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.

翻译：本文提出了一种强大且紧凑的心电图（ECG）分类算法，用于心律失常诊断，旨在解决当前ECG分析中对深度学习和卷积神经网络（CNN）的依赖。该工作致力于降低对深度学习的需求，因为深度学习通常需要大量计算资源和标注数据集。我们的方法引入了一种架构简单的人工神经网络（ANN），并结合了先进的特征工程技术。本工作的一个关键贡献是纳入了17个工程化特征，这些特征能够从原始ECG信号中提取关键模式。通过整合数学变换、信号处理方法和数据提取算法，我们的模型高效地捕捉了ECG信号的形态学和生理学特征，而无需依赖深度学习。我们的方法在分类4种心律失常（包括心房颤动、窦性心动过速、窦性心动过缓和心室扑动）时，表现出与其他先进模型相似的性能。该算法在MIT-BIH和圣彼得堡INCART心律失常数据库上达到了97.36%的准确率。我们的方法为医疗应用中实时诊断心脏疾病提供了一种实用且可行的解决方案，特别适用于资源有限的环境。