Key result
Covering rough set classification outperforms other classifiers in diagnosing cardiac arrhythmia from ECG signals.
A novel covering rough set-based classification method demonstrates improved performance over existing classifiers for detecting cardiac arrhythmias from ECG signals.
May aid arrhythmia detection algorithms; leaves open prospective clinical validation before diagnostic adoption.
The objective of this work is to use data processing methods to unravel the biomedical difficulties of detecting a selection of arrhythmia conditions from patient's electrocardiograph (ECG) signals. ECG graphical signal record waveforms square portion analysed supported morphological variations between irregular waves and regular waves. The Pan-Tompkins (PT) method is applied for takeout P, R, Q, S and T morphological peaks and wavelet transform (WT) temporal features are tested on five categories of graphical record ECG signals. During this paper, we tend to propose the covering rough set (CRS)-based classification method for classification of heartbeats to sign interior cardiac arrhythmia in ECG signals. The proposed classification system is tested using ECG records in Physiobank databases and also the results were compared to those from many prior studies. Experimental results show that our proposed approach in truth diagnoses heart cardiac arrhythmia. The experimental results show that the proposed system outperforms other classifiers.
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Kumar et al. (2017) studied Cardiac arrhythmia. Covering rough set (CRS)-based classification method vs. Other classifiers was evaluated on Classification of heartbeats to detect cardiac arrhythmia. A covering rough set (CRS)-based classification method successfully diagnosed cardiac arrhythmia from ECG signals and outperformed other classifiers.
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