Neural signals-based respiratory motion tracking: a proof-of-concept study Article Swipe
Xiangbin Zhang
,
Wenjie Liu
,
Feng Xu
,
Weizhong He
,
Yingpeng Song
,
Guangjun Li
,
Yingjie Zhang
,
Guyu Dai
,
Qing Xiao
,
Qianqian Meng
,
Xianhu Zeng
,
Sen Bai
,
Renming Zhong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acf819
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acf819
Objective. Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking. Approach. The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion. Main results. Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87. Significance. Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/acf819
- OA Status
- hybrid
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386532318
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386532318Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/acf819Digital Object Identifier
- Title
-
Neural signals-based respiratory motion tracking: a proof-of-concept studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-08Full publication date if available
- Authors
-
Xiangbin Zhang, Wenjie Liu, Feng Xu, Weizhong He, Yingpeng Song, Guangjun Li, Yingjie Zhang, Guyu Dai, Qing Xiao, Qianqian Meng, Xianhu Zeng, Sen Bai, Renming ZhongList of authors in order
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https://doi.org/10.1088/1361-6560/acf819Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1361-6560/acf819Direct OA link when available
- Concepts
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Electroencephalography, Respiratory system, Respiratory monitoring, Computer science, Artificial intelligence, Medicine, Internal medicine, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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