Simultaneous measurement under resting-state of autonomic nervous activity and brain activity by near-infrared spectroscopy alone

Ouchi, E; Usuniwa, Hitoshi; Nemoto, Kiyotaka; Inagaki, Terumi

doi:10.1016/j.infrared.2022.104065

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…When this device was not available, the INVOS device stored the data in internal memory with a nonuniform sampling frequency typically between 1/34 and 1/35 Hz. To generate a uniform sampling rate, cubic spline interpolation was used to fill in the missing data [ 29 ]. Then, the rcSO 2 signal was up-sampled to a sampling frequency of 10 Hz, and a low-pass filter (a zero-phase finite impulse response filter, FIR) was used to prevent aliasing before down-sampling to 1/6 Hz.…”

Section: Methodsmentioning

confidence: 99%

“…To preprocess the SpO 2 signals, first values of less than 20% or sudden changes (more than 4% in one second) with a collar of 30 s were removed [ 31 ]. Missing data were interpolated using cubic spline interpolation [ 29 ], and the signal was down-sampled, after applying an appropriate anti-aliasing low-pass filter, to 1/6 Hz for agreement with the rcSO 2 signal. Missing data prior to down-sampling was then removed.…”

Section: Methodsmentioning

confidence: 99%

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Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

et al. 2023

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Objective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO2) and peripheral oxygen saturation (SpO2) signals to detect brain injury in extremely preterm infants. Study design: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligible infants were <28 weeks’ gestational age and had continuous rcSO2 measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO2 data were available for 32 infants. The rcSO2 and SpO2 signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II–IV) using a leave-one-out cross-validation approach. Results: The area under the receiver operating characteristic curve (AUC) for the PRD rcSO2 was 0.846 (95% CI: 0.720–0.948), outperforming the rcSO2 threshold approach (AUC 0.593 95% CI 0.399–0.775). Neither the clinical model nor any of the SpO2 models were significantly associated with brain injury. Conclusion: There was a significant association between the data-driven definition of PRDs in rcSO2 and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

et al. 2023

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Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Zhang¹,

Long²,

Wang³

et al. 2023

J. Innov. Opt. Health Sci.

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Interactions between the central nervous system (CNS) and autonomic nervous system (ANS) play a crucial role in modulating perception, cognition, and emotion production. Previous studies on CNS–ANS interactions, or heart–brain coupling, have often used heart rate variability (HRV) metrics derived from electrocardiography (ECG) recordings as empirical measurements of sympathetic and parasympathetic activities. Functional near-infrared spectroscopy (fNIRS) is a functional brain imaging modality that is increasingly used in brain and cognition studies. The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms. Therefore, fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously, allowing analysis of CNS–ANS interactions. In this proof-of-concept study, fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena (MOBA) game. From the fNIRS recordings, neural and heartbeat frequency bands were extracted to assess prefrontal activities and short-term pulse rate variability (PRV), an approximation for short-term HRV, respectively. Under the experimental conditions used, fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards, in terms of absolute measurements and video game playing (VGP)-related changes. It was also observed that, similar to previous studies on physical activity and exercise, the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP. It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP, facilitating the understanding of VGP-related heart–brain coupling.

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Simultaneous measurement under resting-state of autonomic nervous activity and brain activity by near-infrared spectroscopy alone

Cited by 2 publications

References 27 publications

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

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