Detection of Potential Hypertension with Pan Tompkins Extraction and Naive Bayes Classifier Methods

This study addresses the pressing need for early detection of heart abnormalities to combat the rising mortality rates from heart disease. Utilizing an assembled electrocardiogram (ECG) device connected with AD8232 sensors and Arduino Nano, data from 20 students in various physical activity conditions were collected. Employing the Pan Tompkins algorithm, which includes a bandpass filter to eliminate noise, the ECG signals were processed to detect peak PQRST intervals. Subsequently, the intervals were analyzed to classify abnormalities in heart rate. Testing the data using the Naive Bayes method yielded an impressive accuracy rate of 96.7%, suggesting the efficacy of the proposed approach in early detection and prevention of cardiovascular issues.