The Parallel Hybrid Model achieved an ECG classification accuracy of 98.46% and an error rate of 1.54%, outperforming individual lightweight models in resource-constrained environments.
Does the Parallel Hybrid Model (PHM) improve the accuracy and reliability of ECG classification in resource-constrained environments compared to individual lightweight models?
The Parallel Hybrid Model provides a computationally efficient and highly accurate (98.46%) ECG classification system suitable for resource-constrained environments like wearable devices.
Absolute Event Rate: 98.46% vs 98.13%
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.
Alyahya et al. (Mon,) conducted a other in Arrhythmia (n=109,446). Parallel Hybrid Model (PHM) vs. Individual models (LeNet-5, EfficientNet, SequentialNet) was evaluated on Classification accuracy. The Parallel Hybrid Model achieved an ECG classification accuracy of 98.46% and an error rate of 1.54%, outperforming individual lightweight models in resource-constrained environments.
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