2024/12/19 by Christian Mateo Frausto-Avila, Frausto-Avila, Mateo, Jose Pablo Manriquez-Amavizca +5 · 1 citation
Computer Science · Medicine · #Applications (stat.AP) #Computation (stat.CO) #ECG Monitoring and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2412.17852
openalex publication_date 2024/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.