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Real Time Arrhythmia Detecting Wearable using a Novel Deep Learning Model

Cardiac arrhythmia is by-far the biggest common cardiovascular illness. It makes the heart beat with an irregular rhythm; either too slowly or too fast. Atrial fibrillation is the most well-known form of arrhythmia. It arises when the upper chambers of the heart – atria (singular: atrium) - experiences chaotic electrical signals and this results in rapid heart rate. Though initially, the disease may not have any symptoms - if left untreated it may be fatal. Electrocardiography (ECG) is the most common test to find out if the patient is suffering from atrial fibrillation. Thus, ECG analysis helps to diagnose cardiac arrhythmia. In this work, we aim at developing a complete wearable application that uses signal pre-processing combined with a Deep Learning Model consisting of 1-D Convolution Neural Networks and Long Short-term Memory Networks to classify single-lead ECG signals into different categories. The model classifies between sinus rhythm, atrial fibrillation, noisy ECG signal and other forms with a high percentage of precision and accuracy and then displays the result and notifies the user about their condition through a mobile application that is connected to a real-time database through the cloud. This method is fast and reliable as the device gives the user an early diagnosis of arrhythmia based on the received electric signals - upon which you can immediately undergo proper clinical procedures to avoid complications.