• Miss. Seema Paighan
• Prof. Anant More
• Prof. Rajesh Shekokar3

Cardiovascular disease is the leading global cause of death. A normal heart rate is 60-100 beats per minute. However, heart rate higher than 76 beats per minute when in resting may be linked to a higher risk of a heart attack. It is very difficult for a doctor to read an ECG report with bare eyes. At times, there is a high chance to miss out any abnormality in the ECG report as the change in the ECG wave shape is hardly noticeable. This paper gives cardiac anomaly prediction and monitoring ECG signal using the raspberry pi. We used an ECG module Ad8232 interfaced with Arduino board which communicates with raspberry Pi for sending the real-time sensor data to the server using WiFi technology to predict the possibility of heart disease. The disease prediction is performed by using Support Vector Machine classifier and for maintaining the security about personal health-related data we are using an encryption scheme. Hereby providing real-time values, of ECG signal we achieved 95% accuracy in disease prediction.

Cardiac Anomalies Detection, Machine Learning, Real time Parameter, ECG monitoring, Support Vector Machine (SVM), Encryption

"An ECG Monitoring System for Prediction of Cardiac Anomalies using Support Vector Machine Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.389-396, June 2019, Available :http://www.jetir.org/papers/JETIR1907F52.pdf

"An ECG Monitoring System for Prediction of Cardiac Anomalies using Support Vector Machine Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp389-396, June 2019, Available at : http://www.jetir.org/papers/JETIR1907F52.pdf