Automatic Cardiac Feature Point Annotation of Cardio-mechanical Signals from Valvular Heart Disease Patients Using Multi-modal Sensor Fusion | Proceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices (2025)

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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Seth S Martin, Aaron W Aday, Zaid I Almarzooq, Cheryl AM Anderson, Pankaj Arora, Christy L Avery, Carissa M Baker-Smith, Bethany Barone Gibbs, Andrea Z Beaton, Amelia K Boehme, et al. 2024. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation 149, 19 (2024), e1164–e1164.

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Amirtahà Taebi, Brian E. Solar, Andrew J. Bomar, Richard H. Sandler, and Hansen A. Mansy. 2019. Recent Advances in Seismocardiography. Vibration 2, 1 (2019), 64–86.

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Chenxi Yang, Nicole D. Aranoff, Philip Green, and Negar Tavassolian. 2020. Classification of Aortic Stenosis Using Time–Frequency Features From Chest Cardio-Mechanical Signals. IEEE Transactions on Biomedical Engineering 67, 6(2020), 1672–1683.

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Alexandre Laurin, Farzad Khosrow, Andrew Blaber, and Kouhyar Tavakolian. 2015. Accurate and consistent automatic seismocardiogram annotation without concurrent ECG. In 2015 Computing in Cardiology Conference (CinC). 25–28.

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Hiren Kumar Thakkar and Prasan Kumar Sahoo. 2020. Towards Automatic and Fast Annotation of Seismocardiogram Signals Using Machine Learning. IEEE Sensors Journal 20, 5 (2020), 2578–2589.

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Farzad Khosrow-khavar, Kouhyar Tavakolian, Andrew P. Blaber, John M. Zanetti, Reza Fazel-Rezai, and Carlo Menon. 2015. Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations. IEEE Journal of Biomedical and Health Informatics 19, 4 (2015), 1428–1434.

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Farzad Khosrow-khavar, Kouhyar Tavakolian, and Carlo Menon. 2015. Moving toward automatic and standalone delineation of seismocardiogram signal. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 7163–7166.

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Zeynep Melike Isilay Zeybek, Vittorio Racca, Antonio Pezzano, Monica Tavanelli, and Marco Di Rienzo. 2022. Can Seismocardiogram Fiducial Points Be Used for the Routine Estimation of Cardiac Time Intervals in Cardiac Patients? FRONTIERS IN PHYSIOLOGY 13 (MAR18 2022).

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Chenxi Yang, Foli Fan, Nicole Aranoff, Philip Green, Yuwen Li, Chengyu Liu, and Negar Tavassolian. 2021. An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases. FRONTIERS IN PHYSIOLOGY 12 (SEP 30 2021).

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Xingyao Wang, Yuwen Li, Hongxiang Gao, Xianghong Cheng, Jianqing Li, and Chengyu Liu. 2023. A Causal Intervention Scheme for Semantic Segmentation of Quasi-Periodic Cardiovascular Signals. IEEE Journal of Biomedical and Health Informatics 27, 7 (2023), 3175–3186.

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Index Terms

  1. Automatic Cardiac Feature Point Annotation of Cardio-mechanical Signals from Valvular Heart Disease Patients Using Multi-modal Sensor Fusion

    1. Computing methodologies

      1. Machine learning

        1. Machine learning approaches

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    Automatic Cardiac Feature Point Annotation of Cardio-mechanical Signals from Valvular Heart Disease Patients Using Multi-modal Sensor Fusion | Proceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices (1)

    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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    Publication History

    Published: 11 December 2024

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    Author Tags

    1. Cardiac interval segmentation
    2. Cardio-mechanical signals
    3. Multi-modal signals
    4. Self-supervised learning
    5. Valvular heart diseases

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