Experimental comparison of photoplethysmography-based atrial fibrillation detection using simple machine learning methods

Szymon Buś; Konrad Jędrzejewski; Tomasz Krauze; Przemyslaw Guzik

doi:10.1117/12.2580594

14 October 2020 Experimental comparison of photoplethysmography-based atrial fibrillation detection using simple machine learning methods

Szymon Buś, Konrad Jędrzejewski, Tomasz Krauze, Przemyslaw Guzik

Author Affiliations +

Proceedings Volume 11581, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2020; 115811A (2020) https://doi.org/10.1117/12.2580594
Event: Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2020, 2020, Wilga, Poland

Abstract

The results of experimental studies on application of selected simple machine learning (ML) methods for detection of atrial fibrillation (AFib) based on photoplethysmogram (PPG) are presented in the paper. The goal of the studies was to compare the performance of AFib detection using different ML algorithms in short PPG segments containing 32 consecutive cardiac cycles. Four parameters describing time series of interbeat intervals (IBI) were derived from the time domain Heart Rate Variability (HRV) and used as features for classification algorithms. Optimal values of metaparameters for all considered ML algorithms were experimentally determined. Accuracy, sensitivity, specificity and F1-score were then calculated to measure the quality of detection performance of each classification algorithm.

Citation Download Citation

Szymon Buś, Konrad Jędrzejewski, Tomasz Krauze, and Przemyslaw Guzik "Experimental comparison of photoplethysmography-based atrial fibrillation detection using simple machine learning methods", Proc. SPIE 11581, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2020, 115811A (14 October 2020); https://doi.org/10.1117/12.2580594

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