Imagen de Portada

Atrial Fibrillation Detection from Ambulatory ECG with Accelerometry Contextualisation: A Semi-Supervised Learning Approach

Guardado en:
Publicado en:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 1-7
Autor principal: Voinas, Alex E
Otros Autores: Kumar, Devender, Smeddinck, Jan, Stochholm, Andreas, Sadasivan Puthusserypady
Publicado:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Materias:
Living conditions
Datasets
Electrocardiography
Fibrillation
Semi-supervised learning
Cardiac arrhythmia
Deep learning
Machine learning
Accelerometers
Environmental
Acceso en línea:Citation/Abstract
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Conference Title: 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Conference Start Date: 2025 July 14Conference End Date: 2025 July 18Conference Location: Copenhagen, DenmarkAtrial fibrillation (AF) is a common cardiac arrhythmia causing severe complications if left untreated. Due to its sporadic nature, early detection often requires longitudinal ambulatory electrocardiogram (ECG) screening. Recently, deep learning (DL) has gained prominence in analysing long-term ECG and automating AF detection. However, like any medical classification problem, obtaining diverse labelled ECG data for DL model training is expensive and time-consuming. This paper proposes a semi-supervised learning (SSL) based AF detection model employing a variational auto-encoder (VAE). It leverages varying amounts of labelled and unlabelled ECG data to optimise the AF detection performance on ambulatory ECG. As ambulatory contexts under free-living conditions influence ECG recordings, we incorporate context via accelerometry data and experiment with its influence on model performance. The proposed SSL model was trained on ECG data from 72,003 unique patients and can classify between sinus rhythms, AF, and other arrhythmias. Experimental results on unseen test dataset and the publicly available CACHET-CADB dataset clearly demonstrate the model’s generalisability, achieving an accuracy of over 91% with just 20% of the training set being labelled. With extensive experiments, our study exhibits the ability of SSL to improve AF detection from ambulatory ECG using small amounts of labelled data.
DOI:10.1109/EMBC58623.2025.11251627
Fuente:Science Database