Why the study?
Automatic detection and classification of different types of arrhythmias by analyzing ECG signals is essential for diagnosis of cardiac defects, motivating development of efficient classifiers.
Absolute Event Rate: 98.62% vs 95.6%
May support hybrid neural network development for arrhythmia detection; leaves open prospective clinical validation before adoption.
Automatic detection and classification of different types of arrhythmias by analyzing the ECG signal is essential for diagnosis of cardiac defects. Thus the focus of this paper is to propose an efficient classifier to classify the ECG signal into different arrhythmias such as Normal, VE, VF, LBBB and APB to support in diagnosis. Initially Histogram features are extracted from RR interval, PT interval, PR interval, TT interval, ST interval, QT interval of the ECG signal. Then the Real Coded Genetic Algorithm (RCGA) is applied to identify the optimal feature set from the extracted histogram features. After that the Multilayer feed forward neural network (MFFNN) evolved using Particle Bee approach. The performance of the proposed RCGA-PBNN has been evaluated in terms of classification accuracy, sensitivity and specificity using the MIT-BIH arrhythmias ECG Database.
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Priscila et al. (2018) studied Cardiac arrhythmia (n=250). Real Coded Genetic Algorithm with Particle Bee-Neural Network (RCGA-PBNN) vs. Multi-Layer Feed Forward Neural Network (MFNN) and Support Vector Machine (SVM) was evaluated on Classification accuracy.
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