In the existing methods the epilepsy was analysed using the PPG, EEG, ECG. The output obtained from the two different signals were analysed and found that the signal obtained while using ECG wearable sensor was more accurate and reliable when compared to the result obtained while using PPG. The main drawback while using EEG signal is the placement of wet scalp electrodes. Hence heart rate is used to analyse epilepsy in the proposed methodin order to obtain accurate and reliable result. In this method analysing the heart rate signal of the patient and epilepsy death rate prediction are two main goals of the project. The biomedical sensors and wearable sensors currently available in the market was studied. Sensors like EEG, ECG, PPG characteristics, and their working were studied completely. In this paper, we have not used any biomedical sensors or wearable sensors. For the heart rate signal, the dataset from the “fitbase” website was collected. Heart rate data collection per minute average heartbeat of the person. heart rate signal data length was 12,000 But only 90 samples were used for ensemble averaging. Not only the heart rate of the signal the epilepsy was also focussed. The epilepsy death rate data from the Statista. Epilepsy data contains year and death for future prediction; a linear regression algorithm was used.