ECG Real-time Monitoring and Heart Anomaly Detection Reimagined

The electrocardiogram (ECG) test is developed to monitor the functionality of the cardiovascular system. Nowadays, numerous attentions have been given to the accurate and early detection of heartbeat anomalies in real-time to prevent complications and take necessary measures. This paper proposes a robust real-time binary classification for ECG signals to detect possible anomalies. We implement an initial detection phase right where ECG data is collected through lightweight deep learning analysis. We evaluate the system on two widely used datasets, PTB and MIT-BIH datasets from PhysioNet. Our experiments suggest using artificial neural network (ANN) algorithms for their superior performance over other machine learning algorithms with an accuracy up to 99.3%. Furthermore, we implemented our system on a Raspberry Pi B+ representing an ECG patch to collect and process ECG signals and detect any abnormalities using the proposed ANN model. To create a scalable system, we stream the data in real-time using Apache Kafka and MQTT to keep records of patients’ ECG data and use it for further analysis to identify causes and support medical diagnosis. The system notifies healthcare providers when abnormalities are detected.