PROPOSED IDEA
This project aims to implement AI based heart disease prediction from the ECG waveform. The abnormalities in the PQRST waveform along with the vital parameters will result in heart diseases like arrhythmia, bundle branch block, ventricular arrhythmias, myocardial infarction, ischemia, atrial enlargement, myocardial ischemia, etc. It also aims to measure the vital parameters such as pulse rate, temperature and SpO2 of the patient by using the wrist band which contains MAX30100 sensor and DS18B20 sensor. The vital parameters above the threshold value will affect the heart function. By measuring the parameters along with the ECG wave will give a precise and accurate prediction of heart disease. Machine Learning algorithms are employed for the disease analysis from the ECG waveform and the vital parameters. The signal processing and machine learning algorithms are developed with the help of the MATLAB platform. The result of the analysis will be displayed in the monitor. The monitor displays the ECG waveform, vital parameters such as temperature, pulse rate and Spo2, the waveform abnormalities and the possible disease related to the abnormalities.

METHODOLOGY
The sensors used in this project are AD8232 (ECG module) with three electrodes, DS18B20 (temperature sensor), MAX30100 (pulse oximeter and heart rate sensor). The two electrodes of the ECG module are attached to the right and left clavicle and the third electrode is attached to the low edge of the left rib cage. Temperature sensor and heart rate sensor are embedded in the wrist band which is attached to the wrist and it measures the temperature, heart rate and SpO2 of the patient.
The data acquired from the microcontroller (Arduino UNO R4 WIFI) are stored, then processed and trained by the MATLAB platform. The result will be displayed on the OLED screen.

BENEFITS
This project aims to provide accurate and precise ECG signals and the disease prediction from the improper waveforms. It detects the early signs of heart disease or abnormalities that might go unnoticed in routine examinations. The data collected by this system can be used for future research purposes and which helps to improve understanding of heart disease mechanisms and better treatment.