Version 1
: Received: 6 March 2024 / Approved: 7 March 2024 / Online: 8 March 2024 (01:52:57 CET)
How to cite:
Kim, K. S.; Kim, T.; Cha, J. Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction. Preprints2024, 2024030386. https://doi.org/10.20944/preprints202403.0386.v1
Kim, K. S.; Kim, T.; Cha, J. Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction. Preprints 2024, 2024030386. https://doi.org/10.20944/preprints202403.0386.v1
Kim, K. S.; Kim, T.; Cha, J. Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction. Preprints2024, 2024030386. https://doi.org/10.20944/preprints202403.0386.v1
APA Style
Kim, K. S., Kim, T., & Cha, J. (2024). Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction. Preprints. https://doi.org/10.20944/preprints202403.0386.v1
Chicago/Turabian Style
Kim, K. S., Taehoon Kim and Jihyun Cha. 2024 "Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction" Preprints. https://doi.org/10.20944/preprints202403.0386.v1
Abstract
In this study, we propose the Signal Integration Reconstruction Peak Detection (SIRPD) algorithm for reliable Cardiac Beat Interval (CBI) measurement using functional Near-Infrared Spectroscopy (fNIRS) signals. This algorithm enhances reliability by identifying a regular waveform in cerebral blood flow associated with heartbeats. The SIRPD algorithm assesses the Signal Quality Index (SQI) per channel, discards signals from unavailable channels, and interpolates oxygenated and total hemoglobin concentration signals, thereby boosting resolution and integrating the signal into a single channel. The integrated and smoothed signal is transformed to identify the most significant periodic waveform, from which the CBI is extracted. Validated with data from 20 healthy subjects who simultaneously recorded fNIRS and ECG signals, our findings confirm the equivalence of the CBI derived from ECG and calculated through the SIRPD algorithm from fNIRS (R^2 = .98, RMSE = .011, ICC = .991). We also extracted heart rate variability (HRV) features, confirming no significant difference in all features between ECG and fNIRS devices. Uniquely, this study regards the fNIRS heart signal not as an artifact source but as a valuable bio signal. The proposed method, which allows for the detection of cardiac beat intervals from cerebral blood flow oscillations, suggests the potential clinical applicability of fNIRS as an auxiliary indicator for various cardiovascular factors.
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
