A heuristic methodology for ECG heartbeat categorization using Convolutional Neural Networks

This research paper presents the importance of cardiologists and their availability to the public by the usage of various smart devices. These new simplified ECGs may be as complex or sophisticated as those that represent the usage of the medical facility however they are accurately able to observe the heart rate to monitor their health. One of the most commonly overlooked problems in the modern world is Arrhythmia. This is a problem related to the rhythm of the heartbeat and it occurs due to the improper coordination between the electrical impulses with your heartbeat. Heartbeat occurs daily in the lives of several people as they aren’t properly focused upon the monitoring of heartbeat. This research paper attempts to focus on the issue of Heart Arrhythmia as well as the creation and classification model which is capable of identifying the type of heart arrhythmia by an individual how may be suffering from heart issues. To successfully create our model, this research work utilizes the convolution neural networks to train the proposed model with existing ECG data and properly identify the heart rate as well as the type of arrhythmia that they have so that it provides the ability to immediately provide the proper medical attention.