Compact Neural Network Algorithm for Electrocardiogram Classification

TL;DR

This paper introduces a compact, feature-engineered neural network for ECG classification that achieves high accuracy without deep learning, suitable for real-time, resource-limited medical settings.

Contribution

The work presents a simple ANN combined with 17 engineered features, reducing computational demands while maintaining state-of-the-art classification performance.

Findings

Achieved 97.36% accuracy on MIT-BIH and INCART databases.

Effectively classifies 4 arrhythmia types with high efficiency.

Demonstrates feasibility for real-time, resource-limited applications.

Abstract

In this paper, we present a powerful, compact electrocardiogram (ECG) classification algorithm for cardiac arrhythmia diagnosis that addresses the current reliance on deep learning and convolutional neural networks (CNNs) in ECG analysis. This work aims to reduce the demand for deep learning, which often requires extensive computational resources and large labeled datasets. Our approach introduces an artificial neural network (ANN) with a simple architecture combined with advanced feature engineering techniques. A key contribution of this work is the incorporation of 17 engineered features that enable the extraction of critical patterns from raw ECG signals. By integrating mathematical transformations, signal processing methods, and data extraction algorithms, our model captures the morphological and physiological characteristics of ECG signals with high efficiency, without requiring deep learning. Our method demonstrates a similar performance to other state-of-the-art models in classifying 4 types of arrhythmias, including atrial fibrillation, sinus tachycardia, sinus bradycardia, and ventricular flutter. Our algorithm achieved an accuracy of 97.36% on the MIT-BIH and St. Petersburg INCART arrhythmia databases. Our approach offers a practical and feasible solution for real-time diagnosis of cardiac disorders in medical applications, particularly in resource-limited environments.