- Train data includes ECG signals with patients with no history of MI or CA,
- Further categorization based on patient history including gender, smoker, hypertension, previous MI/CA conditions
- Normal patients' training data: normalization and undergoes Fourier transform (FTT) to detect ECG signals peaks and establish a pattern for PRQST positions and amplitudes
- Cross-correlate signals between normal patients and patients with history of CA and MI to differentiate and map patterns due to history (smoker, hypertension)
- Establish a threshold for patients to start incurring/recurring MI or CA
- Build a GUI for easy user- friendly interface