Key result
A residual convolutional neural network using noncontact ballistocardiogram signals detected atrial fibrillation with 93.7% sensitivity, 98.4% specificity, and 96.8% accuracy.
Why the study?
AF is the most common arrhythmia and can seriously threaten patient health, making AF detection of important clinical significance.
Does a residual CNN using noncontact BCG signals accurately detect atrial fibrillation in inpatients?
Does a residual CNN using noncontact BCG signals accurately detect atrial fibrillation in inpatients?
A residual CNN applied to noncontact BCG signals demonstrates high accuracy and sensitivity for detecting atrial fibrillation, suggesting potential for long-term home screening.
BCG-based AF detection may enable noncontact overnight monitoring; hypothesis-generating and requires prospective validation before clinical adoption.
Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition.
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Su et al. (2022) studied Atrial fibrillation (n=45). Residual convolutional neural network (CNN) based on ballistocardiogram (BCG) signals was evaluated on Atrial fibrillation detection performance (precision, sensitivity, specificity, F1 score, accuracy). A residual convolutional neural network using noncontact ballistocardiogram signals detected atrial fibrillation with 93.7% sensitivity, 98.4% specificity, and 96.8% accuracy.
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