Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier
2016 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 40, no 4, 108- p.Article in journal (Refereed) PublishedText
In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F- measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).
Place, publisher, year, edition, pages
SPRINGER , 2016. Vol. 40, no 4, 108- p.
Electrocardiogram (ECG); Multiscale Principal Component Analysis (MSPCA); Discrete Wavelet Transform (DWT); Decision Tree; Random Forest (RF); Heart arrhythmia
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-126802DOI: 10.1007/s10916-016-0467-8ISI: 000371468500023PubMedID: 26922592OAI: oai:DiVA.org:liu-126802DiVA: diva2:917729