Reliable ECG classification using parallel hybrid models with limited resources

Accuracy in classification of electrocardiogram (ECG) signals is vital in healthcare for diagnosing arrhythmias and other cardiovascular disorders. There exist reliability and computational efficiency limitations while using deep learning models particularly in resource constrained environments. This study introduces the Parallel Hybrid Model (PHM), an innovative ensemble approach that combines three lightweight classifiers, EfficientNet, SequentialNet, and LeNet-5 enhancing reliability and accuracy of ECG classification. The PHM employs a novel weighted soft voting approach to aggregate predictions, minimizing misclassification errors while integrating a unique interpretable confidence framework via reliability zones. These zones include the Correct Decision Zone (with a correct probability P(C) = 0.9645), the Misclassification Zone (with an error probability P(E) = 0.0076), and the False Decision Zone (with a false decision probability P(F) = 0.0279), offering a clear measure of confidence. The proposed PHM model is evaluated on the MIT-BIH Arrhythmia Database, achieving an accuracy of 98.46%, outperforming individual models (LeNet-5: 98.13%, EfficientNet: 97.92%, SequentialNet: 98.08%), with an error rate of 1.54%. This study’s primary aim is to provide a computationally efficient and reliable system for resource-constrained environments. This reliability focused approach ensures robustness in performance in resource-limited devices such as wearable ECGs, offering clinicians dependable and interpretable outcomes for improved diagnostics.