Predicting Arrhythmia

Abstract

One of the many arrhythmia management advices is that diagnosis will always play an important role, but it is not possible because a person may have suffered from an arrhythmia without being aware of it; hence this is somewhat broad in terms of cardiovascular misunderstandings. Currently, this diagnosis is not going to be very accurate or far to find, thus opening much space for improvement from a medical point of view. This is the entire platform that we proposed for automatic arrhythmia classification and prediction through applied techniques of deep learning for several electrocardiogram image databases. What we would also want to take advantage here is the recent deep learning methods for extracting extremely interesting features from ECG images, thus allowing a differentiation between different types of arrhythmias. The end product of this research study would be an easy-to-use mobile application, which will include real-time ECG analysis through trained deep learning models. The main users are people who will have access to an advanced application that would allow the taking of pictures through smartphone cameras and instantaneously analysis, thus enhancing accessibility to cardiac health care for all. This proposed system will achieve early detection, timely intervention, and reduced burden on the healthcare system. Incorporating advanced deep learning models and mobile technology into this project would prove to be a great achievement that could revolutionize cardiac diagnostics for patients and health providers alike.