Dual Space DTR-Net Learning Based Heart Disease Prediction

The biggest cause of death worldwide is a heart attack. If a heart attack can be identified early and treated right away, many lives can be spared. In this study, we suggest a wireless ECG sensor connected to an in-dash ECG monitoring system as the basis for a heart attack detection and alert system. We employ an artificial neural network (DTR Net) pretrained model on ECG signals for heart attack detection. When a heart attack is discovered, the system immediately uses a satellite communication system to notify co-passengers, local hospitals, and paramedics. The suggested solution comprises a wireless ECG sensor that is fastened to the driver's body and sends ECG signals to an ECG monitoring system built into the car's dashboard. Signal processing methods are used to extract the characteristics after the ECG signals have undergone pre-processing to reduce noise and artefacts. The DTR Net model for heart attack detection is then fed the retrieved features. To precisely identify the existence of a heart attack, the DTR Net model is trained using a substantial dataset of ECG signals. When the DTR Net model recognizes a heart attack, the system immediately sends out a satellite communication system alert to co-passengers, local hospitals, and ambulances. This enables quick medical care to be given, possibly saving lives. In conclusion, our suggested approach for early heart attack detection and alerting in automobiles, which combines a wireless ECG sensor with an DTR Net model, is reliable and effective. By automatically alerting local hospitals, ambulances, and fellow passengers, the technology makes sure that immediate medical assistance is given, potentially saving lives. The technology can reduce fatalities and improve road safety by enabling immediate medical care.