The Intelligent Heart Rate Monitoring Model for Survivability Prediction of Cardiac Arrest Patients Using Deep Cardiac Learning Model

Cardiac monitoring is non-invasive, convenient monitoring to check heart function. As healthcare has become more preventative and proactive, early diagnosis of heart disease has increased the chances of better treatment and recovery. Cardiac monitoring is a test that continuously records the heart's electrical activity for 24 hours or more. It is also sometimes called ambulatory electrocardiography. Such tests help analyze mechanisms that protect patients from heart disease. This paper proposes an intelligent heart rate monitoring model for the survivability prediction of cardiac arrest patients based on a deep cardiac learning algorithm. In this method, sensors implanted in the patient's body calculate the random change in heart rate. A deep cardiac learning algorithm analyzes these calculations, and this method is elegantly designed to calculate survivability prediction data. The proposed model achieved 91.9% of heart rhythm management, 88.95% of heart rate management, 93.96% of cardiac arrest detection, 90.98% of abnormalities management and 89.77
^%
of supply monitoring.