Random Forest Classifier Based ECG Arrhythmia Classification-Reference-Cited by-同舟云学术

Random Forest Classifier Based ECG Arrhythmia Classification

Published:2012 Issue: Volume: Page:189-198
ISSN:
Container-title:Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs
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Author:

V.Mahesh ¹,Kandaswamy A.²,Vimal C.²,Sathish B.²

Affiliation:

1. V.MaheshPSG College of Technology, India

2. PSG College of Technology, India

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.

Publisher

IGI Global

Reference19 articles.

1. Classification of cardiac abnormalities using heart rate signals

2. Classification of heart rate data using artificial neural network and fuzzy equivalence relation

3. Babak, M.-A., & Seyed, K. S. (2006). Neural Network Based Arrhythmia Classification using Heart Rate Variability Signal. In Proceedings of the XIV European Signal Processing Conference.

4. Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features

5. Cardiac Arrhythmia Classification using Autoregressive Modeling.;G.Dingfei;Biomedical Engineering Online,2002

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