research-article
Authors: Siyu Wei, Xingyao Wang, Le Geng, Yiqing Yao, + 5, Yongfeng Shao, Qun Wei, + 3, Jianqing Li, Chengyu Liu, Chenxi Yang (Less)
SHWID '24: Proceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices
Pages 63 - 67
Published: 11 December 2024 Publication History
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Abstract
The study proposes an innovative ECG-free algorithm for the automatic annotation of cardiac feature points in seismo-cardiogram (SCG) signals from valvular heart disease (VHD) patients. The algorithm is based on multi-modality signals, obviating the need for manual SCG annotation. It consists of self-supervised learning and pseudo-label generation from ECG signals. Initially, the algorithm employs self-supervised learning to analyze the pseudo-periodic nature of SCG signals. Subsequently, pseudo-labels are established using synchronized ECG signals for model training. Finally, labeling tests are conducted with SCG signals alone. The training and testing datasets consist of samples from 100 VHD patients. Experiments demonstrate that the proposed algorithm achieves a precision of 98.94% and a recall of 97.44% on the test set, suggesting the feasibility of the proposed method. The presented framework would be essential for further fiducial point detection and analysis of cardio-mechanical signals, which might provide out-of-clinic evaluations of VHD in the future.
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Index Terms
Automatic Cardiac Feature Point Annotation of Cardio-mechanical Signals from Valvular Heart Disease Patients Using Multi-modal Sensor Fusion
Computing methodologies
Machine learning
Machine learning approaches
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SHWID '24: Proceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices
October 2024
317 pages
ISBN:9798400709746
DOI:10.1145/3703847
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 December 2024
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Author Tags
- Cardiac interval segmentation
- Cardio-mechanical signals
- Multi-modal signals
- Self-supervised learning
- Valvular heart diseases
Qualifiers
- Research-article
Conference
SHWID 2024
SHWID 2024: 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices
October 18 - 20, 2024
Guangdong, Guangzhou, China
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