Robust arterial blood pressure onset detection method from signal artifacts

Seung Bo Lee; Eun Suk Song; Hakseung Kim; Dong Ju Kim

doi:10.1109/IWW-BCI.2018.8311518

Robust arterial blood pressure onset detection method from signal artifacts

Seung Bo Lee, Eun Suk Song, Hakseung Kim, Dong Ju Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Arterial blood pressure (ABP) is used in various areas such as brain computer interface and clinical field. The morphological analysis of the ABP signal allows researchers to identify important information such as cardiovascular system and psychopathology. Detection of onset, which is the most important landmark in the ABP waveform, is essential for morphology analysis of ABP. Since the physiological signal is vulnerable to the risk of contamination, the robust onset detection method is needed. This study proposed a pulse onset detection method based on Monte Carlo approach that is robust from artifacts. The 10 cases of ABP signals were analyzed to detect signal onset. When we assessed the time difference from the actual onset, there was an average error of 2.4μs. The results suggested that the proposed method could achieve robustness in pulse detection and facilitated pulse wave analysis using clinical recordings with various artifacts.

Original language	English
Title of host publication	2018 6th International Conference on Brain-Computer Interface, BCI 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-3
Number of pages	3
ISBN (Electronic)	9781538625743
DOIs	https://doi.org/10.1109/IWW-BCI.2018.8311518
Publication status	Published - 2018 Mar 9
Event	6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17

Publication series

Name	2018 6th International Conference on Brain-Computer Interface, BCI 2018
Volume	2018-January

Other

Other	6th International Conference on Brain-Computer Interface, BCI 2018
Country/Territory	Korea, Republic of
City	GangWon
Period	18/1/15 → 18/1/17

Bibliographical note

Funding Information:
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00464) supervised by the IITP (Institute for Information & communications Technology Promotion), and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C1790).

Publisher Copyright:
© 2018 IEEE.

Keywords

arterial blood pressure
artifact
component
onset detection
systolic peak

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Behavioral Neuroscience

Access to Document

10.1109/IWW-BCI.2018.8311518

Cite this

Lee, S. B., Song, E. S., Kim, H., & Kim, D. J. (2018). Robust arterial blood pressure onset detection method from signal artifacts. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (pp. 1-3). (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311518

Robust arterial blood pressure onset detection method from signal artifacts. / Lee, Seung Bo; Song, Eun Suk; Kim, Hakseung et al.
2018 6th International Conference on Brain-Computer Interface, BCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-3 (2018 6th International Conference on Brain-Computer Interface, BCI 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, SB, Song, ES, Kim, H & Kim, DJ 2018, Robust arterial blood pressure onset detection method from signal artifacts. in 2018 6th International Conference on Brain-Computer Interface, BCI 2018. 2018 6th International Conference on Brain-Computer Interface, BCI 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-3, 6th International Conference on Brain-Computer Interface, BCI 2018, GangWon, Korea, Republic of, 18/1/15. https://doi.org/10.1109/IWW-BCI.2018.8311518

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title = "Robust arterial blood pressure onset detection method from signal artifacts",

abstract = "Arterial blood pressure (ABP) is used in various areas such as brain computer interface and clinical field. The morphological analysis of the ABP signal allows researchers to identify important information such as cardiovascular system and psychopathology. Detection of onset, which is the most important landmark in the ABP waveform, is essential for morphology analysis of ABP. Since the physiological signal is vulnerable to the risk of contamination, the robust onset detection method is needed. This study proposed a pulse onset detection method based on Monte Carlo approach that is robust from artifacts. The 10 cases of ABP signals were analyzed to detect signal onset. When we assessed the time difference from the actual onset, there was an average error of 2.4μs. The results suggested that the proposed method could achieve robustness in pulse detection and facilitated pulse wave analysis using clinical recordings with various artifacts.",

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note = "Funding Information: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00464) supervised by the IITP (Institute for Information & communications Technology Promotion), and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI17C1790). Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th International Conference on Brain-Computer Interface, BCI 2018 ; Conference date: 15-01-2018 Through 17-01-2018",

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N2 - Arterial blood pressure (ABP) is used in various areas such as brain computer interface and clinical field. The morphological analysis of the ABP signal allows researchers to identify important information such as cardiovascular system and psychopathology. Detection of onset, which is the most important landmark in the ABP waveform, is essential for morphology analysis of ABP. Since the physiological signal is vulnerable to the risk of contamination, the robust onset detection method is needed. This study proposed a pulse onset detection method based on Monte Carlo approach that is robust from artifacts. The 10 cases of ABP signals were analyzed to detect signal onset. When we assessed the time difference from the actual onset, there was an average error of 2.4μs. The results suggested that the proposed method could achieve robustness in pulse detection and facilitated pulse wave analysis using clinical recordings with various artifacts.

AB - Arterial blood pressure (ABP) is used in various areas such as brain computer interface and clinical field. The morphological analysis of the ABP signal allows researchers to identify important information such as cardiovascular system and psychopathology. Detection of onset, which is the most important landmark in the ABP waveform, is essential for morphology analysis of ABP. Since the physiological signal is vulnerable to the risk of contamination, the robust onset detection method is needed. This study proposed a pulse onset detection method based on Monte Carlo approach that is robust from artifacts. The 10 cases of ABP signals were analyzed to detect signal onset. When we assessed the time difference from the actual onset, there was an average error of 2.4μs. The results suggested that the proposed method could achieve robustness in pulse detection and facilitated pulse wave analysis using clinical recordings with various artifacts.

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Robust arterial blood pressure onset detection method from signal artifacts

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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