Computer-Aided Arrhythmia Diagnosis by Learning ECG Signal

TL;DR

This paper presents a neural network-based approach for arrhythmia detection from ECG signals, utilizing optimized activation functions and a self-learning method to improve accuracy and computational efficiency.

Contribution

It introduces a novel neural network with piecewise linear activation functions and a self-learning technique for ECG analysis, enhancing accuracy and reducing complexity.

Findings

Achieved 97.28% accuracy with self-learning method.

Achieved 99.56% accuracy with optimized neural network.

Improved computational efficiency over traditional methods.

Abstract

Electrocardiogram (ECG) is one of the non-invasive and low-risk methods to monitor the condition of the human heart. Any abnormal pattern(s) in the ECG signal is an indicative measure of malfunctioning of the heart, termed as arrhythmia. Due to the lack of human expertise and high probability to misdiagnose, computer-aided diagnosis and analysis are preferred. In this work, we perform arrhythmia detection with an optimized neural network having piecewise linear approximation based activation function to alleviate the complex computations in the traditional activation functions. Further, we propose a self-learning method for arrhythmia detection by learning and analyzing the characteristics (period) of the ECG signal. Self-learning based approach achieves 97.28% of arrhythmia detection accuracy, and neural network with optimized activation functions achieve an arrhythmia detection accuracy of 99.56%.