. We illustrate the performance of the Dy WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) data base. Seventy hours of data was considered. We also compare the performance of Dy WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results we observed that although no one algorithm exhibited superior performance in all situations, the Dy WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the Dy WT-based detector exhibited excellent performance.     

基于生成对抗网络的动态心电信号QRS波检测

心电信号分析是预防心血管疾病的重要举措,QRS波的精确检测不仅是心电信号处理的关键步骤且对心率计算和异常情况分析具有重要作用.针对动态心电信号存在信号质量差或异常节奏波形导致常用QRS波检测方法精度较低的问题,本文提出了 一种基于生成对抗网络新型QRS波检测算法.该算法以Pix2Pix网络为基础,生成网络采用U-Net...     

Digital fractional order operators for R-wave detection in electrocardiogram signal

In this study, we present an effective R-wave detection method in the QRS complex of the electrocardiogram (ECG) based on digital differentiation and integration of fractional order. The detection algorithm is performed in two steps. The pre-processing step is based on a fractional order digital band-pass filter whose fractional order is obtained by maximising the signal to noise ratio of the ECG signal, followed by a five points differentiator of fractional order 1.5 then the squaring transformation and the smoothing are used to generate peaks corresponding to the ECG parts with high slopes. The detection step is a new and simple strategy which is also based on fractional order operators for the localisation of the R waves. The MIT/BIH arrhythmia database is used to test the effectiveness of the proposed method. The algorithm has provided very good performance and has achieved about 99.86% of the detection rate for the standard database. The results obtained are presented, discussed and compared to the most recent and efficient R-wave detection algorithms.     

A Real-Time QRS Detection Algorithm

We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.     

Digital Filters for Real-Time ECG Signal Processing Using Microprocessors

Traditionally, analog circuits have been used for signal conditioning of electrocardiograms. As an alternative, algorithms implemented as programs on microprocessors can do similar filtering tasks. Also, digital filter algorithms can perform processes that are difficult or impossible using analog techniques. Presented here are a set of real-time digital filters each implemented as a subroutine. By calling these subroutines in an appropriate sequence, a user can cascade filters together to implement a desired filtering task on a single microprocessor. Included are an adaptive 60-Hz interference filter, two low-pass filters, a high-pass filter for eliminating dc offset in an ECG, an ECG data reduction algorithm, band-pass filters for use in QRS detection, and a derivative-based QRS detection algorithm. These filters achieve real-time speeds by requiring only integer arithmetic. They can be implemented on a diversity of available microprocessors.     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

基于小波和扩展卡尔曼的心电检测

文中将小波变换和扩展卡尔曼滤波器相结合,利用小波变换多尺度多分辨的特点,将心电信号进行分解。然后对心电信号在各尺度上进行扩展卡尔曼滤波。最后在扩展卡尔曼滤波的输出结果上进行QRS波形检测。文中方法经MIT-BIH心电数据库检验,QRS波Se（探测灵敏度）在99.40%以上,同时,QRS＋P（正探测率）在99.39%以上,提高了心电信号检测的正确率。     

Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate Envelope

The electrocardiogram (ECG ) signal is prone to various high and low frequency noises, including baseline wandering and power-line interference, which become the source of errors in QRS and in other extracted features. This paper presents a new ECG signal-processing approach based on empirical mode decomposition (EMD) and an improved approximate envelope method. To reduce the number of the initial intrinsic mode functions (IMFs), a Butterworth lowpass filter is used to eliminate high frequency noises before the EMD. To correct baseline wandering and to eliminate low frequency noises, the two last-order IMFs are abandoned. An improved approximate envelope is proposed and applied after the Hilbert transform to enhance the energy of QRS complexes and to suppress unwanted P/T waves and noises. Then, an algorithm based on the slope threshold is used for R-peak detection. The proposed denoising and R-peak detection algorithm are validated using the MIT-BIH Arrhythmia Database. The simulation results show that the proposed method can effectively eliminate the Gaussian noise, baseline wander, and power-line interference added to the ECG signal. The method can also function reliably even under poor signal quality and with long P and T peaks. The QRS detector has an average sensitivity of Se=99.94 % and a positive predictivity of +P=99.87 % over the first lead of the MIT-BIH Arrhythmia Database.     

Nonlinear transforms of ECG signals for digital QRS detection: aquantitative analysis

A class of algorithms has been developed which detects QRS complexes in the electrocardiogram (ECG). The algorithms employ nonlinear transforms derived from multiplication of backward differences (MOBD). The algorithms are evaluated with the American Heart Association ECG database, and comparisons are made with the algorithms reported by Okada (1979) and by Hamilton and Tompkins (1986). The MOBD algorithms provide a good performance tradeoff between accuracy and response time, making this type of algorithm desirable for real-time microprocessor-based implementation     

A comparison of the noise sensitivity of nine QRS detectionalgorithms

The noise sensitivities for nine different QRS detection algorithms were measured for a normal, single-channel lead II, synthesized ECG corrupted with five different types of synthesized noise. The noise types were electromyographic interference, 60 Hz powerline interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite noise corrupted data.     

New Concepts for PVC Detection

A new scheme is proposed for the detection of premature ventricular beats, which is a vital function in rhythm monitoring of cardiac patients. A transformation based on the first difference of the digitized electrocardiogram (ECG) signal is developed for the detection and delineation of QRS complexes. The method for classifying the abnormal complexes from the normal ones is based on the concepts of minimum phase and signal length. The parameters of a linear discriminant function obtained from a training feature vector set are used to classify the complexes. Results of application of the scheme to ECG of two arrhythmia patients are presented.     

A QRS-complex detector of the electrocardiogram signal for the long-term monitoring of the patient’s condition

A procedure for detection of the QRS complexes of electrocardiogram (ECG) signals for systems of long-term monitoring of the patient’s condition based on successive use of the band-pass filtering, the Hilbert transform, and an adaptive threshold algorithm is considered. The efficiency of different QRS-complex detectors has been investigated for model ECG signals in the presence of interferences of various nature and intensity. The efficiency of the proposed procedure has been additionally tested using various clinic records of ECG signals from the MIT Physionet database.     

On detecting the presence of fetal R-wave using the moving averagedmagnitude difference algorithm

An efficient algorithm detecting the presence of a fetal QRS complex is presented. The proposed fetal QRS detection method computes the averaged magnitude of the difference between the fetal ECG signal and the reference signal to detect the fetal QRS event. The detected fetal QRS complexes are exponentially averaged to generate the template signal which can track the slowly varying shape of the fetal ECG signal. As an effort to obtain improved detection performances, two approaches of normalizing the fetal ECG signal and the template are considered.     

ECG beat detection using filter banks

The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters     

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

基于AT91SAM9261S的心电疾病诊断系统

介绍基于AT91SAM9261的心电疾病诊断系统的设计。采用的测试平台以AT91SAM9261为核心,整个设计完成了硬件驱动和软件设计,可以对心电信号进行前置处理,接着进行波形检测与分析,从而做出心电情况的诊断,对于不正常心电给出反馈。该设计中采用的算法简单有效,给出了处理前后心电波形的液晶屏显示效果和QRS波群检测的正确率,正确率为96.93%符合实时诊断的要求。     

QRS feature extraction using linear prediction

This communication proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. This communication also indicates that the prediction order need not be more than two for fast arrhythmia detection. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex, the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to a set of three states pulse-code train relative to the original ECG signal. The pulse-code train has the advantage of easy implementation in digital hardware circuits to achieve automated ECG diagnosis. The algorithm performs very well in feature extraction in arrhythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contraction) detection has at least a 92 percent sensitivity for MIT/BIH arrhythmia database.     

强背景噪声下基于HHT端点检测方法

端点检测是语音识别系统中十分关键的一个步骤,它对整个语音系统识别的准确性有着至关重要的作用。针对目前端点检测算法在强背景噪声下存在的不足,通过引入HHT,提出了一种基于希尔伯特-黄变换的端点检测方法。该方法首先采用EMD分解出有限个IMF,然后对IMF进行Hilbert变换,将得到的IMF能量谱作为特征参数来进行语音信号的端点检测,仿真实验证明了该算法在强背景噪声下的有效性与稳健性。     

基于希尔伯特振动分解和卷积神经网络的融合特征心电识别算法

在信息高速发展的当代社会,5G技术的问世将极大地助力社会经济和信息化发展,而隐私安全和信息安全愈发得到重视,因此公众会对身份的识别技术提出了更高要求。然而,传统基于密码、ID卡以及新型的基于人脸和指纹的识别方法存在易丢失、遗忘和窃取或易于伪造和获取复制等问题而存在极大的安全隐患。为提高身份识别的可靠性和准确率,提出了基于希尔伯特振动分解和卷积神经网络的融合特征心电图信号识别算法。首先采用基于重叠组收缩阈值算法和平移不变的消噪算法对含噪心电信号去噪,其次利用盲源分割技术将心电信号分割成固定时长的心电片段,再次采用基于希尔伯特振动分解的时频分析方法获得心电信号的时频表示图,通过提出的心电残差卷积神经网络对时频表示图实现特征提取和降维,最后通过Softmax分类器实现分类和识别。以Physionet数据库的ECG-ID数据集验证提出算法的性能,采用10折交叉验证法得到平均识别率为99.08%。结果表明,提出的心电识别算法具有高效的识别性能和良好的应用前景。     

Computer analysis of the electrocardiogram during esophageal pacingcardiac stress

It has been estimated that 15 to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to a variety of reasons such as obesity, poor physical condition, claudication, etc. Transesophageal atrial pacing has been proposed as a noninvasive alternative for inducing cardiac stress in patients who cannot exercise. Although computer analysis is commonly employed to analyze the electrocardiogram (ECG) during the conventional exercise stress test, the surface ECG recorded during transesophageal atrial pacing is contaminated with large pacing artifacts which confound beat identification by standard computer software. We report the development of a robust signal processing algorithm for interpretation of the surface ECG during transesophageal atrial pacing stress. The algorithm employs novel schemes using both linear and nonlinear transformations to detect and differentiate between the pacing artifact and QRS complex even in difficult situations where the pacing artifact is in proximity to or superimposed on the QRS complex. The algorithm uses sophisticated logic for automatic recognition of sustained capture. It subsequently calculates beat-by-beat and average (over five beats) ST segment amplitude and slope. The algorithm also reports the instantaneous heart rate, RR interval, pace-to-R interval, R-wave amplitude, and estimated sinus node recovery time upon loss of sustained capture. The limitations of present exercise ECG computer methods in processing the ECG during transesophageal atrial pacing stress are evaluated and significantly improved performance by our algorithm is demonstrated.