Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia

ALEXAKIS, C., NYONGESA, H. O., SAATCHI, R., HARRIS, N. D., DAVIES, C., EMERY, C., IRELAND, R. H. and HELLER, S. R. (2003). Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia. In: 30th Annual Meeting on Computers in Cardiology Conference, Thessaloniki Chalkidiki, GREECE , SEP 21-24, 2003 . 537-540.

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Abstract

Nocturnal hypoglycaemia has been implicated in the sudden deaths of young people with diabetes. Experimental hypoglycaemia has been found to prolong the ventricular repolarisation and to affect the T wave morphology. It is postulated that abnormally low blood glucose could in certain circumstances, be responsible for the development of a fatal cardiac arrhythmia. We have used automatic extraction of both time-interval and morphological features, from the Electrocardiogram (ECG) to classify ECGs into normal and arrhythmic. Classification was implemented by artificial neural networks (ANN) and Linear Discriminant Analysis (LDA). The ANN gave more accurate results. Average training accuracy of the ANN was 85.07% compared with 70.15% on unseen data. This study may lead towards the demonstration of the possible relationship between cardiac function and abnormally low blood glucose.

Item Type: Conference or Workshop Item (Paper)
Research Institute, Centre or Group: Materials and Engineering Research Institute > Centre for Robotics and Automation > Mobile Machine and Vision Laboratory
Depositing User: Danny Weston
Date Deposited: 13 Apr 2010 16:03
Last Modified: 09 Aug 2010 13:46
URI: http://shura.shu.ac.uk/id/eprint/1660

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