ECG Monitoring System Using Deep Learning

This study suggests employing deep learning methods to create a portable ECG monitoring device. A common diagnostic technique in cardiology is the electrocardiogram (ECG), which offers vital information about the electrical activity of the heart. However, manual ECG signal analysis can be laborious and error-prone. Current developments in deep learning present fresh possibilities for automated ECG signal interpretation and analysis. Deep neural networks are used for categorization, feature extraction, and signal pre-processing in the proposed ECG monitoring system. To start, noise and artefacts are removed from the raw ECG signals through pre-processing. Using neural networks, which are intended to automatically learn pertinent features from the input signals, features are then extracted from the pre-processed data. The extracted features are then fed.