Recent advancements in wearable technology have enabled continuous health monitoring, significantly expanding the capabilities of devices like the Apple Watch, Fitbit, and Samsung Watch in medical diagnostics. Among these, Electrocardiography (ECG) interpretation is a critical function, traditionally requiring expert analysis. This project proposes the use of deep learning (DL) algorithms to automate ECG interpretation on these devices, enhancing diagnostic accuracy and accessibility. The integration of DL could revolutionize how cardiac abnormalities are detected in everyday settings, potentially reducing emergency response times, and improving preventative healthcare.

A preliminary review of existing studies highlights the application of algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in ECG interpretation. Recent research demonstrates promising results in accuracy and speed using deep learning frameworks. However, there is a gap in research specifically targeting accurate interpretation on low-power wearable devices. This project will explore scalable and efficient DL architectures that operate within these constraints.