Real-time ECG signal pre-processing and neuro fuzzy-based CHD risk prediction

Coronary heart disease (CHD) is a major chronic disease which is directly responsible for myocardial infarction. Heart rate variability (HRV) has been used for the prediction of CHD risk in human beings. In this work, neuro fuzzy-based CHD risk prediction is performed after performing pre-processing and HRV feature extraction. The pre-processing is used to remove high frequency noise which is modelled as white Gaussian noise. The real-time ECG signal acquisition, pre-processing and HRV feature extraction are performed using NI LabVIEW and DAQ board. A 30 seconds recording of ECG signal was selected in both smokers and non-smokers. Various statistical parameters are extracted from HRV to predict coronary heart disease (CHD) risk among the subjects. The HRV extracted signals are classified into normal and CHD risky subjects using neuro fuzzy classifier. The classification performance of the neuro fuzzy classifier is compared with the ANN, KNN, and decision tree classifiers.