ECG Analysis for Heart Disease Prediction with Vital Parameters

This biomedical monitoring system captures and analyzes vital cardiac signals to assist in early heart disease prediction. At the front end, the AD8232 ECG module interfaces with the human body via electrodes to detect minute electrical potentials generated by the heart, filtering and amplifying the raw analog waveform. Simultaneously, the MAX30102 sensor uses optical plethysmography via infrared and red LEDs to measure pulse oximetry and heart rate parameters. Both the analog ECG signal and the digital sensor readings are routed into the Arduino UNO R4 WiFi. The Arduino processes these incoming bio-signals locally and renders real-time visual output, such as waveforms and vital statistics, onto a connected 0.96-inch OLED display using I2C communication. To perform advanced predictive diagnostics, the Arduino transmits the acquired telemetry data to a host computer running MATLAB. Within the MATLAB environment, a pre-trained classification model evaluates the combined ECG waveforms and vital parameters to identify potential cardiac abnormalities or disease indicators.