ECG Signal Analysis for Automated Cardiac Arrhythmia Detection

Abstract

The graphical recordings of electrical stimuli generated by heart muscle cells are known as an electrocardiogram (ECG). In cardiology, ECG is widely used to detect different cardiovascular diseases among which arrhythmias are the most common. Irregular heart cycles are collectively known as arrhythmias and may produce sudden cardiac arrest. Many times, arrhythmia evolves over an extended period. Hence, it requires an artificial-intelligence-enabled continuous ECG monitoring system that can detect irregular heart cycles automatically. In this regard, this chapter presents a methodological analysis of machine-learning and deep-learning-based arrhythmia detection techniques. Focusing on the state of the art, a deep-learning-based technique is implemented which recognizes normal heartbeat and seven different classes of arrhythmias. This technique uses a convolutional neural network as a classification tool. The performance of this technique is evaluated using ECG records of the MIT-BIH arrhythmia database. This technique performs well in terms of different classification metrics.