Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data.

A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.

Evaluation of the effectiveness of non-contact respiration rate detection for post-crash care application.

The ability of emergency medical services (EMS) personnel to understand the mortality and morbidity risks associated with different injuries is critical for effective motor vehicle collision (MVC) post-crash care. Interwoven with this ability is the practice of accurate and efficient patient triage. Triage allows EMS to gain a better understanding of the physiological and physical status of a patient and their injuries, with the intention of identifying additional resources needed, potential treatment options, and the most appropriate care destination. An interactive, in-vehicle triage system, based on the Simple Triage and Rapid Treatment protocol, under development could lead to improved MVC triage accuracy and efficiency. As a component of that system, this study examines the effectiveness of using non-contact respiration rate detection technologies integrated into a vehicle cabin. The selected technologies included ultrasonic respiration detection, thermal respiration detection, and pressurebased detection which were all compared against data collected from a respiration belt. All technologies were integrated into a 2015 Ford Taurus. Testing took place inside the running vehicle and considered occupant weight, cabin temperature, occupant clothing weight, and environmental sound as experimental factors. Respiration rate was then calculated using a 30-second sliding window where the means and standard deviations were used to compare the accuracy and precision of the sensor systems across the experimental factors. The sensor type, temperature level, and sound level main effects significantly influenced the mean respiration rate. For the standard deviation of respiration rate, significant effects were found for the sensor type, sound level, and clothing weight main effects. Among the tested sensor systems, the pressure sensor was found to best match the accuracy and precision of the respiration belt. Future work should address limitations in the approach used for this proof-of-concept work.

HFD refeeding in mice after fasting impairs learning by activating caspase-1 in the brain.

BACKGROUND: Diets that include some aspect of fasting have dramatically increased in popularity. In addition, fasting reduces inflammasome activity in the brain while improving learning. Here, we examine the impact of refeeding a low-fat diet (LFD) or high-fat diet (HFD) after fasting. METHODS: Male wildtype (WT), caspase-1 knockout (KO) and/or IL-1 receptor 1 (IL-1R1) KO mice were fasted for 24 h or allowed ad libitum access to food (chow). Immediately after fasting, mice were allowed to refeed for 2 h in the presence of LFD, HFD or chow. Mouse learning was examined using novel object recognition (NOR) and novel location recognition (NLR). Caspase-1 activity was quantified in the brain using histochemistry (HC) and image analysis. RESULTS: Refeeding with a HFD but not a LFD or chow fully impaired both NOR and NLR. Likewise, HFD when compared to LFD refeeding increased caspase-1 activity in the whole amygdala and, particularly, in the posterior basolateral nuclei (BLp) by 2.5-fold and 4.6-fold, respectively. When caspase-1 KO or IL-1R1 KO mice were examined, learning impairment secondary to HFD refeeding did not occur. Equally, administration of n-acetylcysteine to fasted WT mice prevented HFD-dependent learning impairment and caspase-1 activation in the BLp. Finally, the free-fatty acid receptor 1 (FFAR1) antagonist, DC260126, mitigated learning impairment associated with HFD refeeding while blocking caspase-1 activation in the BLp. CONCLUSIONS: Consumption of a HFD after fasting impairs learning by a mechanism that is dependent on caspase-1 and the IL-1R1 receptor. These consequences of a HFD refeeding on the BLP of the amygdala appear linked to oxidative stress and FFAR1.