Real-time Cardiac Arrhythmia Detection Using Machine Learning and Wearable Devices

Abstract

Cardiac arrhythmia, a condition characterized by sporadic heartbeats, postures critical wellbeing dangers in the event that not identified instantly. Conventional checking strategies, such as Holter screens, are regularly awkward and restricted in their capacity to supply ceaseless, real-time observing. This think about presents a novel approach for real-time cardiac arrhythmia location utilizing machine learning calculations coordinates with wearable gadgets. The proposed framework leverages progressed machine learning procedures to analyse electrocardiogram (ECG) information collected from wearable gadgets, empowering persistent observing and opportune discovery of arrhythmias. The wearable gadgets are prepared with sensors that capture high-resolution ECG signals, which are at that point transmitted to a cloud-based stage for investigation. We utilize a combination of profound learning and conventional machine learning calculations, counting Convolutional Neural network (CNNs) and Support Vector Machines (SVMs), to classify diverse sorts of arrhythmias. The models are prepared on a comprehensive dataset of commented on ECG recordings, guaranteeing tall precision and vigor. To approve the adequacy of the proposed framework, broad tests were conducted utilizing real-world ECG information. The comes about illustrate that our approach accomplishes prevalent location precision compared to conventional methods, with the included advantage of real-time handling capabilities. Furthermore, the integration with wearable gadgets upgrades client consolation and comfort, advancing broad appropriation for ceaseless cardiac observing.