Key result
The proposed Extreme Learning Machine based model (ELM-D) exceeded state-of-the-art models in detection accuracy over ECG arrhythmia benchmarks.
Why the study?
Does an Extreme Learning Machine based model (ELM-D) improve detection accuracy of heart arrhythmia in ECG signals compared to state-of-the-art models?
Does an Extreme Learning Machine based model (ELM-D) improve detection accuracy of heart arrhythmia in ECG signals compared to state-of-the-art models?
A novel Extreme Learning Machine model (ELM-D) demonstrates improved accuracy for detecting heart arrhythmias in ECG signals while maintaining low implementation complexity.
May support efficient arrhythmia detection tools; leaves open prospective clinical validation before practice adoption.
Electrocardiogram (ECG) or (EKG) is the common mechanism of recording the electrical signals of the heart to detect heart impairment functionality. In this paper, we proposed a novel Extreme Learning Machine based model for heart arrhythmia detection in the ECG signal (ELM-D). Empirically, the proposed ELM-D model exceeds the state-of-the-art model in the detection accuracy over the ECG arrhythmia benchmarks. In addition, it is quite simple to implement using a small implementation budget. Moreover, it could be implemented on an FPGA or any small hardware device.
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ElSayed et al. (2020) studied Heart arrhythmia. Extreme Learning Machine based model (ELM-D) vs. State-of-the-art models was evaluated on Detection accuracy over ECG arrhythmia benchmarks. The proposed Extreme Learning Machine based model (ELM-D) exceeded state-of-the-art models in detection accuracy over ECG arrhythmia benchmarks.
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