Key result
A semi-supervised learning model incorporating accelerometry context achieved over 91% accuracy in detecting atrial fibrillation from ambulatory ECGs using only 20% labelled training data.
Why the study?
Obtaining diverse labelled ambulatory ECG data to train deep learning models for automating atrial fibrillation detection is expensive and time-consuming.
Does a semi-supervised learning model incorporating accelerometry context improve atrial fibrillation detection from ambulatory ECGs?
Does a semi-supervised learning model incorporating accelerometry context improve atrial fibrillation detection from ambulatory ECGs?
A semi-supervised learning model incorporating accelerometry context can accurately detect atrial fibrillation from ambulatory ECGs using only a small amount of labelled data.
Reduces labeled data needs for DL AF detection; leaves open clinical validation before adoption.
Atrial 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 classification 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. Our 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 our 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.
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Voinas et al. (2023) studied Atrial fibrillation (n=72,003). Semi-supervised learning (SSL) based AF classification model with accelerometry context was evaluated on Classification accuracy between sinus rhythms, AF, and other arrhythmias. A semi-supervised learning model incorporating accelerometry context achieved over 91% accuracy in detecting atrial fibrillation from ambulatory ECGs using only 20% labelled training data.
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