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1.
Sleep Med ; 100: 573-576, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36327586

RESUMO

Previous research has shown an interplay between the thalamus and cerebral cortex during NREM sleep in humans, however the directionality of the thalamocortical synchronization is as yet unknown. In this study thalamocortical connectivity during different NREM sleep stages using sleep scalp electroencephalograms and local field potentials from the left and right anterior thalamus was measured in three epilepsy patients implanted with deep brain stimulation electrodes. Connectivity was assessed as debiased weighted phase lag index and granger causality between the thalamus and cortex for the NREM sleep stages N1, N2 and N3. Results showed connectivity was most prominently directed from cortex to thalamus. Moreover, connectivity varied in strength between the different sleep stages, but barely in direction or frequency. These results imply relatively stable thalamocortical connectivity during NREM sleep directed from the cortex to the thalamus.


Assuntos
Estimulação Encefálica Profunda , Humanos , Estimulação Encefálica Profunda/métodos , Fases do Sono/fisiologia , Eletroencefalografia/métodos , Tálamo , Córtex Cerebral/fisiologia , Sono/fisiologia
2.
Sci Rep ; 12(1): 18409, 2022 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-36319742

RESUMO

Unfolding the overnight dynamics in human sleep features plays a pivotal role in understanding sleep regulation. Studies revealed the complex reorganization of the frequency composition of sleep electroencephalogram (EEG) during the course of sleep, however the scale-free and the oscillatory measures remained undistinguished and improperly characterized before. By focusing on the first four non-rapid eye movement (NREM) periods of night sleep records of 251 healthy human subjects (4-69 years), here we reveal the flattening of spectral slopes and decrease in several measures of the spectral intercepts during consecutive sleep cycles. Slopes and intercepts are significant predictors of slow wave activity (SWA), the gold standard measure of sleep intensity. The overnight increase in spectral peak sizes (amplitudes relative to scale-free spectra) in the broad sigma range is paralleled by a U-shaped time course of peak frequencies in frontopolar regions. Although, the set of spectral indices analyzed herein reproduce known age- and sex-effects, the interindividual variability in spectral slope steepness is lower as compared to the variability in SWA. Findings indicate that distinct scale-free and oscillatory measures of sleep EEG could provide composite measures of sleep dynamics with low redundancy, potentially affording new insights into sleep regulatory processes in future studies.


Assuntos
Eletroencefalografia , Sono de Ondas Lentas , Humanos , Sono/fisiologia , Fases do Sono/fisiologia
3.
Int J Mol Sci ; 23(19)2022 Oct 04.
Artigo em Inglês | MEDLINE | ID: mdl-36233081

RESUMO

This work aimed to study the recovery of consciousness during forced awakening from slow-wave sleep (SWS) and rapid eye movement sleep (REM) in healthy volunteers. To track the changes in the degree of awareness of the stimuli during the transition to wakefulness, event-related potentials (ERPs) and motor responses (MR) in the auditory local-global paradigm were analyzed. The results show that during awakening from both SWS and REM, first, alpha-activity restores in the EEG, and only 20 and 25 s (for REM and SWS awakenings, respectively) after alpha onset MR to target stimuli recovers. During REM awakening, alpha-rhythm, MR, and conscious awareness of stimuli recover faster than during SWS awakening. Moreover, pre-attentive processing of local irregularities emerges earlier, even before alpha-rhythm onset, while during SWS awakening, the local effect we registered only after alpha restoration. The P300-like response both on global and local irregularities was found only when accurate MR was restored. Thus, the appearance in EEG predominating alpha-activity is insufficient either for conscious awareness of external stimuli or for generating MR to them. This work may help to understand the pathophysiology of sleep disorders well as conditions characterized by the dissociation between behavior and various aspects of consciousness.


Assuntos
Sono REM , Sono de Ondas Lentas , Estado de Consciência , Eletroencefalografia , Potenciais Evocados , Humanos , Sono/fisiologia , Fases do Sono/fisiologia , Vigília/fisiologia
4.
Sleep Med ; 100: 364-377, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36201888

RESUMO

OBJECTIVE/BACKGROUND: Slow wave activity (SWA) and sigma frequency activity (SFA) are hallmarks of NREM sleep EEG and important indicators of neural plasticity, development of the central nervous system, and cognition. However, little is known about the factors that modulate these sleep EEG activities, especially in small children. PATIENTS/METHODS: We analyzed the power spectral densities of SWA (1-4 Hz) and SFA range (10-15 Hz) from six EEG derivations of 56 infants (8 months) and 60 toddlers (24 months) during their all-night sleep and during the first and the last half of night sleep. The spectral values were compared between the four seasons. RESULTS: In the spring group of infants, compared with the darker seasons, SFA was lower in the centro-occipital EEG derivations during both halves of the night. The SWA findings of the infants were restricted to the last half of the night (SWA2) and frontally, where SWA2 was higher during winter than spring. The toddlers presented less frontal SWA2 during winter compared with autumn. Both age groups showed a reduction in both SWA and SFA towards the last half of the night. CONCLUSIONS: The sleep EEG spectral power densities are more often associated with seasons in infants' SFA range. The results might stem from seasonally changing light exposure, but the exact mechanism warrants further study. Moreover, contrary to the adult-like increment of SFA, the SFA at both ages was lower at the last part of the night sleep. This suggests different regulation of spindle activity in infants and toddlers.


Assuntos
Sono de Ondas Lentas , Sono , Adulto , Lactente , Pré-Escolar , Humanos , Estações do Ano , Sono/fisiologia , Eletroencefalografia/métodos , Fases do Sono/fisiologia
5.
Adv Exp Med Biol ; 1384: 17-29, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36217076

RESUMO

A growing number of studies have shown the strong relationship between sleep and different cognitive processes, especially those that involve memory consolidation. Traditionally, these processes were attributed to mechanisms related to the macroarchitecture of sleep, as sleep cycles or the duration of specific stages, such as the REM stage. More recently, the relationship between different cognitive traits and specific waves (sleep spindles or slow oscillations) has been studied. We here present the most important physiological processes induced by sleep, with particular focus on brain electrophysiology. In addition, recent and classical literature were reviewed to cover the gap between sleep and cognition, while illustrating this relationship by means of clinical examples. Finally, we propose that future studies may focus not only on analyzing specific waves, but also on the relationship between their characteristics as potential biomarkers for multiple diseases.


Assuntos
Eletroencefalografia , Consolidação da Memória , Encéfalo/fisiologia , Cognição , Consolidação da Memória/fisiologia , Sono/fisiologia , Fases do Sono/fisiologia
6.
Clin Neurophysiol ; 143: 75-83, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36155385

RESUMO

OBJECTIVE: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. METHODS: A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs. RESULTS: The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output. CONCLUSIONS: Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors. SIGNIFICANCE: The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.


Assuntos
Eletroencefalografia , Sono , Algoritmos , Eletroencefalografia/métodos , Humanos , Recém-Nascido , Polissonografia , Sono/fisiologia , Fases do Sono/fisiologia
7.
J Sleep Res ; 31(6): e13733, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36130730

RESUMO

Sleep spindles are a hallmark electroencephalographic feature of non-rapid eye movement sleep, and are believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain-stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real-time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector, a robust multi-channel electroencephalographic signal-processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the real-time spindle detection method by streaming pre-recorded sleep electroencephalographic datasets to a real-time computer system running a Simulink® Real-Time™ implementation of the algorithm. Sleep spindles were detected with high levels of Sensitivity (~83%), Precision (~78%) and a convincing F1-Score (~81%) in reference to state-of-the-art offline algorithms (which reached similar or lower levels when compared with each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude and symmetry, and showed the typical time-frequency characteristics as well as a centroparietal topography. Spindles were detected close to their centre and reliably at the predefined target phase. The real-time spindle detection algorithm therefore empowers researchers to target spindles during human sleep, and apply the stimulation method and experimental paradigm of their choice.


Assuntos
Eletroencefalografia , Sono , Humanos , Eletroencefalografia/métodos , Sono/fisiologia , Algoritmos , Encéfalo/fisiologia , Fases do Sono/fisiologia
8.
Infant Behav Dev ; 69: 101775, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36126380

RESUMO

Research studying the role of sleep in development abounds but focuses on global aspects of sleep like quality or timing. Far fewer studies include the ultradian cycle, or patterns of REM and non-REM (NREM), because doing so is costly in time and resources. In a complete, lab-based sleep study, individuals are monitored by a technician overnight while wearing a host of sensors to capture brain activity, eye and limb movement, and cardiorespiratory rates. There is a need for creative, minimally disruptive solutions to study sleep that do not compromise the richness and accuracy of the measurements. The current pilot study parsed down the physiological measures to only movement and cardiorespiratory rates, creating a protocol simple enough for caregivers to incorporate into bedtime. Ten 12-month-old infants (+/- 3 weeks) wore an actigraph and wireless cardiorespiratory sensor for five nights of data collection. Of this, 92% were useable data. Actigraphy was analyzed with the Sadeh algorithm to delineate sleep from wake. Heart rate and respiration were then used to state score visually or via an algorithm; greater variability demarcated REM from NREM. Time spent in each state was compared between scoring methods as well as to published results from age matched infants who underwent polysomnography (PSG). Visually scored data, using a 1-hour viewing window, was in line with peer's PSG values. To automatically state score, epoch-by-epoch cardiorespiratory and actigraphy files were produced for each minute of data collection. Heart and respiratory rates were transformed into z-scores and iterations of scoring, using increasingly greater z score thresholds, were compared to determine which identified state proportions most similar to data collected with PSG. Based on these results, our novel method appears to be a feasible choice for studying the ultradian cycle. The combination of actigraphy and cardiorespiratory monitoring is uniquely advantageous because it is less resource intensive and more naturalistic, being put on by caregivers while still resulting in high rates of good data. Taken together, it is a quality option for infant researchers interested in incorporating sleep into their paradigms.


Assuntos
Actigrafia , Fases do Sono , Lactente , Humanos , Polissonografia/métodos , Fases do Sono/fisiologia , Projetos Piloto , Actigrafia/métodos , Sono/fisiologia
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4942-4945, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085976

RESUMO

This work proposes a method utilizing only the submentalis EMG channel for the classification of sleep and wake states among the healthy individuals and patients with various sleep disorders such as sleep apnea hypopnea syndrome, dyssomnia, etc. We extracted autoregressive model parameters, discrete wavelet transform coefficients, Hjorth's complexity and mobility, relative bandpowers, Poincaré plot descriptors and statistical features from the EMG signal. We also used the energy of each epoch as a feature to distinguish between the sleep and wake states. Mutual information based feature selection approach was considered to obtain the top 25 features which provided maximum accuracy. For classification, we employed an ensemble of decision trees with random undersampling and boosting technique to deal with the class-imbalance problem in the sleep data. We achieved an overall accuracy of about 85% for the healthy population and about 70% on an average across different pathological groups. This work shows the potential of EMG chin activity for sleep analysis. Clinical Relevance- Automatic and reliable sleep-wake classification can reduce the burden of sleep experts in analyzing overnight sleep data (~ 8 hours) and also assist them to diagnose various neurological disorders at an early stage. Utilizing EMG channel provides an easier and convenient long-term recording of data without causing much disturbance in sleepunlike EEG which is inconvenient and hampers the natural sleep.


Assuntos
Apneia Obstrutiva do Sono , Fases do Sono , Humanos , Músculos , Polissonografia/métodos , Sono , Fases do Sono/fisiologia
10.
Artigo em Inglês | MEDLINE | ID: mdl-36099215

RESUMO

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.


Assuntos
Eletroencefalografia , Sono , Encéfalo , Voluntários Saudáveis , Humanos , Sono/fisiologia , Fases do Sono/fisiologia
11.
Sensors (Basel) ; 22(16)2022 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-36016077

RESUMO

The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.


Assuntos
Dispositivos Eletrônicos Vestíveis , Adulto , Frequência Cardíaca/fisiologia , Humanos , Polissonografia , Sono/fisiologia , Fases do Sono/fisiologia
12.
J Neural Eng ; 19(5)2022 09 08.
Artigo em Inglês | MEDLINE | ID: mdl-36001951

RESUMO

Objective. Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establish an automatic narcolepsy detection method for MSLT.Approach.We construct a two-phase model on MSLT recordings, where ambiguous sleep staging and sleep transition dynamics make joint efforts to address this issue. In phase 1, we extract representative features from electroencephalogram (EEG) and electrooculogram (EOG) signals. Then, the features are input to an EasyEnsemble classifier for automatic sleep staging. In phase 2, we investigate sleep transition dynamics, including sleep stage transitions and sleep stages, and output likelihood of narcolepsy by virtue of principal component analysis (PCA) and a logistic regression classifier. To demonstrate the proposed framework in clinical application, we conduct experiments on 24 participants from the Children's Hospital of Fudan University, considering ten patients with narcolepsy and fourteen patients with MSLT negative.Main results.Applying the two-phase leave-one-subject-out testing scheme, the model reaches an accuracy, sensitivity, and specificity of 87.5%, 80.0%, and 92.9% for narcolepsy detection. Influenced by disease pathology, accuracy of automatic sleep staging in narcolepsy appears to decrease compared to that in the non-narcoleptic population.Significance.This method can automatically and efficiently distinguish patients with narcolepsy based on MSLT. It probes into the amalgamation of automatic sleep staging and sleep transition dynamics for narcolepsy detection, which would assist clinic and neuroelectrophysiology specialists in visual interpretation and diagnosis.


Assuntos
Narcolepsia , Criança , Eletroculografia , Humanos , Narcolepsia/diagnóstico , Polissonografia/métodos , Sono/fisiologia , Fases do Sono/fisiologia
13.
Neural Netw ; 154: 310-322, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35930855

RESUMO

Computational sleep scoring from multimodal neurophysiological time-series (polysomnography PSG) has achieved impressive clinical success. Models that use only a single electroencephalographic (EEG) channel from PSG have not yet received the same clinical recognition, since they lack Rapid Eye Movement (REM) scoring quality. The question whether this lack can be remedied at all remains an important one. We conjecture that predominant Long Short-Term Memory (LSTM) models do not adequately represent distant REM EEG segments (termed epochs), since LSTMs compress these to a fixed-size vector from separate past and future sequences. To this end, we introduce the EEG representation model ENGELBERT (electroEncephaloGraphic Epoch Local Bidirectional Encoder Representations from Transformer). It jointly attends to multiple EEG epochs from both past and future. Compared to typical token sequences in language, for which attention models have originally been conceived, overnight EEG sequences easily span more than 1000 30 s epochs. Local attention on overlapping windows reduces the critical quadratic computational complexity to linear, enabling versatile sub-one-hour to all-day scoring. ENGELBERT is at least one order of magnitude smaller than established LSTM models and is easy to train from scratch in a single phase. It surpassed state-of-the-art macro F1-scores in 3 single-EEG sleep scoring experiments. REM F1-scores were pushed to at least 86%. ENGELBERT virtually closed the gap to PSG-based methods from 4-5 percentage points (pp) to less than 1 pp F1-score.


Assuntos
Eletroencefalografia , Fases do Sono , Eletroencefalografia/métodos , Polissonografia/métodos , Sono/fisiologia , Fases do Sono/fisiologia , Sono REM/fisiologia
14.
Sleep ; 45(10)2022 10 10.
Artigo em Inglês | MEDLINE | ID: mdl-35951011

RESUMO

STUDY OBJECTIVES: Snoozing was defined as using multiple alarms to accomplish waking, and considered as a method of sleep inertia reduction that utilizes the stress system. Surveys measured snoozing behavior including who, when, how, and why snoozing occurs. In addition, the physiological effects of snoozing on sleep were examined via wearable sleep staging and heart rate (HR) activity, both over a long time scale, and on the days that it occurs. We aimed to establish snoozing as a construct in need of additional study. METHODS: A novel survey examined snoozing prevalence, how snoozing was accomplished, and explored possible contributors and motivators of snoozing behavior in 450 participants. Trait- and day-level surveys were combined with wearable data to determine if snoozers sleep differently than nonsnoozers, and how snoozers and nonsnoozers differ in other areas, such as personality. RESULTS: 57% of participants snoozed. Being female, younger, having fewer steps, having lower conscientiousness, having more disturbed sleep, and being a more evening chronotype increased the likelihood of being a snoozer. Snoozers had elevated resting HR and showed lighter sleep before waking. Snoozers did not sleep less than nonsnoozers nor did they feel more sleepiness or nap more often. CONCLUSIONS: Snoozing is a common behavior associated with changes in sleep physiology before waking, both in a trait- and state-dependent manner, and is influenced by demographic and behavioral traits. Additional research is needed, especially in detailing the physiology of snoozing, its impact on health, and its interactions with observational studies of sleep.


Assuntos
Sono , Vigília , Feminino , Humanos , Masculino , Projetos de Pesquisa , Sono/fisiologia , Fases do Sono/fisiologia , Inquéritos e Questionários , Vigília/fisiologia
15.
Sensors (Basel) ; 22(14)2022 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-35891065

RESUMO

Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method.


Assuntos
Condução de Veículo , Vigília , Eletroencefalografia/métodos , Fadiga , Humanos , Reprodutibilidade dos Testes , Fases do Sono/fisiologia , Vigília/fisiologia
16.
J Healthc Eng ; 2022: 2016598, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35844670

RESUMO

As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people's quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient's mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen's Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.


Assuntos
Qualidade de Vida , Fases do Sono , Frequência Cardíaca/fisiologia , Humanos , Polissonografia/métodos , Sono , Fases do Sono/fisiologia
17.
Neuroimage ; 260: 119490, 2022 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-35853543

RESUMO

Spatial hearing in humans is a high-level auditory process that is crucial to rapid sound localization in the environment. Both neurophysiological models with animals and neuroimaging evidence from human subjects in the wakefulness stage suggest that the localization of auditory objects is mainly located in the posterior auditory cortex. However, whether this cognitive process is preserved during sleep remains unclear. To fill this research gap, we investigated the sleeping brain's capacity to identify sound locations by recording simultaneous electroencephalographic (EEG) and magnetoencephalographic (MEG) signals during wakefulness and non-rapid eye movement (NREM) sleep in human subjects. Using the frequency-tagging paradigm, the subjects were presented with a basic syllable sequence at 5 Hz and a location change that occurred every three syllables, resulting in a sound localization shift at 1.67 Hz. The EEG and MEG signals were used for sleep scoring and neural tracking analyses, respectively. Neural tracking responses at 5 Hz reflecting basic auditory processing were observed during both wakefulness and NREM sleep, although the responses during sleep were weaker than those during wakefulness. Cortical responses at 1.67 Hz, which correspond to the sound location change, were observed during wakefulness regardless of attention to the stimuli but vanished during NREM sleep. These results for the first time indicate that sleep preserves basic auditory processing but disrupts the higher-order brain function of sound localization.


Assuntos
Sono de Ondas Lentas , Localização de Som , Animais , Eletroencefalografia/métodos , Movimentos Oculares , Humanos , Sono/fisiologia , Fases do Sono/fisiologia , Vigília/fisiologia
18.
Neurobiol Learn Mem ; 194: 107662, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-35870718

RESUMO

The relationship between sleep and memory consolidation has not been fully revealed. The current study aimed to investigate how a brief afternoon nap contributed to the consolidation of declarative and procedural memory by exploring the relationship between sleep characteristics (i.e., the durations of sleep stages and slow oscillation, slow-wave activity, and spindle activity extracted from sleep) and task performance and the relationship between delta, theta, alpha, and beta bands extracted from wake during task performance and task performance. Twenty-three healthy young adults underwent a paired associates learning task and a sequential finger-tapping task with easy and difficult levels and were tested for memory performance before and after the intervention (i.e., an about 30-min nap or stay awake). Electroencephalogram (EEG) signals were continously recorded during the whole experiment. Results revealed that a short afternoon nap improved movement speed for the procedural memory task, regardless of the task difficulty, but unaffected the performance on the declarative memory task. Besides, the improvement in movement speed for the easy procedural memory task was positively correlated with slow-wave activity (SWA) during non-rapid-eye-movement (NREM) sleep but negatively correlated with slow oscillation and spindle activity during sleep stage 2 and NREM sleep, and the improvement in the difficult procedural memory task correlated positively with SWA during NREM sleep. Moreover, performance on the easy declarative and procedural memory tasks was negatively correlated with the relative power of alpha or theta; whereas the alpha band was positively correlated with the difficult declarative memory performance. These findings suggested that a brief afternoon nap with NREM sleep would benefit procedural memory consolidation but not declarative memory; such contribution of napping to memory consolidation would be either explained by the sleep characteristics or physiological arousal during performing tasks; task difficulty would moderate the relationship between the declarative memory performance and EEGs during task performance.


Assuntos
Consolidação da Memória , Sono de Ondas Lentas , Humanos , Consolidação da Memória/fisiologia , Sono/fisiologia , Fases do Sono/fisiologia , Vigília/fisiologia , Adulto Jovem
19.
J Neural Eng ; 19(4)2022 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-35878599

RESUMO

Objective.Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep.Approach.A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods.Main results.The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs.Significance.A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.


Assuntos
Eletroencefalografia , Imageamento por Ressonância Magnética , Encéfalo/fisiologia , Eletroencefalografia/métodos , Humanos , Imageamento por Ressonância Magnética/métodos , Sono , Fases do Sono/fisiologia
20.
Comput Biol Med ; 147: 105804, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35803081

RESUMO

Cyclic alternating pattern (CAP) sequences are composed of cycles of alternate activation phases (A-phases) and background phases. CAP A-phases can be further divided into three subtypes, which act as important bio-markers of sleep instability and are also associated with identifiable sleep pathologies. Thus, its accurate detection and identification is of great clinical interest and significance. To release the burden of sleep experts who manually perform this labeling task, several automatic detectors have been proposed, yet the characteristics of CAP have not been fully exploited to achieve a satisfactory performance. In this paper, we propose an automated method to detect A-phases and their subtypes using Transformer-based U-Net framework. In light of the long-span duration of A-phases, our method has intrinsic advantages as U-Net extracts local information while Transformer module provides global dependencies. We also use a curriculum-learning based training strategy to further improve the performance. The method is validated on the publicly available CAP Sleep Database. It obtains average F1 scores of 67.78% and 72.16% on 16 healthy subjects and 30 patients with nocturnal frontal lobe epilepsy respectively for A-phase detection, and the average macro F1-score is 59.5% for multi-class subtype classification. Compared with state-of-the-art methods, the proposed method achieves superior performance in these two CAP labeling tasks.


Assuntos
Eletroencefalografia , Sono , Bases de Dados Factuais , Eletroencefalografia/métodos , Endoscopia , Voluntários Saudáveis , Humanos , Sono/fisiologia , Fases do Sono/fisiologia
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