Machine learning assisted system for the resource-constrained atrial fibrillation detection from short single-lead ECG signals
Abdukalikova, Anara (2018)
School of Engineering Science, Tietotekniikka
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Electronic health or E-Health is spreading extensively nowadays. E-Health solutions help to achieve the sustainability goal of increasing the expected lifetime while improving the quality of life by providing a constant healthcare monitoring. The focus of this work is on studying the detection of one of the cardiovascular diseases – Atrial Fibrillation (AF) arrhythmia, which has a severe influence on the heart health conditions and could even increase the risk of death. Therefore, it is important to detect it as early as possible. In this thesis we focused on studying various machine learning techniques for AF detection using short single lead ECG recordings. A web-based solution was built as a final prototype, which first simulates the reception of a recorded signal, conducts the preprocessing, makes a prediction of the AF presence, and visualizes the result. For the AF detection the relatively high accuracy score was achieved comparable to the one of the state-of-the-art. The work was based on the investigation of the proposed architectures and the usage of the database of signals from the 2017 PhysioNet/CinC Challenge. However, an additional constraint was introduced to the original problem formulation, since the idea of a future deployment on the resource-limited devices places the restrictions on the complexity of the computations being performed for achieving the prediction. Therefore, this constraint was considered during the development phase of the project.