Tan Lyu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Tan Lyu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Biyong Zhang
Eindhoven University of Technology, Eindhoven 5612AP, The Netherlands
1 shared publication - Peilin Lu
Department of Neurology, Sir Run Run Shaw Hospital, ZheJiang University School of Medicine, Hangzhou, China
1 shared publication - Qiushi Su
Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China
1 shared publication - Xiaomei Wu
College of Biomedical Engineering, Fudan University, Shanghai 200433, China
1 shared publication - Yanqi Huang
Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China
1 shared publication
Latest Publications
- Open Access
- 19 September 2022
- Published by MDPI AG in Electronics
- Vol. 11 (18) , 2974
Atrial Fibrillation Detection Based on a Residual CNN Using BCG Signals
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.
- 19 September 2022
- Published by MDPI AG in Electronics
- Vol. 11 (18) , 2974
Open AccessAtrial Fibrillation Detection Based on a Residual CNN Using BCG Signals
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.