NDLI: Different techniques used to improve the performance of a classifier of the twelve-lead electrocardiogram

Content Provider	IEEE Xplore Digital Library
Author	de Chazal, P.
Copyright Year	2001
Description	Author affiliation: Univ. Coll. Dublin, Ireland (de Chazal, P.)
Abstract	Investigates the automatic classification of the 12-lead electrocardiogram (ECG) into different pathophysiological disease categories. The ECG database used in this study contained 926 recordings, with 500 records classified with 100% accuracy and 426 records classified with 75% accuracy. Each record contained a simultaneously recorded 12-lead ECG of 8-10 s duration. Each record is classed as either (i) normal; (ii) left, right or bi-ventricular hypertrophy; or (iii) anterior, inferior or combined myocardial infarction. A baseline classifier was trained using a single beat from the 500 classified recordings and resulted in a 7-way classification test-set accuracy of 55%. The following techniques were used for improving the classification performance: (1) multi-beat data, (2) regularisation of the covariance matrix, and (3) utilisation of inaccurately classified data in the training process. Combining these three techniques resulted in a classifier with a test-set accuracy of over 75%.
Starting Page	525
Ending Page	528
File Size	378978
Page Count	4
File Format	PDF
ISBN	0780372662
ISSN	02766547
DOI	10.1109/CIC.2001.977708
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-09-23
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Databases Testing Band pass filters Covariance matrix Ambient intelligence Educational institutions Australia Diseases Myocardium
Content Type	Text
Resource Type	Article

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