Why the study?
Traditional CNN cannot be used directly for heart disease classification from ECG signals because of noise and irrelevant data, necessitating proper preprocessing and feature selection.
Population
ECG signals from the Standard MIT-BIH arrhythmia database across 16 heartbeat disease categories
Comparison
Hybrid CNN architecture with GOA vs other state-of-the-art techniques
Design
Machine learning model development and validation study
Authors
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May aid automated arrhythmia detection; leaves open prospective validation before clinical use.
A hybrid Convolutional Neural Network optimized with the Grasshopper Optimization Algorithm can classify ECG arrhythmias with extremely high accuracy, offering a robust tool for automated heart disease detection.
Tyagi et al. (2021) studied this question.
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