Automated cardiac condition diagnosis using AI based ECG analysis system for school children

Major focus of this paper is in the development and testing of a prototype of Electrocardiogram (ECG) machine intended for automatic analysis of cardiovascular diseases by applying artificial intelligence. The objective of the work is in cardiac screening of school children at rural areas, in order to detect the cardiac diseases at its early stages. This work has focused to differentiate ECG signals of people into arrhythmia affected, congestive heart failure, and normal sinus rhythm. For feature extraction from ECG signal, wavelet time scattering methodology has been used and a Support Vector Machine (SVM) classifier is employed to accurately distinguish between ECG signals, which were carried out in MATLAB toolbox. A hardware system of interfaced ARDUINO UNO and ECG sensor AD8232 has been developed and the entire system is tested on group members and predictions were made accurately. Testing with school children is pending due to concerns about COVID-19 safety issues.