Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features.

ABSTRACT: Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia detection using two standard datasets (MIT-BIH Arrhythmia and the ECG 5000). In the first phase, two types of feature extraction schemes (autoencoder) and (Convolution) are used to obtain relevant features from ECG samples and subsequently, eight ML models are successfully trained and tested to find various performance matrices through simulation-based experiments. Then, the TOPSIS and mRMR ranking schemes are used to rank the ML models and identify the three best-performing models recommended for real-time arrhythmia detection. In this study,

SUBMITTER: Biswas S 

PROVIDER: S-EPMC12704877 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature