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.
|Title of host publication||2018 6th International Conference on Brain-Computer Interface, BCI 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||3|
|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
|Name||2018 6th International Conference on Brain-Computer Interface, BCI 2018|
|Other||6th International Conference on Brain-Computer Interface, BCI 2018|
|Country/Territory||Korea, Republic of|
|Period||18/1/15 → 18/1/17|
Bibliographical noteFunding 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).
© 2018 IEEE.
- arterial blood pressure
- onset detection
- systolic peak
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Behavioral Neuroscience