Key result
An artificial neural network using heart rate variability parameters successfully classified cardiac diseases with an accuracy of 80-85%.
Why the study?
Does an artificial neural network using heart rate variability parameters accurately classify heart diseases?
Does an artificial neural network using heart rate variability parameters accurately classify heart diseases?
An artificial neural network utilizing heart rate variability parameters can classify various cardiac diseases with 80-85% accuracy.
Answers are grounded in this paper and cite their sources.
Supports exploratory HRV-ANN use for cardiac classification; hypothesis-generating and requires prospective clinical validation.
Electrocardiogram (ECG) is acquisition of electrical activity signals in cardiology. It contains important information about the condition and diseases of heart. An ECG wave, pattern, size, shape and the time interval between different peaks of P-QRS-T wave provide useful information about the diseases which afflict heart. Heart rate signals vary and this variation contains important indicators of cardiac diseases. To assess autonomic nervous system, heart rate variability is popular and non-invasive tool. These indicators contained in ECG wave appear all the day or occur randomly in the day. So, computer based information is much useful over day long interval to diagnose heart disease. Thus, this paper deals with classification of heart diseases on the basis of heart rate variability using artificial neural network. Feed forward neural network is considered to be almost correct 85% of the test results.
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Kakar et al. (2018) studied Cardiac diseases (Complete heart block, Ischemic, Sick sinus syndrome, Left bundle branch block) (n=500). Artificial Neural Network (ANN) using Heart Rate Variability was evaluated on Classification accuracy of the feed forward neural network. An artificial neural network using heart rate variability parameters successfully classified cardiac diseases with an accuracy of 80-85%.
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