Solving variability: Accurately extracting feature components from ballistocardiograms Article Swipe
T. Yang
,
Haihang Yuan
,
Junqi Yang
,
Zhongchao Zhou
,
Masayuki Abe
,
Yoshitake Nakayama
,
Shao Ying Huang
,
Wenwei Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/20552076241277746
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/20552076241277746
Objective A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction. Methods Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent. Results In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively. Conclusion Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/20552076241277746
- OA Status
- gold
- Cited By
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- References
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- OpenAlex ID
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https://openalex.org/W4402336139Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/20552076241277746Digital Object Identifier
- Title
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Solving variability: Accurately extracting feature components from ballistocardiogramsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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T. Yang, Haihang Yuan, Junqi Yang, Zhongchao Zhou, Masayuki Abe, Yoshitake Nakayama, Shao Ying Huang, Wenwei YuList of authors in order
- Landing page
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https://doi.org/10.1177/20552076241277746Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1177/20552076241277746Direct OA link when available
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Feature (linguistics), Computer science, Pattern recognition (psychology), Artificial intelligence, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | Digital Health |
| primary_location.source.host_organization | https://openalex.org/P4310320017 |
| primary_location.source.host_organization_name | SAGE Publishing |
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| primary_location.source.host_organization_lineage_names | SAGE Publishing |
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| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
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| publication_year | 2024 |
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