Revett, Kenneth and Gorunescu, Florin and El-Dahshan, El-Sayed and Salem, Abdel-Badeeh M. (2009) Machine learning in electrocardiogram diagnosis. In: Proceedings of the International Multiconference on Computer Science and Information Technology 12 - 14 Oct 2009, Mragowo, Poland. IMCSIT, pp. 429-433. ISBN 9788360810224
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Official URL: http://www.proceedings2009.imcsit.org/pliks/10.pdf
The electrocardiogram (ECG) is a measure of the electrical activity of the heart. Since its introduction in 1887 by Waller, it has been used as a clinical tool for evaluating heart function. A number of cardiovascular diseases (CVDs)(arrhythmia, atrial fibrillation, atrioventricular (AV) dysfunctions, and coronary arterial disease, etc.) can be detected non-invasively using ECG monitoring devices. With the advent of modern signal processing and machine learning techniques, the diagnostic power of the ECG has expanded exponentially. The principal reason for this is the expanded set of features that are typically extracted from the ECG time series. The enhanced feature space provides a wide range of attributes that can be employed in a variety of machine learning techniques, with the goal of providing tools to assist in CVD classification. This paper summarizes some of the principle machine learning approaches to ECG classification, evaluating them in terms of the features they employ, the type(s) of CVD(s) to which they are applied, and their classification accuracy.
|Item Type:||Book Section|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||06 Jan 2010 11:11|
|Last Modified:||06 Jan 2010 11:11|
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