Key result
MSCMN achieves state-of-the-art atrial fibrillation detection from single-lead ECGs with an 85.4% F1 score.
Why the study?
Atrial fibrillation detection from single-lead ECG remains challenging because signals are weak, frequently disturbed by nonstationary noise, show variable morphology, and lack spatial correlation information.
Does the MSCMN model improve the detection of atrial fibrillation from single-lead ECGs compared to existing algorithms?
Does the MSCMN model improve the detection of atrial fibrillation from single-lead ECGs compared to existing algorithms?
Absolute Event Rate: 85.4% vs 84.5%
The proposed MSCMN model effectively detects atrial fibrillation from single-lead ECGs, outperforming existing algorithms by utilizing multi-scale convolutional modulation and early fusion of R-peak position encoding.
Should not yet change AF screening practice; leaves open prospective validation of MSCMN.
The development of wearable sensor technology has made the acquisition of continuous single-lead electrocardiogram (ECG) data more convenient and accessible. Atrial fibrillation (AF) detection in a single-lead ECG wave is becoming a popular research topic. However, it is challenging due to the following issues: 1) the AF signal is weak and frequently disturbed by nonstationary noises; 2) both AF and non-AF rhythms have various morphologies; and 3) a single-lead ECG lacks spatial correlation information on the electrical movement of the heart. To solve these issues, we propose a multi-scale convolutional modulation network (MSCMN) composed of stacked modulators and converters. The modulator achieves an efficient self-attention mechanism that adaptively suppresses the noises by computing the Hadamard product between the multi-scale nonlinear convolutional features and the linear channel mixing features. The converter, which consists of two partial convolutional layers and one linear layer, is used to capture ECG temporal dependency and cross-channel correlation among the modulated features. Furthermore, because AF rhythms exhibit irregular R-R intervals, we employ an early fusion of R-peak position encoding and ECG waveforms to enhance feature extraction. We conducted extensive experiments on three public datasets. The results consistently validate the effectiveness of the proposed MSCMN in detecting AF from a single-lead ECG.
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Qiu et al. (2024) studied Atrial fibrillation (n=25,981). Multi-scale convolutional modulation network (MSCMN) vs. Existing deep learning methods (e.g., MCNN-BLSTM) was evaluated on F1 Macro Average score on DB1 (PhysioNet/CinC 2017). The proposed Multi-scale convolutional modulation network (MSCMN) achieved state-of-the-art performance for atrial fibrillation detection from single-lead ECGs, with an F1 MacroAvg of 85.4% on the PhysioNet/CinC 2017 dataset.
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