A Machine Learning approach for Atrial Fibrillation detection in telemonitored patients

PEDRO BARRERA; LORENZA VECINO SCHANDY; SERGIO LIBERZCUK; CRISTIAN MATEOS DÍAZ; BONOMINI, MARÍA PAULA

Congreso; XXIV Congreso de Bioingeniería; 2023

Sociedad Argentina de Bioingeniería (SABI)

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. As it is typically asymptomatic, it oftengoes undiagnosed until major complications arise, such as stroke. Therefore, the development of rapid, economical, and widely accessible diagnostic tools for detecting AF at an early stage is crucial. Telemonitoring with machine learning-assisted devices shows promise in achieving this goal. This paper presents an algorithm that automatically detects AF in signals obtained by portable electrocardiographs connected to a telemonitoring platform via smartphones. The algorithm consists of two stages: a noise detection stage and an AF detection stage. The noise de- tection stage involves analyzing the ECG signals using 5-second windows with a 1-second shift. A support vector machine (SVM) classifier predicts the presence or absence of noise in each window, allowing for the detection of noisy and non-noisy segments of the signal. In the AF detection stage, the non-noisy segments are processed using a Pan-Tompkins algorithm to find the R peaks of the signal, and the corresponding RR interval series. This RR series is used to calculate five indices, which serve asinput features of a logistic regression classifier that predicts the presence or absence of AF in the ECG signal. The algorithmwas trained and tested using the Physionet Short Single-Lead AF Database (SSLAFDB) and achieved an accuracy of 87.9%and an F1-score of 87.7%. Further validation was performed by an external partner using two other databases, reporting anaccuracy of 93.1% and 89.5% on two different databases.