Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network-Part I : Model Development

  title={Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network-Part I : Model Development},
  author={A. Ponnle},
Electrocardiogram (ECG) is a graphic recording of the electrical activity produced by the heart. The accuracy of any electrocardiogram waveform extraction plays a vital role in helping a better diagnosis of any heart related illnesses. We present a computer-aided application model for detection of cardiac arrhythmia in ECG signal, which consists of signal pre-processing and detection of the ECG signal components adapting Pan-Tompkins and Hamilton-Tompkins algorithms; feature extraction from the… Expand
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The analysis of the ECG can benefit in diagnosing most of the heart diseases. The electrocardiogram (ECG) provides almost all information about electrical activity of the heart. One cardiac cycle inExpand
ECG Beats Classification Using Mixture of Features
In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. Expand
Electro Cardiogram (ECG) is a non invasive technique and is used as a primary diagnostic tool for cardiovascular diseases. ECG Signal provides necessary information about electrophysiology of heartExpand
Detection of QRS Complexes in 12-lead ECG using Adaptive Quantized Threshold
†† Summary The QRS complex is the most prominent wave component within the electrocardiogram. It reflects the electrical activity of heart during the ventricular contraction and the time of itsExpand
ECG Data-Acquisition and classification system by using wavelet-domain Hidden Markov Models
This article is concerned with the classification of ECG pulses by using state of the art Continuous Density Hidden Markov Models (CDHMM's) and the types of beat being selected are normal, premature ventricular contraction, and normal rhythm. Expand
Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification
  • P. Tadejko, W. Rakowski
  • Computer Science
  • 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07)
  • 2007
In this study, a classifier was developed with SOM and learning vector quantization (LVQ) algorithms using the data from the records recommended by ANSI/AAMI EC57 standard, and the results of recognition beats either as normal or arrhythmias was improved. Expand
Statistics over features of ECG signals
The usage of statistics over the set of the features representing the electrocardiogram (ECG) signals confirmed that the proposed MLPNN has potential in detecting the variabilities of the ECG signals. Expand
ECG Feature Extraction Techniques - A Survey Approach
Various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal based on Fuzzy Logic Methods, Artificial Neural Networks, Genetic Algorithm, Support Vector Machines, and other Signal Analysis techniques are discussed. Expand
Feature extraction from ECG signals using wavelet transforms for disease diagnostics
A modified combined wavelet transform technique that has been developed to analyse multilead electrocardiogram signals for cardiac disease diagnostics and two alternate diagnostic criteria have been used to check the diagnostic authenticity of the test results. Expand
The principles of software QRS detection
The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction. Expand