ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks

TL;DR

This paper presents a CNN-based deep learning system for classifying ECG signals to detect arrhythmias, achieving high accuracy on the MIT-BIH dataset by categorizing five main arrhythmia types.

Contribution

The study introduces a residual CNN architecture for ECG classification, demonstrating improved accuracy in arrhythmia detection over existing methods.

Findings

Achieved 98.2% classification accuracy on 15,000 ECG cases.

Successfully classified five main ECG arrhythmia groups.

Validated effectiveness using the MIT-BIH dataset.

Abstract

Arrhythmia is just one of the many cardiovascular illnesses that have been extensively studied throughout the years. Using multi-lead ECG data, this research describes a deep learning (DL) pipeline technique based on convolutional neural network (CNN) algorithms to detect cardiovascular lar arrhythmia in patients. The suggested model architecture has hidden layers with a residual block in addition to the input and output layers. In this study, the classification of the ECG signals into five main groups, namely: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat (N), are performed. Using the MIT-BIH arrhythmia dataset, we assessed the suggested technique. The findings show that our suggested strategy classified 15,000 cases with a high accuracy of 98.2%