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An ANN-based HRV classifier for cardiac health prognosis
Ramesh Kumar Sunkaria1, Vinod Kumar1, Suresh Chandra Saxena1
1Electrical Engineering Department, Indian Institute of Technology Roorkee, Roorkee (Uttaranchal), 247667, India.
Insights
A novel artificial neural network (ANN) classifier accurately predicts cardiac health using heart rate variability (HRV) from electrocardiogram (ECG) data, matching cardiologist assessments.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Biomedical Signal Processing
Background:
- Cardiac health assessment traditionally relies on cardiologist interpretation of patient data.
- Heart Rate Variability (HRV) analysis offers a non-invasive method for evaluating cardiac status.
- Existing methods may lack objective, automated classification of cardiac health severity.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN)-based classifier for cardiac health.
- To assess the performance of the ANN classifier using Heart Rate Variability (HRV) data.
- To compare the ANN classifier's output with expert cardiologist diagnoses.
Main Methods:
- Electrocardiogram (ECG) data were collected from 46 patients suspected of cardiac abnormalities.
- Heart Rate Variability (HRV) was calculated using a self-designed autoregressive-model-based technique.
- A multi-layer artificial neural network (ANN) was trained on 28 patients' HRV data and tested on 18 patients' data.
Main Results:
- The proposed ANN-based HRV classifier successfully categorized patients' cardiac health.
- The classification results from the ANN model demonstrated a high degree of agreement with cardiologist opinions.
- The system provides cardiac health status independently of direct cardiologist input.
Conclusions:
- An ANN-based HRV classifier can accurately and objectively assess cardiac health status.
- This automated approach shows potential for supporting clinical decision-making in cardiology.
- The findings suggest the utility of machine learning in analyzing ECG and HRV for cardiac diagnostics.
Abstract:
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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