Predicting the Resuscitation Cardiac Rhythms by Ruminative Analysis

Electrocardiogram (ECG) is widely used as a diagnostic tool to analyse the type of dysrhythmia mainly focused on ventricular fibrillation in resuscitation cardiac rhythms. Ventricular Fibrillation is a hasty, lethal heart rhythm where the heart shudders rather than pumping due to uncoordinated activities of the heart. Real time techniques are employed for the detection of QRS-T wave property from ECG to excerpt the required plots. The techniques are precise and the databases are recorded manually based on time and amplitude. Those data sets were used to assess the performance of the classifier. Naive Bayes classifier is used to predict ventricular fibrillation based on probabilities. This paper explains the method to predict the ventricular fibrillation using a Naive Bayes classifier before its onset. The proposed paper is implemented and simulated using Python software and also produce an output of accuracy 96.67%.