KEYWORDS: Data modeling, Image segmentation, Fourier transforms, Deep learning, Sensors, Medical research, Machine learning, Functional near infrared spectroscopy, Digital signal processing
Functional near-infrared spectroscopy (fNIRS) presents an affordable and light-weight method to monitor the cerebral hemodynamics of the brain. However, noise and artefacts hamper the analysis of fNIRS signals. Thus, the signal quality assessment is a crucial step when planning fNIRS experiments. Currently no standardized method exists for the evaluation. Commonly used visual inspection of the signals is time consuming and prone to subjective bias. Recently use of machine learning and deep learning approaches have been applied for the fNIRS signal quality assessment, showing promising results. However, currently there are only a few experiments which have investigated the use of these approaches to evaluate fNIRS signal quality. In this human brain study, we utilized previously developed deep learning approach used for the assessment of PPG signal quality with short-time Fourier transform (STFT) to evaluate the quality of raw fNIRS signals with wavelengths 690 nm, 810 nm, 830 nm and 980 nm. The data was collected from 38 subjects with a two-channel fNIRS device, measured during breath hold protocol in sitting position. A total of 10,144 segments were extracted using a window of 10 seconds length without overlap and annotated for SQA by three independent evaluators. The segments were transformed with STFT, and further processed into 2D images. The images were used as input data for CNN deep learning network, and the output further used to classify the segments as acceptable or unacceptable. The results show high potential of using DL approach for fNIRS signal quality assessment with classification accuracy of 87.89 %.
KEYWORDS: Hemodynamics, Brain, Oxygenation, Near infrared spectroscopy, LED lighting, Light emitting diodes, Frequency response, Medicine, Light sources and illumination
Transcranial photobiomodulation (tPBM) has emerged as a promising economical point-of-care tool to enhance mitochondrial dynamics, mitigate neuroinflammation, improve sleep and cognitive functions in various CNS disorders. Its propensity to modulate cerebrovascular tone can potentially alter cerebral hemodynamics. We set out to investigate whether tPBM can influence the brain oxygenation as assessed by fNIRS in healthy subjects with a body positional challenge.
Obtaining parameters that characterize cerebral fluid interactions in the human brain is of high interest particularly as regards studies of the brain clearance and in relation to neurodegeneration diseases (NDD). Furthermore, disturbances in sleep affecting brain clearance have been linked to NDDs like Alzheimer’s disease (AD). At present, polysomnography (PSG) is the methodological gold standard in sleep research being used in sleep labs. However, it does not provide direct information on cerebral fluid dynamics which may be an important parameter linked to brain clearance activity during sleep. We have developed functional near-infrared spectroscopy (fNIRS) based method for assessment of human cerebral fluid dynamics during sleep. It is optimized as a wearable sleep monitoring device enabling overnight sleep recordings at home without disturbing natural sleep. In this paper, we study spectral entropy (SE) of cerebral fluid dynamics during sleep study. Developed fNIRS technique measures, in addition to cerebral hemodynamics, cortical water concentration changes reflecting dynamics of the cerebrospinal fluid (CSF) volume in macroscale. Our preliminary results of overnight fNIRS sleep measurements from 10 adult subjects show that SE values fluctuate in cycle during the whole night sleep. It may indicate the transition among sleep stages.
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