A survey on ECG analysis

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals

Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the nonstationary nature of the ECG signal. In this context, this paper presents a new approach, i.e., discrete orthogonal stockwell transform using discrete cosine transform for efficient representation of the ECG signal in time–frequency space. These time–frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using support vector machine (SVM). In order to improve the classification performance, particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this paper, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. The experimental results yielded an improved overall accuracy, sensitivity (Sp), and positive predictivity (Pp) of 98.82% in comparison with the existing approaches available in the literature.

ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM

Functionalized textiles and graphene are both emerging materials for wearable technologies. Here, we unite graphene with ordinary textiles and report the development of graphene-clad, conductive textile electrodes for biosignal acquisition specifically in cardiac monitoring. The proposed electrode was prepared by dipping nylon fabric in reduced graphene oxide (rGO) solution followed by a subsequent thermal treatment to allow conformal coating of conductive graphene layers around the fabric. The application of the electrode was demonstrated by successful measurements of the electrocardiogram (ECG). Performance of the textile-based electrodes were compared to the conventional silver/silver chloride (Ag/AgCl) electrodes in terms of skin-electrode impedance, ECG signal quality and noise levels. Excellent conformity and cross correlation of 97% was achieved between the signals measured with the new graphene-clad textile electrodes and conventional electrodes.

Graphene-clad textile electrodes for electrocardiogram monitoring

Delineation of ECG characteristic features using multiresolution wavelet analysis method

In this paper, a novel methodology, based on discrete wavelet transform (DWT) is developed for extraction of characteristic features from twelve - lead Electrocardiogram recordings. The first step of this method is to denoise the signal using DWT technique. A multiresolution approach along with thresholding is used for the detection of R - Peaks in each cardiac beats. Followed, by this other fiducial points (Q and S) are detected and QRS onset and offset points are identified. Baseline is also detected and heights of R, Q, S waves are calculated. This, algorithm was validated using PTB diagnostic database giving a sensitivity of 99.6% and MITDB Arrhythmia, giving a sensitivity of 99.8%. The QRS vectors are calculated for normal and patients with Anteroseptal MI and a comparative study is presented. Accordingly, it has been found that classification of normal and AS MI is possible by computing the QRS vector. And a simple classification rule is established for this purpose.

ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform

Myocardial infarction (MI) is a coronary artery disease acquired due to the lack of blood supply in one or more sections of the myocardium, resulting in necrosis in that region. It has different types based on the region of necrosis. In this paper, a statistical approach for classification of Anteroseptal MI (ASMI) is proposed. The first step of the method involves noise elimination and feature extraction from the Electrocardiogram (ECG) signals, using multi-resolution wavelet analysis and thresholding-based techniques. In the next step a classification scheme is developed using the nearest neighbour classification rule (NN rule). Both temporal and amplitude features relevant for automatic ASMI diagnosis are extracted from four chest leads v1–v4. The distance metric for NN classifier is calculated using both Euclidian distance and Mahalanobis distance. A relative comparison between these two techniques reveals that the later is superior to the former, as evident from the classification accuracy. T...

ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier

In the present work, we analyse characteristic features of the pulses photoplethysmographic (PPG) signal and their changes and evolution during various psycologically stressed conditions. Acute psychological stress was induced to subjects using a modified version of Trier Social Stress Test. It was revealed that the morphology of the PPG pulses is different during the streneous sessions from that of the relaxed one and possibly reflect arterial stiffness variations which can be a marker for differentiation of the stressed stages from the relaxed stage of the protocol. Since, the PPG pulse formation is due to the super-imposition of a systolic and a reflected wave, a novel two step Gaussian mixture modelling technique was used to extract their relative arrival time and width. Position (B 1 , B 2 ) and width (C 1 , C 2 ) of the systolic and reflected wave are the relevant features studied along with the time to reflection (T 12 ). Significant statistical differences were observed in B 1 (p 2 (p 1 (p 2 (p < 0.05) in the relaxed and stressed session. These results suggest changes in the morphology of PPG pulses possibly revealing marks of arterial stiffness occurring due to psychological stress.

http://www.cinc.org/archives/2017/pdf/270-035.pdf

Swati Banerjee

Papers

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

Delineation of ECG characteristic features using multiresolution wavelet analysis method

ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform

ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier

A two step Gaussian modelling to assess PPG morphological variability induced by psychological stress