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Random Forest Classifier Based ECG Arrhythmia Classification

C. Vimal, B. Sathish

Source Title: International Journal of Healthcare Information Systems and Informatics (IJHISI)5(2)

ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781609603960|DOI: 10.4018/jhisi.2010040101

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MLA

Vimal, C., and B. Sathish. "Random Forest Classifier Based ECG Arrhythmia Classification." IJHISI vol.5, no.2 2010: pp.1-10. http://doi.org/10.4018/jhisi.2010040101

APA

Vimal, C. & Sathish, B. (2010). Random Forest Classifier Based ECG Arrhythmia Classification. International Journal of Healthcare Information Systems and Informatics (IJHISI), 5(2), 1-10. http://doi.org/10.4018/jhisi.2010040101

Chicago

Vimal, C., and B. Sathish. "Random Forest Classifier Based ECG Arrhythmia Classification," International Journal of Healthcare Information Systems and Informatics (IJHISI) 5, no.2: 1-10. http://doi.org/10.4018/jhisi.2010040101

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Abstract

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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Random Forest Classifier Based ECG Arrhythmia Classification

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