Portable ECG Device Detecting Abnormalities Using Deep Learning

This paper presents a portable ECG device combined with a deep learning model to provide ECG analysis in real-time to predict cardiac abnormalities. ECG signals are captured using a compact hardware setup and processed using an advanced, sophisticated neural network model which provides meaningful and timely detection of the possibility of any heart conditions. Deep learning models were trained and optimized to differentiate various forms of heart abnormalities using real-time data from the portable ECG device. Being portable and real-time, the device is meant for dual use by health professionals and common men. The portable nature of the device means that it allows continuous heart monitoring outside the clinical setup, thus providing an opportunity for earlier detection and timely intervention. This paper talks about the hardware setup, data acquisition, model architecture, and performance evaluation of the system. Initial tests show the potential of the device for the high-sensitivity detection of heart illnesses, including arrhythmias, meaning a real shift in a personal health scale tailored to remote patient monitoring. Future work will delve into other improvements that will augment diagnostic accuracy and widen the range of cardiovascular health monitoring coverage.