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2.
Sensors (Basel) ; 21(18)2021 Sep 09.
Artigo em Inglês | MEDLINE | ID: mdl-34577255

RESUMO

Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.


Assuntos
Eletroencefalografia , Qualidade de Vida , Nível de Alerta , Humanos , Aprendizado de Máquina , Sono , Fases do Sono
3.
Sensors (Basel) ; 21(15)2021 Jul 27.
Artigo em Inglês | MEDLINE | ID: mdl-34372308

RESUMO

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.


Assuntos
Sono , Dispositivos Eletrônicos Vestíveis , Fotopletismografia , Polissonografia , Fases do Sono
4.
Comput Methods Programs Biomed ; 208: 106280, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34333204

RESUMO

BACKGROUND AND OBJECTIVES: while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years to relieve sleep staging from its heavy requirements, through the collection of more easily assessable signals and its automation using machine learning. However, these alternatives have their limitations, some due to variabilities among and between subjects, other inherent to their use of sub-discriminative signals. Many new solutions rely on the evaluation of the Autonomic Nervous System (ANS) activation through the assessment of the heart-rate (HR); the latter is modulated by the aforementioned variabilities, which may result in data and concept shifts between what was learned and what we want to classify. Such adversary effects are usually tackled by Transfer Learning, dealing with problems where there are differences between what is known (source) and what we want to classify (target). In this paper, we propose two new kernel-based methods of transfer learning and assess their performances in Rapid-Eye-Movement (REM) sleep stage detection, using solely the heart rate. METHODS: our first contribution is the introduction of Kernel-Cross Alignment (KCA), a measure of similarity between a source and a target, which is a direct extension of Kernel-Target Alignment (KTA). To our knowledge, KCA has currently never been studied in the literature. Our second contribution is two alignment-based methods of transfer learning: Kernel-Target Alignment Transfer Learning (KTATL) and Kernel-Cross Alignment Transfer Learning (KCATL). Both methods differ from KTA, whose traditional use is kernel-tuning: in our methods, the kernel has been fixed beforehand, and our objective is the improvement of the estimation of unknown target labels by taking into account how observations relate to each other, which, as it will be explained, allows to transfer knowledge (transfer learning). RESULTS: we compare performances with transfer learning (KCATL, KTATL) to performances without transfer using a fixed classifier (a Support Vector Classifier - SVC). In most cases, both transfer learning methods result in an improvement of performances (higher detection rates for a fixed false-alarm rate). Our methods do not require iterative computations. CONCLUSION: we observe improved performances using our transfer methods, which are computationally efficient, as they only require the computation of a kernel matrix and are non-iterative. However, some optimisation aspects are still under investigation.


Assuntos
Aprendizado de Máquina , Fases do Sono , Frequência Cardíaca , Humanos , Polissonografia
5.
Nutrients ; 13(8)2021 Aug 17.
Artigo em Inglês | MEDLINE | ID: mdl-34444980

RESUMO

Recent animal studies have supported that Lactobacillus plantarum PS128 (PS128) can reduce the severity of anxiety and depression. However, previous studies did not focus on the sleep quality and mood of humans. This study determines whether PS128 reduces the severity of anxiety and depressive symptoms, regulates autonomic nervous system function, and improves sleep quality. Forty participants between 20 and 40 years of age with self-reported insomnia were randomly assigned to two groups, a PS128 group and a placebo group, in a double-blind trial. Participants took two capsules of either PS128 or a placebo after dinner for 30 days. Study measures included subjective depressive symptoms, anxiety and sleep questionnaires, and miniature-polysomnography recordings at baseline and on the 15th and 30th days of taking capsules. Overall, all outcomes were comparable between the two groups at baseline and within the 30-day period, yet some differences were still found. Compared to the control group, the PS128 group showed significant decreases in Beck Depression Inventory-II scores, fatigue levels, brainwave activity, and awakenings during the deep sleep stage. Their improved depressive symptoms were related to changes in brain waves and sleep maintenance. These findings suggest that daily administration of PS128 may lead to a decrease in depressive symptoms, fatigue level, cortical excitation, and an improvement in sleep quality during the deep sleep stage. Daily consumption of PS128 as a dietary supplement may improve the depressive symptoms and sleep quality of insomniacs, although further investigation is warranted.


Assuntos
Ansiedade/tratamento farmacológico , Depressão/tratamento farmacológico , Lactobacillus plantarum , Probióticos , Distúrbios do Início e da Manutenção do Sono/tratamento farmacológico , Sono , Adulto , Afeto , Ansiedade/complicações , Ansiedade/microbiologia , Transtornos de Ansiedade/complicações , Transtornos de Ansiedade/tratamento farmacológico , Transtornos de Ansiedade/microbiologia , Ondas Encefálicas , Depressão/complicações , Depressão/microbiologia , Transtorno Depressivo/complicações , Transtorno Depressivo/tratamento farmacológico , Transtorno Depressivo/microbiologia , Método Duplo-Cego , Fadiga , Feminino , Microbioma Gastrointestinal , Humanos , Masculino , Projetos Piloto , Polissonografia , Testes Psicológicos , Autorrelato , Distúrbios do Início e da Manutenção do Sono/complicações , Distúrbios do Início e da Manutenção do Sono/microbiologia , Fases do Sono
6.
Comput Biol Med ; 136: 104762, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34399195

RESUMO

BACKGROUND: Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks. METHOD: Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine. RESULT: In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively. CONCLUSION: Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.


Assuntos
Cataplexia , Narcolepsia , Humanos , Sono , Fases do Sono , Sono REM
7.
Biomed Res Int ; 2021: 5561974, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34350292

RESUMO

Early identification and diagnosis of mild cognitive impairment (MCI) in patients with parkinsonism (PDS) are critical. The aim of this study was to identify biomarkers of MCI in PDS using conventional electroencephalogram (EEG) power spectral analysis and detrended fluctuation analysis (DFA). In this retrospective study, patients with PDS who underwent an overnight polysomnography (PSG) study in our hospital from 2019 to 2020 were enrolled. Patients with PDS assessed by clinical examination and questionnaires were divided into two groups: the PDS with normal cognitive function (PDS-NC) group and the PDS with MCI (PDS-MCI) group. Sleep EEG signals were extracted and purified from the PSG and subjected to a conventional power spectral analysis, as well as detrended fluctuation analysis (DFA) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Forty patients with PDS were enrolled, including 25 with PDS-NC and 15 with PDS-MCI. Results revealed that compared with PDS-NC patients, patients with PDS-MCI had a reduced fast ratio ((alpha + beta)/(delta + theta)) and increased DFA during NREM sleep. DFA during NREM was diagnostic of PDS-MCI, with an area under the receiver operating characteristic curve of 0.753 (95% CI: 0.592-0.914) (p < 0.05). Mild cognitive dysfunction was positively correlated with NREM-DFA (r = 0.426, p = 0.007) and negatively correlated with an NREM-fast ratio (r = -0.524, p = 0.001). This suggested that altered EEG activity during NREM sleep is associated with MCI in patients with PDS. NREM sleep EEG characteristics of the power spectral analysis and DFA correlate to MCI. Slowing of EEG activity during NREM sleep may reflect contribution to the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI.


Assuntos
Disfunção Cognitiva/diagnóstico por imagem , Disfunção Cognitiva/fisiopatologia , Eletroencefalografia , Transtornos Parkinsonianos/diagnóstico por imagem , Transtornos Parkinsonianos/fisiopatologia , Fases do Sono/fisiologia , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Estudos de Casos e Controles , Disfunção Cognitiva/complicações , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Transtornos Parkinsonianos/complicações , Polissonografia , Curva ROC , Vigília/fisiologia
8.
Commun Biol ; 4(1): 854, 2021 07 09.
Artigo em Inglês | MEDLINE | ID: mdl-34244598

RESUMO

Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.


Assuntos
Mapeamento Encefálico/métodos , Encéfalo/fisiologia , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/fisiologia , Sono/fisiologia , Vigília/fisiologia , Algoritmos , Encéfalo/diagnóstico por imagem , Eletroencefalografia/métodos , Humanos , Modelos Neurológicos , Rede Nervosa/diagnóstico por imagem , Neuroimagem/métodos , Fases do Sono/fisiologia
9.
Cortex ; 142: 94-103, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34256198

RESUMO

The brain mechanisms by which we transition from sleep to a conscious state remain largely unknown in humans, partly because of methodological challenges. Here we study a pre-existing dataset of waking up participants originally designed for a study of dreaming (Horikawa, Tamaki, Miyawaki, & Kamitani, 2013) and suggest that suddenly awakening from early sleep stages results from a two-stage process that involves a sequence of cortical and subcortical brain activity. First, subcortical and sensorimotor structures seem to be recruited before most cortical regions, followed by fast, ignition-like whole-brain activation-with frontal regions engaging a little after the rest of the brain. Second, a comparably slower and possibly mirror-reversed stage might take place, with cortical regions activating before subcortical structures and the cerebellum. This pattern of activation points to a key role of subcortical structures for the initiation and maintenance of conscious states.


Assuntos
Imageamento por Ressonância Magnética , Sono REM , Encéfalo/diagnóstico por imagem , Estado de Consciência , Humanos , Sono , Fases do Sono , Vigília
10.
Clin Geriatr Med ; 37(3): 377-386, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34210444

RESUMO

Sleep-related complaints are so common in older adults that it may be difficult to distinguish whether the complaint is a consequence of normal aging or a disease process. The elderly are more likely to have common medical problems that affect sleep, and the most common sleep problems, including sleep apnea and insomnia, are more prevalent in this demographic. This article briefly describes normal sleep in general, the clinical assessment of sleep complaints, and expected changes with aging, with an overview of the epidemiology of insomnia and sleep apnea in this age group.


Assuntos
Envelhecimento , Distúrbios do Início e da Manutenção do Sono , Fases do Sono , Transtornos do Sono-Vigília/etiologia , Sono/fisiologia , Idoso , Humanos , Polissonografia , Síndromes da Apneia do Sono
11.
Clin Geriatr Med ; 37(3): 417-427, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34210447

RESUMO

As in other adults, continuous positive airway pressure treatment for obstructive sleep apnea should be the mainstay of treatment. Benefits include improvements in sleepiness and quality of life, as well as improvements in hypertension control, arrhythmias, cardiovascular risk, and mortality. This article discusses issues in prescribing this treatment, including those related specifically to elderly individuals.


Assuntos
Pressão Positiva Contínua nas Vias Aéreas/métodos , Qualidade de Vida , Apneia Obstrutiva do Sono/terapia , Idoso , Humanos , Fases do Sono/fisiologia , Resultado do Tratamento
12.
Int J Psychophysiol ; 167: 86-93, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34252481

RESUMO

COVID-19 has become a long-term problem, and global pandemic conditions may persist for years. Researchers are providing mounting evidence of relationships between COVID-19 lockdowns and sleep problems. However, few studies have investigated the impact of home isolation on sleep time perception, especially in comparable social isolation situations with similar pressures. Subjective sleep time perception parameters were derived from sleep diaries. Objective parameters were derived from actigraphy. Subjective and objective data were obtained between February 17 and February 27, 2020 from 70 adult participants subject to COVID-19 related lockdown provisions in China. We divided participants into a home stayers (HS) group (subject to full stay-at home orders) and an area-restricted workers (ARW) group (permitted to work at their nearby workplaces). The HS group demonstrated significantly delayed actigraphy-defined sleep onset time compared to self-reported sleep onset time; this effect was absent in the ARW group. Between-group differences in actigraphy-defined sleep onset time and significant between-group differences for actigraphy-defined and self-reported wake-up time were observed. HS group participants also presented significantly delayed actigraphy-defined wake-up time compared with self-reported wake-up time. No significant effect was found on total sleep time perception. Moreover, sleep/wake time misperception were found to be associated with daylight exposure and physical activity levels respectively. To the extent they are generalizable, these results suggest that lockdown restrictions can affect sleep onset and wake-up time perception but not total sleep time perception. Public health policy should consider such effects in the present pandemic situation and in future emergent public health situations.


Assuntos
Actigrafia , COVID-19 , Registros Médicos , Pandemias , Quarentena/psicologia , Autorrelato , Sono , Adulto , China , Controle de Doenças Transmissíveis , Exercício Físico , Feminino , Humanos , Luz , Masculino , Pessoa de Meia-Idade , Distúrbios do Início e da Manutenção do Sono/epidemiologia , Distúrbios do Início e da Manutenção do Sono/etiologia , Fases do Sono , Transtornos do Sono-Vigília , Inquéritos e Questionários
13.
Sensors (Basel) ; 21(13)2021 Jun 23.
Artigo em Inglês | MEDLINE | ID: mdl-34201861

RESUMO

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.


Assuntos
Actigrafia , Fases do Sono , Humanos , Polissonografia , Reprodutibilidade dos Testes , Sono
14.
Artigo em Inglês | MEDLINE | ID: mdl-34288872

RESUMO

Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches have been developed to replace this tedious and expensive task. Although these methods demonstrated better performance than human sleep experts on specific datasets, they remain largely unused in sleep clinics. The main reason is that each sleep clinic uses a specific PSG montage that most automatic approaches cannot handle out-of-the-box. Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics. To address these issues, we introduce RobustSleepNet, a deep learning model for automatic sleep stage classification able to handle arbitrary PSG montages. We trained and evaluated this model in a leave-one-out-dataset fashion on a large corpus of 8 heterogeneous sleep staging datasets to make it robust to demographic changes. When evaluated on an unseen dataset, RobustSleepNet reaches 97% of the F1 of a model explicitly trained on this dataset. Hence, RobustSleepNet unlocks the possibility to perform high-quality out-of-the-box automatic sleep staging with any clinical setup. We further show that finetuning RobustSleepNet, using a part of the unseen dataset, increases the F1 by 2% when compared to a model trained specifically for this dataset. Therefore, finetuning might be used to reach a state-of-the-art level of performance on a specific population.


Assuntos
Eletroencefalografia , Fases do Sono , Humanos , Aprendizado de Máquina , Polissonografia , Sono
15.
Sensors (Basel) ; 21(10)2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-34064694

RESUMO

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.


Assuntos
Aprendizado Profundo , Eletroencefalografia , Humanos , Polissonografia , Sono , Fases do Sono
16.
Sensors (Basel) ; 21(10)2021 May 13.
Artigo em Inglês | MEDLINE | ID: mdl-34068077

RESUMO

Past research has demonstrated differential responses of the brain during sleep in response especially to variations in paralinguistic properties of auditory stimuli, suggesting they can still be processed "offline". However, the nature of the underlying mechanisms remains unclear. Here, we therefore used multivariate pattern analyses to directly test the similarities in brain activity among different sleep stages (non-rapid eye movement stages N1-N3, as well as rapid-eye movement sleep REM, and wake). We varied stimulus salience by manipulating subjective (own vs. unfamiliar name) and paralinguistic (familiar vs. unfamiliar voice) salience in 16 healthy sleepers during an 8-h sleep opportunity. Paralinguistic salience (i.e., familiar vs. unfamiliar voice) was reliably decoded from EEG response patterns during both N2 and N3 sleep. Importantly, the classifiers trained on N2 and N3 data generalized to N3 and N2, respectively, suggesting similar processing mode in these states. Moreover, projecting the classifiers' weights using a forward model revealed similar fronto-central topographical patterns in NREM stages N2 and N3. Finally, we found no generalization from wake to any sleep stage (and vice versa) suggesting that "processing modes" or the overall processing architecture with respect to relevant oscillations and/or networks substantially change from wake to sleep. However, the results point to a single and rather uniform NREM-specific mechanism that is involved in (auditory) salience detection during sleep.


Assuntos
Eletroencefalografia , Vigília , Encéfalo , Sono , Fases do Sono
17.
Clin Neurophysiol ; 132(8): 1757-1769, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34130242

RESUMO

Since the term Stimulus-Induced Rhythmic, Periodic, or Ictal Discharges (SIRPIDs) was introduced into the vocabulary of electrophysiologists/neurologists, there has been an ongoing debate about its significance, as well as its correlation with outcomes. SIRPIDs are frequently seen in patients who are critically ill from various causes. The literature reflects the findings of triphasic morphology, with the generalized periodic discharge (GPD) classification in many patients with SIRPIDs: toxic/metabolic encephalopathies, septic, and hypoxemic/hypercapnic encephalopathies, but also sharp periodic complexes in Creutzfeldt-Jakob disease and advanced Alzheimer's disease. In these settings, GPDs disappear when patients fall asleep and reappear when patients spontaneously wake up, or are awoken by an external stimulus, or sometimes because of a respiratory event, with the possibility of the appearance of GPDs with a cyclic alternating pattern. SIRPIDs may be seen as a transitional pattern between sleep and waking states, corresponding to a postarousal/awakening phenomenon. As SIRPIDs are a transient phenomenon and can usually be recorded repeatedly with each stimulation, the word "Ictal" could be replaced by "Intermittent": Stimulus-Induced Rhythmic or Periodic Intermittent Discharges. However, considering that SIRPIDs may be "potentially ictal" or on an "ictal-interictal continuum" in some situations, the "plus" modifier may be added: SIRPIDs-plus.


Assuntos
Ondas Encefálicas/fisiologia , Encéfalo/fisiopatologia , Síndrome de Creutzfeldt-Jakob/fisiopatologia , Periodicidade , Fases do Sono/fisiologia , Vigília/fisiologia , Encéfalo/diagnóstico por imagem , Síndrome de Creutzfeldt-Jakob/diagnóstico por imagem , Eletroencefalografia/métodos , Humanos
18.
Commun Biol ; 4(1): 722, 2021 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-34117351

RESUMO

Understanding human sleep requires appropriate animal models. Sleep has been extensively studied in rodents, although rodent sleep differs substantially from human sleep. Here we investigate sleep in tree shrews, small diurnal mammals phylogenetically close to primates, and compare it to sleep in rats and humans using electrophysiological recordings from frontal cortex of each species. Tree shrews exhibited consolidated sleep, with a sleep bout duration parameter, τ, uncharacteristically high for a small mammal, and differing substantially from the sleep of rodents that is often punctuated by wakefulness. Two NREM sleep stages were observed in tree shrews: NREM, characterized by high delta waves and spindles, and an intermediate stage (IS-NREM) occurring on NREM to REM transitions and consisting of intermediate delta waves with concomitant theta-alpha activity. While IS-NREM activity was reliable in tree shrews, we could also detect it in human EEG data, on a subset of transitions. Finally, coupling events between sleep spindles and slow waves clustered near the beginning of the sleep period in tree shrews, paralleling humans, whereas they were more evenly distributed in rats. Our results suggest considerable homology of sleep structure between humans and tree shrews despite the large difference in body mass between these species.


Assuntos
Sono/fisiologia , Tupaiidae/fisiologia , Animais , Eletroencefalografia , Feminino , Lobo Frontal/fisiologia , Humanos , Masculino , Ratos , Ratos Long-Evans/fisiologia , Fases do Sono/fisiologia , Sono REM/fisiologia , Adulto Jovem
19.
Neurosci Res ; 172: 26-40, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33965451

RESUMO

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient's sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.


Assuntos
Eletroencefalografia , Máquina de Vetores de Suporte , Algoritmos , Humanos , Análise dos Mínimos Quadrados , Processamento de Sinais Assistido por Computador , Fases do Sono , Análise de Ondaletas
20.
Clin Neurophysiol ; 132(7): 1550-1563, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34034085

RESUMO

OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to include data from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep (von Ellenrieder et al., 2020), and the present work extends our methods to those data. METHODS: Normalized amplitude spectra on semi-logarithmic axes from all four arousal states (wake, N2, N3 and REM) were averaged region-wise and fitted to a multi-component Gaussian distribution. A reduced model comprising five key parameters per brain region was color-coded on to cortical surface models. RESULTS: The lognormal Gaussian mixture model described the iEEG accurately from all brain regions, in all sleep-wake states. There was smooth variation in model parameters as sleep and wake states yielded to each other. Specific observations unrelated to the model were that the primary cortical areas of vision, motor function and audition, in addition to the hippocampus, did not participate in the 'awakening' of the cortex during REM sleep. CONCLUSIONS: Despite the significant differences in the appearance of the time-domain EEG in wakefulness and sleep, the iEEG in all arousal states was successfully described by a parametric spectral model of low dimension. SIGNIFICANCE: Spectral variation in the iEEG is continuous in space (across different cortical regions) and time (stage of circadian cycle), arguing for a 'continuum' hypothesis in the generative processes of sleep and wakefulness in human brain.


Assuntos
Mapeamento Encefálico/métodos , Encéfalo/fisiologia , Eletroencefalografia/métodos , Redes Neurais de Computação , Fases do Sono/fisiologia , Vigília/fisiologia , Bases de Dados Factuais , Humanos , Distribuição Normal
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