| Title |
Machine learning approaches in atrial fibrillation detection / |
| Authors |
Butkus, Deividas ; Bernatavičienė, Jolita |
| DOI |
10.15388/LMITT.2024.2 |
| Full Text |
|
| Is Part of |
Lietuvos magistrantų informatikos ir IT tyrimai: konferencijos darbai, 2024 m. gegužės 10 d... Vilnius : Vilniaus universiteto leidykla. 2024, p. 16-24.. eISSN 2783-784X |
| Keywords [eng] |
atrial fibrillation ; machine learning ; ECG ; artificial intelligence ; deep learning |
| Abstract [eng] |
Atrial fibrillation (AF) characterized by rapid and irregular electrical activity in the atria represents a prevalent form of cardiac arrhythmia that significantly challenges healthcare systems due to its links to heightened mortality and morbidity rates. Early detection of AF is critical for accurate and effective management and treatment. In response to this pressing need, numerous researchers have used machine learning (ML) to enhance the precision and efficiency of AF detection. By analyzing available datasets, signal lengths, preprocessing techniques, and a diverse array of ML approaches, this paper aims to cover methodologies of AF detection using electrocardiogram (ECG) data and ML. |
| Published |
Vilnius : Vilniaus universiteto leidykla |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
