Key result
ANN-based HRV classifier matches cardiologist assessment of cardiac health severity with 100% accuracy.
Why the study?
Does an ANN-based HRV classifier accurately categorize cardiac health severity compared to expert cardiologist assessment in patients with suspected cardiac abnormalities?
Cross-Sectional (n=46)
No
Does an ANN-based HRV classifier accurately categorize cardiac health severity compared to expert cardiologist assessment in patients with suspected cardiac abnormalities?
An ANN-based HRV classifier can accurately categorize cardiac health severity, matching the assessment of experienced cardiologists.
Hypothesis-generating for ANN-HRV cardiac classification; should not yet change practice or replace expert assessment.
A multi-layer artificial neural network (ANN)-based heart rate variability (HRV) classifier has been proposed, which gives the cardiac health status as the output based on HRV of the patients independently of the cardiologists' view. The electrocardiogram (ECG) data of 46 patients were recorded in the out-patient department (OPD) of a hospital and HRV was evaluated using self-designed autoregressive-model-based technique. These patients suspected to be suffering from cardiac abnormalities were thoroughly examined by experienced cardiologists. On the basis of symptoms and other investigations, the attending cardiologists advised them to be classified into four categories as per the severity of cardiac health. Out of 46, the HRV data of 28 patients were used for training and data of 18 patients were used for testing of the proposed classifier. The cardiac health classification of each tested patient with the proposed classifier matches with the medical opinion of the cardiologists.
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Sunkaria et al. (2014) conducted a cross-sectional in Suspected cardiac abnormalities (n=46). Artificial neural network (ANN)-based heart rate variability (HRV) classifier vs. Medical opinion of experienced cardiologists was evaluated on Cardiac health classification into four categories of severity. An artificial neural network-based heart rate variability classifier matched the medical opinion of cardiologists for categorizing cardiac health severity in all 18 tested patients.
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