Wavelet Signal Processing of Physiologic Waveforms

The prime objective of this piece of work is to devise novel techniques for computer based classification of Electrocardiogram (ECG) arrhythmias with a focus on less computational time and better accuracy. As an initial stride in this direction, ECG beat classification is achieved by using feature extracting techniques to make a neural network (NN) system more effective. The feature extraction technique used is Wavelet Signal Processing. Coefficients from the discrete wavelet transform were used to represent the ECG diagnostic information and features were extracted using the coefficients and were normalised. These feature sets were then used in the classifier i.e. a simple feed forward back propagation neural network (FFBNN). This paper presents a detail study of the classification accuracy of ECG signal by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-Layered perceptron (MLP) with back propagation training algorithm. The ECG signals have been taken from MIT-BIH ECG database, and are used in training to classify 3 different Arrhythmias out of ten arrhythmias. These are normal sinus rhythm, paced beat, left bundle branch block. Before testing, the proposed structures are trained by back propagation algorithm. The results show that the wavelet decomposition method is very effective and efficient for fast computation of ECG signal analysis in conjunction with the classifier

Diagnosis of Arrhythmia Using Neural Networks

This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive

Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers” has been accepted for publication in Biomedical Signal Processing and Control, 47, 41–48. The Version of Record is available online at: https://doi.org/10.1016/j.bspc.2018.08.007.[Abstract]: A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.This work was partially supported by the Research Project RTC-2016-5143-1, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). Also, this work has received financial support from the ERDF and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

Use of Wavelets in Electrocardiogram Research: a Literature Review

Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy

Contributions To The Methodology Of Electrocardiographic Imaging (ECGI) And Application Of ECGI To Study Mechanisms Of Atrial Arrhythmia, Post Myocardial Infarction Electrophysiological Substrate, And Ventricular Tachycardia In Patients

ABSTRACT OF THE DISSERTATION Contributions to the Methodology of Electrocardiographic Imaging: ECGI) and Application of ECGI to Study Mechanisms of Atrial Arrhythmia, Post Myocardial Infarction Electrophysiological Substrate, and Ventricular Tachycardia in Patients by Yong Wang Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2009 Professor Yoram Rudy, Chair Electrocardiographic Imaging: ECGI) is a noninvasive imaging modality for cardiac electrophysiology and arrhythmia. ECGI reconstructs epicardial potentials, electrograms and isochrones from body-surface electrocardiograms combined with heart-torso geometry from computed tomography: CT). The application of a new meshless method, the Method of Fundamental Solutions: MFS) is introduced to ECGI with the following major advantages: 1. Elimination of meshing and manual mesh optimization processes, thereby enhancing automation and speeding the ECGI procedure. 2. Elimination of mesh-induced artifacts. 3. Simpler implementation. These properties of MFS enhance the practical application of ECGI as a clinical diagnostic tool. The current ECGI mode of operation is offline with generation of epicardial potential maps delayed to data acquisition. A real time ECGI procedure is proposed, by which the epicardial potentials can be reconstructed while the body surface potential data are acquired: \u3c 1msec/frame) during a clinical procedure. This development enables real-time monitoring, diagnosis, and interactive guidance of intervention for arrhythmia therapy. ECGI is applied to map noninvasively the electrophysiological substrate in eight post-MI patients during sinus rhythm: SR). Contrast-enhanced MRI: ceMRI) is conducted to determine anatomical scar. ECGI imaged regions of electrical scar corresponded closely in location, extent, and morphology to the anatomical scars. In three patients, late diastolic potentials are imaged in the scar epicardial border zone during SR. Scar-related ventricular tachycardia: VT) in two patients are imaged, showing the VT activation sequence in relation to the abnormal electrophysiological substrate. ECGI imaging the substrate in a beat-by-beat fashion could potentially help in noninvasive risk stratification for post-MI arrhythmias and facilitate substrate-based catheter ablation of these arrhythmias. ECGI is applied to eleven consecutive patients referred for VT catheter ablation procedure. ECGI is performed either before: 8 patients) or during: 3 patients) the ablation procedure. Blinded ECGI and invasive electrophysiology: EP) study results are compared. Over a wide range of VT types and locations, ECGI results are consistent with EP data regarding localization of the arrhythmia origin: including myocardial depth) and mechanism: focal, reentrant, fascicular). ECGI also provides mechanistic electrophysiological insights, relating arrhythmia patterns to the myocardial substrate. The study shows ECGI has unique potential clinical advantages, especially for hemodynamically intolerant VT or VT that is difficult to induce. Because it provides local cardiac information, ECGI may aid in better understanding of mechanisms of ventricular arrhythmia. Further prospective trials of ECGI with clinical endpoints are warranted. Many mechanisms for the initiation and perpetuation of atrial fibrillation: AF) have been demonstrated over the last several decades. The tools to study these mechanisms in humans have limitations, the most common being invasiveness of a mapping procedure. In this paper, we present simultaneous noninvasive biatrial epicardial activation sequences of AF in humans, obtained using the Electrocardiographic Imaging: ECGI) system, and analyzed in terms of mechanisms and complexity of activation patterns. We performed ECGI in 36 patients with a diagnosis of AF. To determine ECGI atrial accuracy, atrial pacing from different sites was performed in six patients: 37 pacing events), and ECGI was compared to registered CARTO images. Then, ECGI was performed on all 36 patients during AF and ECGI epicardial maps were analyzed for mechanisms and complexity. ECGI noninvasively imaged the low-amplitude signals of AF in a wide range of patients: 97% procedural success). The spatial accuracy in determining initiation sites as simulated by atrial pacing was ~ 6mm. ECGI imaged many activation patterns of AF, most commonly multiple wavelets: 92%), with pulmonary vein: 69%) and non-pulmonary vein: 62%) trigger sites. Rotor activity was seen rarely: 15%). AF complexity increased with longer clinical history of AF, though the degree of complexity of nonparoxysmal AF varied and overlapped. ECGI offers a way to identify unique epicardial activation patterns of AF in a patient-specific manner. The results are consistent with contemporary animal models of AF mechanisms and highlight the coexistence of a variety of mechanisms among patients

A 12-Lead ECG Database to Identify Origins of Idiopathic Ventricular Arrhythmia Containing 334 Patients

Cardiac catheter ablation has shown the effectiveness of treating the idiopathic premature ventricular complex and ventricular tachycardia. As the most important prerequisite for successful therapy, criteria based on analysis of 12-lead ECGs are employed to reliably speculate the locations of idiopathic ventricular arrhythmia before a subsequent catheter ablation procedure. Among these possible locations, right ventricular outflow tract and left outflow tract are the major ones. We created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract to left ventricular outflow tract. The dataset contains 334 subjects who successfully underwent a catheter ablation procedure that validated the accurate origins of idiopathic ventricular arrhythmia

Hybrid Wavelet-Support Vector Classifiers

The Support Vector Machine (SVM) represents a new and very promising technique for machine learning tasks involving classification, regression or novelty detection. Improvements of its generalization ability can be achieved by incorporating prior knowledge of the task at hand. We propose a new hybrid algorithm consisting of signal-adapted wavelet decompositions and SVMs for waveform classification. The adaptation of the wavelet decompositions is tailormade for SVMs with radial basis functions as kernels. It allows the optimization Of the representation of the data before training the SVM and does not suffer from computationally expensive validation techniques. We assess the performance of our algorithm against the background of current concerns in medical diagnostics, namely the classification of endocardial electrograms and the detection of otoacoustic emissions. Here the performance of SVMs can significantly be improved by our adapted preprocessing step

Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization