<ul><li>A high-performance, compact electrocardiogram (ECG)-based system for automatic classification of arrhythmias is presented in this paper.</li><li>The system integrates machine learning approaches and feature enhancement techniques to capture morphological and time-frequency features from ECG signals.</li><li>It includes 17 newly engineered features to extract significant data and physiological patterns from the ECG signal.</li><li>The system achieves an accuracy of 97.36% on the MIT-BIH arrhythmia database and shows potential for clinical deployment and portable devices in cardiac health monitoring applications.</li></ul>

Compact Neural Network Algorithm for Electrocardiogram Classification

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