Key result
A novel algorithm extracting heart and respiratory rates from fNIRS signals showed strong correlation with ECG (r > 0.92) and low mean absolute error (0.18 s) for respiration period estimation.
Why the study?
fNIRS signals commonly suffer from physiological artifacts, which nonetheless contain rich physiological information that could be leveraged.
Does a novel algorithm using cumulative curve fitting approximation accurately extract heart and respiratory rates from fNIRS signals compared to standard ECG and respiration sensors?
Does a novel algorithm using cumulative curve fitting approximation accurately extract heart and respiratory rates from fNIRS signals compared to standard ECG and respiration sensors?
Effect estimate: r > 0.92
p-value: p=>0.05
A novel algorithm using cumulative curve fitting approximation can accurately extract heart and respiratory rates from fNIRS signals, showing strong correlation with standard ECG and respiration sensors.
fNIRS may enable ECG-free HRV and respiration monitoring; hypothesis-generating and leaves open clinical validation before practice adoption.
Background: Functional near-infrared spectroscopy (fNIRS) is a valuable neuroimaging tool that captures cerebral hemodynamic during various brain tasks. However, fNIRS data usually suffer physiological artifacts. As a matter of fact, these physiological artifacts are rich in valuable physiological information. Methods: Leveraging this, our study presents a novel algorithm for extracting heart and respiratory rates (RRs) from fNIRS signals using a nonstationary, nonlinear filtering approach called cumulative curve fitting approximation. To enhance the accuracy of heart peak localization, a novel real-time method based on polynomial fitting was implemented, addressing the limitations of the 10 Hz temporal resolution in fNIRS. Simultaneous recordings of fNIRS, electrocardiogram (ECG), and respiration using a chest band strain gauge sensor were obtained from 15 subjects during a respiration task. Two-thirds of the subjects’ data were used for the training procedure, employing a 5-fold cross-validation approach, while the remaining subjects were completely unseen and reserved for final testing. Results: The results demonstrated a strong correlation ( r > 0.92, Bland–Altman Ratio <6%) between heart rate variability derived from fNIRS and ECG signals. Moreover, the low mean absolute error (0.18 s) in estimating the respiration period emphasizes the feasibility of the proposed method for RR estimation from fNIRS data. In addition, paired t -tests showed no significant difference between respiration rates estimated from the fNIRS-based measurements and those from the respiration sensor for each subject ( P > 0.05). Conclusion: This study highlights fNIRS as a powerful tool for noninvasive extraction of heart and RRs alongside brain signals. The findings pave the way for developing lightweight, cost-effective wearable devices that can simultaneously monitor hemodynamic, heart, and respiratory activity, enhancing comfort and portability for health monitoring applications.
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Adib et al. (2025) studied this question. fNIRS-based extraction of heart and respiratory rates using cumulative curve fitting approximation vs. ECG and chest band strain gauge sensor was evaluated on Heart rate variability and respiration rate estimation (r > 0.92, p=>0.05). A novel algorithm extracting heart and respiratory rates from fNIRS signals showed strong correlation with ECG (r > 0.92) and low mean absolute error (0.18 s) for respiration period estimation.
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