Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

Abstract

Heart arrhythmia is a fairly common medical condition, in which abnormal electrical activity occurs in the heart. However, it can be life threatening if left untreated or undiagnosed. This paper introduces an improved method to classify heart arrhythmia from electrocardiogram (ECG) signals using Block-based Neural Networks (BbNN). BbNNs are used in the hardware implementation of this problem due to its regular block based structure, relatively fast computational speeds, and lower resource consumption. The training mechanism for evolving BbNNs used in the work utilizes Genetic Algorithm (GA), but is able to handle larger sets of training data more efficiently due to an implementation of a novel multithreaded fitness evaluation approach. The ECG heartbeat dataset is taken from the MIT-BIH arrhythmia database, and feature extraction is done using the evaluation of Hermite polynomials on the preprocessed ECG signal. The proposed BbNN system-on-chip (SoC) shows high accuracy in its arrhythmia classification, with an average accuracy of 99.64% for all tested patient records.