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This research addresses the critical domain of anomaly detection in real-time ECG signals, a pivotal aspect in healthcare monitoring. The study encompasses comprehensive data preprocessing, detailed analysis of ECG graphs, and the application of diverse machine learning models, including logistic regression, random forest, XGboost,LSTM.
Official and maintained implementation of the paper "Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in Medical Machine Learning" (ECG-DualNet) [Physiological Measurement 2022, EMBC 2023].
This repository includes all the scripts developed during the Final Degree Project aiming to provide new insights in risk stratification of Brugada Sindrome through AI-based approaches.