Why the study?
Does a Random Forest classifier using HRV and DWT features accurately detect cardiac arrhythmias from ECG data?
Does a Random Forest classifier using HRV and DWT features accurately detect cardiac arrhythmias from ECG data?
A Random Forest classifier using HRV and DWT features can achieve high accuracy (>98%) in detecting cardiac arrhythmias from ECG data.
Supports ML-based ECG arrhythmia detection tools; leaves open prospective clinical validation before any practice change.
Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.
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Vimal et al. (2010) studied this question.
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