Cardiac Arrhythmia Detection and Prediction Using Deep Learning Technique

One of the fatal diseases in the world is heart disease. Every year, millions of people die of cardiovascular diseases. However, one can decrease the mortality rates if the heart disease was detected and treated early. Usually, people do an electrocardiogram (ECG) test to know about the well-being of their heart. Some kind of irregular functioning and illness in the heart can be found in an ECG test. When the heart malfunctions or if there is any improper beating of the heart, then it results in arrhythmia. There are several types of arrhythmia and some of them are fatal. The process to identify the correct type of arrhythmia is quite difficult and effort-taking process. Even the small changes in the ECG relate to another kind of arrhythmia. It takes experience and patience to recognize the type of arrhythmia accurately. Therefore, deep learning techniques should be employed to analyze the test. Machine learning that involves many levels of processing is known as deep learning. From computer vision to natural language processing, there’s a lot to learn. It has been used in various applications. This method is receiving more popularity because of extreme accuracy, provided the numerous amount of data. The interesting feature is that it analyses the examples and distinguishes the classes and levels automatically. This study is regarding arrhythmia prediction in ECG and the attention it deserves in deep learning community. Providing CNN model, we are going to elaborate the process of detecting cardiac arrhythmia using ECG dataset in this study. The model is executed by rendering CNN with cardiac arrhythmia recognition database. Purpose: About one-third of the world’s population is affected by arrhythmia. Hence, the development of new and successful methodologies is highly in demand in the field of arrhythmia prediction. Further, the need of a cost-effective wearable monitoring gadget to identify the condition of arrhythmia is highly recommended. It assures the trouble-free environment for those who are affected. Observations: Various research papers that were written bases on arrhythmia prediction using machine learning techniques. Additionally, there are also new advancements all over Internet regarding deep learning-based strategies. These strategies can bring an immense change in cardiac arrhythmia prediction.