Heart Rate Extraction From Neonatal Near-Infrared Spectroscopy Signals

Near-infrared spectroscopy (NIRS) intensity signals provide useful additional physiological information, of which the most prominent one is the pulsatile fluctuation by heartbeats. This allows for the extraction of heart rate (HR), one of the primary clinical indicators of health in neonates. In this study, we propose a novel algorithm, NIRS HR (NHR), for extracting HR from NIRS signals acquired from neonates admitted to the neonatal intensive care unit (NICU). After parental consent, we synchronously recorded NIRS at 100 Hz and reference HR (RHR) at 1 Hz, from ten newborn infants (gestational age=38 \pm 5 weeks; 3092 ± 990 g). The NHR algorithm consists of two main parts. The first part includes four steps implemented once on the whole NIRS measurement: preprocessing; HR frequency bandwidth determination; interquartile range (IQR) computation; and segmentation. The second part includes three steps implemented on each signal segment: motion artifact detection, signal quality assessment, and HR computation. We compared the NHR algorithm with two existing algorithms. The results showed that the proposed NHR algorithm provides a significantly ( p < 0.05) higher correlation ( r = 99.5%) and lower Bland-Altman ratio (BAR = 3.6%) between the extracted and RHRs, compared to the existing algorithms. Extracting HR from NIRS in a clinical setting of critically ill neonates admitted to neonatal intensive care is feasible. With NIRS and HR combined in a single monitoring system, it is possible to have a perfectly time-synced integrated analysis of cerebral hemodynamics, as well as systemic hemodynamics and autonomic nervous system tone

Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations

Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works