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 a compact, interpretable feature-engineering pipeline. 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 five rhythm classes—normal sinus rhythm, sinus bradycardia, sinus tachycardia, ventricular flutter, and atrial fibrillation. Our algorithm achieved an accuracy of $$96.48\% \pm 2.50\%$$ on the MIT-BIH and St. Petersburg INCART arrhythmia databases, with a Cohen’s kappa of $$0.9546 \pm 0.0321$$ and a Matthews correlation coefficient of $$0.9557 \pm 0.0313$$ . The compactness of the model and its low inference latency on a standard CPU suggest that the approach is a promising candidate for deployment on resource-constrained platforms.