ECG Monitoring System Using Deep Learning

ECG Monitoring System Using Deep Learning

Aditi Rawal, Aditya Sharma, Prajoti Rane, Sandesh Toshniwal, Prof. Mohan Kumar

1,2,3,4 Department of Electronics and Telecommunication Engineering

5 Professor, Department of Electronics and Telecommunication Engineering

Atharva College Of Engineering, Malad (West), Mumbai, India

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Abstract - 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.

Key Words:   ECG, Oximeter, Deep Learning, CNN, Peaks and troughs, Classification, smart monitoring system, cloud, sensors