Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than -16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

Functional Near Infrared Spectroscopy (fNIRS) enables researchers to conduct studies in situations where use of other functional imaging methods is impossible. An important shortcoming of fNIRS is the sensitivity to motion artifacts. We propose a new wavelet based algorithm for removing movement artifacts from fNIRS signals. We tested the method on simulated and experimental fNIRS data. The results show an average of 18.97 dB and 15.54 dB attenuation in motion artifacts power for our two test subjects without introducing significant distortion in the artifact-free regions of the signal.

In this study, we proposed a novel method for extracting the instantaneous respiratory rate (IRR) from the pulse oximeter photoplethysmogram (PPG). The method was performed in three main steps: (1) a time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced intensity, amplitude and frequency variation signals from PPG, (2) the second SST was applied to each extracted respiratory-induced variation signal to estimate the corresponding IRR, and (3) the proposed peak-conditioned fusion method then combined the IRR estimates to calculate the final IRR. We validated the implemented method with capnography and nasal/oral airflow as the reference RR using the limits of agreement (LOA) approach. Compared to simple fusion and single respiratory-induced variation estimations, peak-conditioned fusion shows better performance. It provided a bias of 0.28 bpm with the 95% LOAs ranging from −3.62 to 4.17, validated against capnography and a bias of 0.04 bpm with the 95% LOAs ranging from −5.74 to 5.82, validated against nasal/oral airflow. This algorithm would expand the functionality of a conventional pulse oximetry beyond the measurement of heart rate and oxygen saturation to measure the respiratory rate continuously and instantly.

We have evaluated the use of phase synchronization to identify resting state functional connectivity (RSFC) in the language system in infants using functional near infrared spectroscopy (fNIRS). We used joint probability distribution of phase between fNIRS channels with a seed channel in the language area to estimate phase relations and to identify the language system network. Our results indicate the feasibility of this method in identifying the language system. The connectivity maps are consistent with anatomical cortical connections and are also comparable to those obtained from functional magnetic resonance imaging (fMRI) functional connectivity studies. The results also indicate left hemisphere lateralization of the language network.

Lack of bladder fullness sensation is an issue that arises in different neurogenic conditions and in addition to influencing patients' quality of life, can result in serious kidney damage. We describe a wireless wearable sensor for detecting bladder fullness using near infrared spectroscopy (NIRS). The sensor has been tested in vitro and in vivo to verify its feasibility and is shown to be capable of detecting changes in bladder content noninvasively.