Wavelet based analysis and characterization of the ECG signal

This paper reports the use of a wavelet analysis technique based on the Mexican Hat wavelet to identify the onset and termination points and the duration of the principal constituent components of the human electrocardiogram (ECG). ECG recordings were obtained from 21 healthy subjects aged between 13 and 65 years, over a wide range of heart rates extending from 46 to 184 beats min(-1). A wavelet transform method was then used to locate precisely the positions of the onset, termination and the durations of individual components in the ECG. Component times were then classified according to the heart rate associated with the cardiac cycle to which the component belonged. Second order equations of the form [formula in text] were fitted to the data obtained for each component to characterize its timing variation.     

早搏信号的小波模极大多重分形分析

本文运用基于小波模极大值的多重分形分析方法,研究心脏房性早搏(APB)信号、室性早搏(PVC)信号及正常心电(ECG)信号的多重分形特征。通过分析多重分形谱得出:三种信号都具有不同程度的多重分形特性;正常ECG信号的分形程度最强,PVC信号次之,APB信号最弱。t检验结果表明,此方法得出的三种信号分形谱宽度差异具有显著性,对临床医学诊断区分APB、PVC信号有很好的借鉴意义。     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity=99.88% and predictivity=99.89%), and a total detection failure of 0.24%.     

ECG signal processing using multiresolution analysis

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity = 99.88% and predictivity = 99.89%), and a total detection failure of 0.24%.     

Wavelet based ECG compression with adaptive thresholding and efficient coding

This paper proposes a new wavelet-based ECG compression technique. It is based on optimized thresholds to determine significant wavelet coefficients and an efficient coding for their positions. Huffman encoding is used to enhance the compression ratio. The proposed technique is tested using several records taken from the MIT-BIH arrhythmia database. Simulation results show that the proposed technique outperforms others obtained by previously published schemes.     

The use of the Hilbert transform in ECG signal analysis

This paper presents a new robust algorithm for QRS detection using the first differential of the ECG signal and its Hilbert transformed data to locate the R wave peaks in the ECG waveform. Using this method, the differentiation of R waves from large, peaked T and P waves is achieved with a high degree of accuracy. In addition, problems with baseline drift, motion artifacts and muscular noise are minimised. The performance of the algorithm was tested using standard ECG waveform records from the MIT-BITH Arrhythmia database. An average detection rate of 99.87%, a sensitivity (Se) of 99.94% and a positive prediction (+P) of 99.93% have been achieved against study records from the MIT-BITH Arrhythmia database. A detection error rate of less than 0.8% was achieved in every study case. The reliability of the proposed detector compares very favorably with published results for other QRS detectors.     

Incremental HMM training applied to ECG signal analysis

This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual. The adaptation training strategy is based on incremental versions of the expectation-maximization, segmental k-means and Bayesian approaches. Performance of the training methods was assessed through experiments considering the QT and ST-T databases. The results obtained show that the incremental training improves beat segmentation and ischemia detection performance with the advantage of low computational effort.     

ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern

The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases.     

Use of dynamic time warping for accurate ECG signal timing characterization

Dynamic time warping techniques have been used to characterize the timing variation of the constituent components of the human electrocardiogram (ECG). Lead II ECG recordings were obtained in 21 subjects, 10 male and 11 female aged between 13–65 years. The fiducial points in each cardiac cycle were identified in the recordings across the range of heart rate from 46–184 beats/min. A set of second order equations in the square root of the cardiac cycle time was obtained to describe the duration each of the constituent components in the ECG signal. The accuracy of the dynamic time warping technique was verified against professionally annotated clinical recordings in the on-line PhysioNet? database. The equations obtained allow a Lead II ECG signal to be synthesized in which the variation with heart rate of the profile of each in the signal mirrors the true in-vivo behaviour.     

基于功率谱特征的心电数据小波压缩方法

利用心电功率谱特征,探索心电数据压缩新方法。用小波分解心电信号为高频与低频分量,对低频分量继续分解达到要求的级数,对高频分量则根据其所在频段的能量,对临床诊断的价值加以取舍。对MIT生理信号数据库心电数据的压缩与还原分析表明,该方法平衡了压缩比与还原精度之间的矛盾,既具有较高的压缩比,又具有较高的还原精度,而且对信号的适应性也明显增强。另外,该压缩方法还具有一定的去噪作用。说明结合心电功率谱特征与小波变换方法压缩心电有其优势。     

基于心电信号的自注意力双向门控循环网络疲劳检测模型

人类操作员的生理疲劳状态对其作业效率与安全性存在很大的影响,本研究提出了一种基于自注意力（SA）机制的双向门控循环（BiGRU）网络疲劳检测模型,研究基于心电信号的疲劳检测方法。首先采集了模拟不同负荷水平的过程控制任务环境下操作人员的心电数据,以一维心电数据作为输入,经过去噪预处理后,使用改进的BiGRU神经网络进行特征提取,BiGRU在保留GRU优点的同时可以更加充分学习心电信号前后时序的特征联系,并通过SA机制筛选显著相关特征信息,最后将所获得的特征信息经过softmax分类器,得到疲劳分类结果。与传统的GRU模型和BiLSTM模型进行了比较,经过改进后的SA-BiGRU模型的疲劳分类性能整体提高2%~5%,总体准确率达83%。     

Wavelet based ST-segment analysis

A novel algorithm for ST-segment analysis is developed using the multi-resolution wavelet approach. The system detects the QRS complexes and analyses each beat using the wavelet transform to identify the characteristic points (fiducial points). These fiducial points are, iso-electric level, the J point, and onsets and offsets of the QRS complex and T wave. The algorithm determines the T onset by looking for a point of inflection between the J point and the T peak. Furthermore, detection of characteristic points by the wavelet technique reduces the effect of noise. The results show that the proposed approach gives very accurate ST levels, as compared to the conventional (empirical) technique, at higher heart rates and with different morphologies. The algorithm detects the ST-segment length in 92.3% beats with an error of 4 ms, and in 97.3% beats the error is within 8 ms. The algorithm has been implemented on a TMS320C25 based add-on DSP card connected to a PC to provide the on-line analysis and display of ST-segment data.     

ECG signal denoising and baseline wander correction based on the empirical mode decomposition

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT-BIH databases. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal.     

基于EMD与IMF分量统计特性的ECG去噪

经验模式分解（EMD）域内心电（ECG）信号的去噪,通常为基于QRS特征波经验性识别固有模态函数（IMF）分量并重建ECG信号。由于该方法引入个人误差,因此识别不准确。针对此问题,本文提出利用EMD与IMF分量统计特性对ECG信号进行去噪。本方法首先对含噪ECG信号进行EMD分解得到一系列IMF分量,然后利用IMF分量的统计特性识别IMF分量属性,并采用被识别为ECG信号的IMF分量重建ECG信号。该识别方法基于统计学方法,具有统计学和现实物理意义。将本方法应用于真实ECG信号去噪处理中,结果表明,本方法可有效去除ECG信号基线漂移噪声与肌电干扰噪声,去噪效果优于经验法。     

基于1D-ECNN的短时心电信号疲劳分类

提出一维双卷积神经网络（1D-ECNN）,基于采集的心电信号检测操作员的疲劳状态。1D-ECNN包括4 个卷积 层、2个最大池化层、1个全连接层和1个softmax输出层。本研究仅使用较少的卷积核数量,这将减少模型参数的数量,降 低模型的复杂程度,提高模型训练的速度,同时避免传统方法中复杂的特征提取过程或特征选择过程。将心电信号分成 时间长度为1 s的样本,送入1D-ECNN,基于短时心电信号进行操作员疲劳状态分类。仿真结果表明,本文方法的平均分 类准确率高达95.72%,能够实时准确地检测操作员的疲劳状态。此外,可以较好地消除个体差异性的影响。     

Feature extraction of ECG signal

This paper deals with new approaches to analyse electrocardiogram (ECG) signals for extracting useful diagnostic features. Initially, elimination of different types of noise is carried out using maximal overlap discrete wavelet transform (MODWT) and universal thresholding. Next, R-peak fiducial points are detected from these noise free ECG signals using discrete wavelet transform along with thresholding. Then, extraction of other features, viz., Q waves, S waves, P waves, T waves, P wave onset and offset points, T wave onset and offset points, QRS onset and offset points are identified using some rule based algorithms. Eventually, other important features are computed using the above extracted features. The software developed for this purpose has been validated by extensive testing of ECG signals acquired from the MIT-BIH database. The resulting signals and tabular results illustrate the performance of the proposed method. The sensitivity, predictivity and error of beat detection are 99.98%, 99.97% and 0.05%, respectively. The performance of the proposed beat detection method is compared to other existing techniques, which shows that the proposed method is superior to other methods.     

基于多分支融合神经网络的心电图导联重构方法

设计一种新型的多分支信息融合神经网络结构,利用已知的I,Ⅱ,V2 3个导联心电信号来重构其它导联心电信号。基于卷积神经网络结构提取多个导联的特征然后进行线性相加融合,采用一种改进的双向长短期记忆网络结构来获得与时序相关的信息,从而实现心电图导联重构。使用Physikalisch Technische Bundesanstalt(PTB)数据库进行验证,导联重构方法具有0.944 4的相关系数和0.320 3的均方根误差,说明新型神经网络结构可以有效地实现心电图导联重构。     

Long-term ECG signal feature extraction

This paper proposes a cepstrum coefficient method applying the dynamic time warping technique to extract the feature vectors from long-term ECG signals. Utilizing this method, one can identify the characteristics hidden in an ECG signal; and then classify the signal as well as diagnose the abnormalities. To evaluate this method, the Normal and PACED BEAT data from the MIT/BIH database are used. The results show that the proposed method successfully extracts the corresponding feature vectors, distinguishes the difference and classifies both signals.