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Chronic sleep restriction has been linked to occupational errors and motor vehicle crashes. Enhancing slow wave sleep may alleviate some of the cognitive deficits associated with chronic sleep restriction. However, the extent to which acoustic stimulation of slow wave activity (SWA) may improve alertness and attention is not well established, particularly with respect to consecutive nights of exposure.Twenty-five healthy adults (32.9 ± 8.2 years; 16 female) who self-restricted their sleep during workdays participated in a randomized, double-blind, cross-over study. Participants wore an automated acoustic stimulation device for two consecutive nights. Acoustic tones (50 ms long) were delivered on the up-phase of the slow wave first and then at constant 1-s inter-tone-intervals once N3 was identified (STIM), until an arousal or shift to another sleep stage occurred, or at inaudible decibels during equivalent stimulation periods (SHAM). Subjective alertness/fatigue (KSS, Samn-Perelli) was assessed across both days, and objective measures of alertness (MSLT) and attention (PVT) were assessed after two nights of stimulation.After one night of acoustic stimulation, increased slow wave energy was observed in 68% of participants, with an average significant increase of 17.7% (p = 0.01), while Night 2 was associated with a 22.2% increase in SWA (p = 0.08). SWE was highly stable across the two nights of STIM (ICC 0.93, p 0.001), and around half (56%) of participants were consistently classified as responders (11/25) or non-responders (3/25). Daytime testing showed that participants felt more alert and awake following each night of acoustic stimulation (p 0.05), with improved objective attention across the day following two nights of acoustic stimulation.Consecutive nights of acoustic stimulation enhanced SWA on both nights, and improved next day alertness and attention. Given large individual differences, we highlight the need to examine both the long-term effects of stimulation, and to identify inter-individual differences in acoustic stimulation response. Our findings suggest that the use of an acoustic device to enhance slow wave sleep may alleviate some of the deficits in alertness and attention typically associated with sleep restriction.

Acoustic stimulation has been shown to enhance slow wave sleep and in turn, cognition, and now cardiac outcomes in young adults. With the emergence of commercial acoustic devices in the home, we sought to examine the impact of an acoustic, slow wave enhancing device on heart rate variability in healthy, middle-aged males (n = 24, 39.92 ± 4.15 years). Under highly controlled conditions, the participants were randomised to receive closed-loop brain state-dependent stimulation in the form of auditory tones (STIM), or no tones (SHAM), in a crossover design, separated by a 1 week washout period. STIM and SHAM were compared on measures of heart rate variability for the whole night and over the first three sleep cycles. We found an increase in slow wave activity following STIM compared with SHAM. There was a significant increase in high frequency power and standard deviation of the normalised RR-intervals (SDNN) during the STIM condition compared with SHAM (p lt; 0.05), due to changes observed specifically during N3. In conclusion, heart rate variability appears to improve following acoustic slow wave sleep enhancement.

Objective. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals. Approach. Data from healthy and apnea subjects were used for training and validation; 2 additional datasets (healthy and sleep disorders subjects) were used for testing. R-peak detection was used to determine IBIs before resampling at 2 Hz; the resulting signal was segmented into 150 s windows (30 s shift). DNN output approximated the probabilities of a window belonging to light, deep, REM, or wake stages. Cohen’s Kappa, accuracy, and sensitivity/specificity per stage were determined, and Kappa was optimized using thresholds on probability ratios for each stage versus light sleep. Main results. Mean (SD) Kappa and accuracy for 4 sleep stages were 0.44 (0.09) and 0.65 (0.07), respectively, in healthy subjects. For 3 sleep stages (light+deep, REM, and wake), Kappa and accuracy were 0.52 (0.12) and 0.76 (0.07), respectively. Algorithm performance on data from subjects with REM behavior disorder or periodic limb movement disorder was significantly worse, with Kappa of 0.24 (0.09) and 0.36 (0.12), respectively. Average processing time by an ARM microprocessor for a 300-sample window was 19.2 ms. Significance. IBIs can be obtained from a variety of cardiac signals, including electrocardiogram, photoplethysmography, and ballistocardiography. The DNN algorithm presented is 3 orders of magnitude smaller compared with state-of-the-art algorithms and was developed to perform real-time, IBI-based sleep staging. With high specificity and moderate sensitivity for deep and REM sleep, small footprint, and causal processing, this algorithm may be used across different platforms to perform real-time sleep staging and direct intervention strategies. Novelty & Significance (92/100 words) This article describes the development and testing of a deep neural network-based algorithm to detect sleep stages using interbeat intervals, which can be obtained from a variety of cardiac signals including photoplethysmography, electrocardiogram, and ballistocardiography. Based on the interbeat intervals identified in electrocardiogram signals, the algorithm architecture included a group of convolution layers and a group of long short-term memory layers. With its small footprint, fast processing time, high specificity and good sensitivity for deep and REM sleep, this algorithm may provide a good option for real-time sleep staging to direct interventions.

The transition from wake to sleep is a continuum that is well characterized by the electroencephalogram (EEG) power spectral ratio (ρ) between the beta (15 to 30 Hz) and theta (4 to 8 Hz) bands. From wake to sleep, the value of ρ gradually decreases.We have designed and implemented a single EEG-signal based closed-loop system that leverages ρ to modulate the volume of a pink-noise type of audio such that the volume becomes gradually softer as sleep initiates. A proof-of-concept trial was conducted with this system and it was found that using this concept resulted in a reduction of sleep latency and latency to deep sleep.

Introduction Sleep is associated with temperature changes in the human body. At sleep onset, distal (hands and feet) and proximal (abdomen) temperatures increase by ~1°C and ~0.5°C, respectively, forming a distal-to-proximal gradient that increases throughout the first half of a night’s sleep. However, core temperature decreases during this period. Few devices can measure these temperatures unobtrusively. Our aim was to estimate distal skin temperature unobtrusively during sleep using a temperature sensor array on a mattress. Methods Three male volunteers participated in the study. Skin temperatures were measured using an array of 5 equally spaced thermistors distributed laterally across the mattress region aligning with the torso. Participants wore an Empatica smart watch to provide benchmark distal skin temperatures.Data from 249 sleep hours were used to build predictive models estimating distal skin temperature. The preprocessed data were grouped by participant and segmented into training and test sets (~60%/40%, respectively), with earlier sleep sessions selected for training and later sessions selected for testing. Using the Automated Machine Learning (AutoML) feature in Databricks, a model was developed that optimized R2. This model was applied to the test set and its performance evaluated by Bland-Altman analysis, using predicted and benchmark distal temperatures. Results The AutoML selected an XGBoost decision-tree model to predict distal skin temperature for each minute. The mean difference in temperature between predicted and benchmark readings was 0.13°C (R2=0.35), with lower and upper limits of agreement (LOA) of -1.59 and 1.84, respectively. Next, all minute-level data were averaged by sleep session, and model performance was re-evaluated across all sleep sessions. This approach resulted in smaller LOAs (-0.58, 0.91), with a mean difference in temperature of 0.16°C (R2=0.73). Conclusion Our results suggest that a temperature sensor array, coupled with an optimized decision-tree model, can predict mean distal skin temperature for each sleep session with reasonable accuracy. Our system enabled unobtrusive, ecologically valid collection of distal skin temperatures during sleep and may be useful for future studies of overnight temperature dynamics. Support (If Any) This study was funded by Sleep Number Corporation. Medical writing support, by Sandra J. Page, PhD, Oxford PharmaGenesis Inc., was funded by Sleep Number Corporation.

Introduction During the wake to sleep transition, the EEG exhibits a reduction in the power in the beta (15-30 Hz) band and an increase in the power of the theta (4-8 Hz) band. In previous publications we reported that the log-ratio “ρ=10×log(β/θ)” quasi-monotonically decreases by an order of magnitude as sleep initiates. Methods We developed a closed-loop, real-time system that processes a single EEG signal (FPz-M2) to modulate the volume of (pink-noise) sound according to “ρ=10×log(β/θ)”. The volume was calibrated such that it progressively decreases as sleep initiates. The EEG was acquired using the Philips AliceTM PSG station connected to a laptop where the algorithm was implemented. The sound was played through a wearable headband connected to the laptop’s audio-output. The algorithm processes 6-second EEG windows to estimate: 1) a signal quality index, 2) the average “ρ”, and 3) the sleep stage using a deep-learning stager. The volume changes with “ρ” according to a sigmoidal model. From the time where N2 or N3 sleep has been continuously detected for 3 minutes, the volume decays to zero in an exponential fashion. Seven subjects without any sleep disorder diagnosis (3F/4M; 33.6 ± 8.7 years old) participated in a home-based trial and recorded 5 sleep sessions. The first familiarization session was followed by randomized 2-session blocks: Block 1: closed-loop volume modulation (active), and Block 2 open-loop (sham) constant volume decrease. Results A 2.2-minute decrease (p=0.1) in average sleep latency was found in the active condition (11.6 ± 5.0m) w.r.t. the sham condition (13.8 ± 6.1m). A 5.2-minute decrease (p=0.08) in average N3 latency was found in the active condition (29.3 ± 10.4m) w.r.t. the sham condition (34.5 ± 13.6m). The log-ratio decreased significantly faster (p Conclusion Closed-loop modulation of the volume of pink-noise based on the EEG’s β/θ ratio may promote a faster sleep onset and a faster transition into deeper NREM sleep. The statistically trending results reported in this research grant further experimental validation with a larger number of subjects. Support Philips Sleep and Respiratory Care

Contains fulltext : 198169.pdf (Publisher’s version ) (Open Access) Objective. Recent evidence reports cognitive, metabolic, and sleep restoration benefits resulting from the enhancement of sleep slow-waves using auditory stimulation. Our objective is to make this concept practical for consumer use by developing and validating an electroencephalogram (EEG) closed-loop system to deliver auditory stimulation during sleep to enhance slow-waves. Approach. The system automatically detects slow-wave sleep with 74% sensitivity and 97% specificity and optimally delivers stimulation in the form of 50 ms-long tones separated by a constant one-second inter-tone interval at a volume that is dynamically modulated such that louder tones are delivered when sleep is deeper. The system was tested in a study involving 28 participants (18F, 10M; 36.9 ± 7.3 years old; median age: 40 years old) who used the system for ten nights (five nights in a sham condition and five in a stimulation condition). Four nights in each condition were recorded at-home and the fifth one in-lab. Main results. The analysis in two age groups defined by the median age of participants in the study shows significant slow wave activity enhancement (+16.1%, p

Contains fulltext : 149748.pdf (Publisher’s version ) (Closed access) Objective. Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. Approach. We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40–60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. Main results. Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and beta (15–30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline after approx. 500 ms. During the steady-state response, we observed alpha band desynchronization over occipital sites and after 500 ms also over frontal sites, while neighboring areas synchronized. The power in beta band over occipital sites increased during the stimulation period, possibly caused by increase in power at sub-harmonic frequencies of stimulation. Gamma power was also enhanced by the stimulation. Significance. These findings have direct implications on the use of RVS and SSVEPs for neural process investigation through steady-state topography, controlled entrainment of brain oscillations and BCIs. A deep understanding of SSVEP propagation in time and space and the link with ongoing brain rhythms is crucial for optimizing the typical SSVEP applications for studying, assisting, or augmenting human cognitive and sensorimotor function. 11 p.

Introduction: In the two-process model of sleep regulation, slow-wave activity (SWA, EEG power in the 0.5–4 Hz band) is a direct indicator of sleep need. SWA builds up during NREM sleep, declines before the onset of REM sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. The sleep regulation model of Borbely et al, 1999, states that the rate of decrease in sleep need S(t) is proportional to SWA, i.e. dS(t)/dt =-γSWA(t) where γ is the decay rate. The sleep need after T minutes of sleep is: S(T)=S0-γCSWA(T), where S0 is the sleep need at sleep-onset and CSWA(T) is the integral of SWA from sleep onset to time T. The goal in this research is to assess the effect of age on the dynamics of sleep need dissipation. Methods: Sleep EEG and EOG data were collected from 25 subjects (10M and 15F; 37.1 ± 6.5 years old) for 3 nights at home. The data was manually scored into sleep stages according to AASM rules. SWA was calculated for each 6-second epoch of NREM sleep. In this model, sleep need dissipation is completely determined by S0 and γ. To estimate these, two boundary conditions were used: 1) the final value of S(t) is 0, and 2) S(t*) coincides with SWA(t*) at time t* where SWA is maximum in the first sleep cycle. Results: Two age groups were defined using as threshold the median age (38) of subjects in the study. S0 did not significantly differ between groups while γ was significantly different (γ group1=2.82 and γ group2=2.12; p=0.006). These results suggest that the efficiency of sleep need dissipation significantly decreases in the older group by 25%. Conclusion: The rate of sleep need dissipation is proportional to slow wave activity. The proportionality coefficient γ (decay rate) can be estimated using a differential model. It was found that γ is significantly lower, by 25%, in the age group 38 to 47 as compared to the age group 22 to 38. The initial sleep need did not differ between groups.

Even modest sleep restriction, especially the loss of sleep slow wave activity (SWA), is invariably associated with slower electroencephalogram (EEG) activity during wake, the occurrence of local sleep in an otherwise awake brain, and impaired performance due to cognitive and memory deficits. Recent studies not only confirm the beneficial role of sleep in memory consolidation, but also point to a specific role for sleep slow waves. Thus, the implementation of methods to enhance sleep slow waves without unwanted arousals or lightening of sleep could have significant practical implications. Here we first review the evidence that it is possible to enhance sleep slow waves in humans using transcranial direct-current stimulation (tDCS) and transcranial magnetic stimulation. Since these methods are currently impractical and their safety is questionable, especially for chronic long-term exposure, we then discuss novel data suggesting that it is possible to enhance slow waves using sensory stimuli. We consider the physiology of the K-complex (KC), a peripheral evoked slow wave, and show that, among different sensory modalities, acoustic stimulation is the most effective in increasing the magnitude of slow waves, likely through the activation of non-lemniscal ascending pathways to the thalamo-cortical system. In addition, we discuss how intensity and frequency of the acoustic stimuli, as well as exact timing and pattern of stimulation, affect sleep enhancement. Finally, we discuss automated algorithms that read the EEG and, in real-time, adjust the stimulation parameters in a closed-loop manner to obtain an increase in sleep slow waves and avoid undesirable arousals. In conclusion, while discussing the mechanisms that underlie the generation of sleep slow waves, we review the converging evidence showing that acoustic stimulation is safe and represents an ideal tool for slow wave sleep (SWS) enhancement.