Why the study?
Does artificial neural networks (ANN) improve the classification accuracy of ECG signals into normal and arrhythmic compared to linear discriminant analysis (LDA)?
Does artificial neural networks (ANN) improve the classification accuracy of ECG signals into normal and arrhythmic compared to linear discriminant analysis (LDA)?
Artificial neural networks can classify ECG signals into normal and arrhythmic with moderate accuracy, which may help demonstrate the relationship between cardiac function and abnormally low blood glucose.
Answers are grounded in this paper and cite their sources.
ANN ECG classification may aid hypoglycemia-arrhythmia links; leaves open clinical use pending validation.
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.
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Alexakis et al. (2003) studied Hypoglycaemia and cardiac arrhythmia. Artificial neural networks (ANN) vs. Linear discriminant analysis (LDA) was evaluated on Classification accuracy of ECGs into normal and arrhythmic.
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