# Compact Neural Network Algorithm for Electrocardiogram Classification

arXiv: https://arxiv.org/abs/2412.17852 · HTML: https://mlsanity.com/paper/2412.17852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ECG arrhythmia classification using a 2-D convolutional neural network](https://mlsanity.com/paper/1804.06812/) (2018)
- [Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction](https://mlsanity.com/paper/2404.15347/) (2024)
- [Identification of particle mixtures using machine-learning-assisted laser diffraction analysis](https://mlsanity.com/paper/2101.11402/) (2021)

## Built on by (most cited first, 1 of 1)

- [CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG](https://mlsanity.com/paper/2509.25804/) (2025)

## Abstract (arXiv)

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.