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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.

This manuscript adopted the cardiac modeling and simulation method to study the problems of physiological pacing in clinical application. A multiscale rabbit ventricular electrophysiological model was constructed. We simulated His-bundle pacing (HBP) treatment for left bundle branch block (LBBB) and atrioventricular block (AVB), and left bundle branch pacing (LBBP) treatment for LBBB by setting various moments of the stimulus. The synthetic ECGs and detailed electrical activities were analyzed. Our electrophysiological model accurately simulated the normal state, HBP, and LBBP. The synthetic ECG showed that QRS duration was narrowed by 30% after HBP correction for LBBB. For LBBB correction with LBBP, the synthetic ECGs of LBBP starting before 30 ms (if the end of atrial excitation is set as 0 ms) presented right bundle branch block (RBBB), and those of LBBP starting at 30–38 ms were synchronous, while those of LBBP starting after 42 ms possessed LBBB morphologies. The best pacing results were obtained when LBBP started at 34 ms. This manuscript verified the feasibility of the constructed ventricular model, and studied the physiological pacing mechanism. The results showed that HBP realized correction for AVB and high LBBB. The performance of LBBP can be improved by applying the stimulus within a specific period of time (0–8 ms) after atrial excitation.