NDLI: Arrhythmia detection using amplitude difference features based on random forest

Content Provider	IEEE Xplore Digital Library
Author	Park, J. Seunghan Lee Kyungtae Kang
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Hanyang Univ., Ansan, South Korea (Park, J.; Seunghan Lee; Kyungtae Kang)
Abstract	A number of promising studies have been proposed for diagnosing arrhythmia, using classification techniques based on a variety of heartbeat features by the interpretation of electrocardiogram (ECG). In this study, a new feature called amplitude difference was investigated using the random forest classifier. Evaluations conducted against the MIT-BIH arrhythmia database before and after adding the amplitude difference features obtained heartbeat classification accuracies of 98.51% and 98.68%, respectively. To validate the significance of the increased performance, the Wilcoxon signed rank test was extensively employed. By the absolute preponderance of plus ranks, we confirmed that applying an amplitude difference feature for heartbeat classification improves their performance.
Starting Page	5191
Ending Page	5194
File Size	724125
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart beat Electrocardiography Feature extraction Accuracy Databases Sensitivity Neural networks
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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