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1.
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
2.
Sensors (Basel) ; 21(10)2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-34064946

RESUMO

The acquisition and analysis of EEG signals of dolphins, a highly intelligent creature, has always been a focus of the research of bioelectric signals. Prevailing cable-connected devices cannot be adapted to data acquisition very well when dolphins are in motion. Therefore, this study designs a novel, light-weighted, and portable EEG acquisition device aimed at relatively unrestricted EEG acquisition. An embedded main control board and an acquisition board were designed, and all modules are encapsulated in a 162 × 94 × 60 mm3 waterproof device box, which can be tied to the dolphin's body by a silicon belt. The acquisition device uses customized suction cups with embedded electrodes and adopts a Bluetooth module for wireless communication with the ground station. The sampled signals are written to the memory card on board when the Bluetooth communication is blocked. A limited experiment was designed to verify the effectiveness of the device functionality onshore and underwater. However, more rigorous long-term tests on dolphins in various states with our device are expected in future to further prove its capability and study the movement-related artifacts.


Assuntos
Golfinhos , Animais , Artefatos , Eletrodos , Eletroencefalografia , Movimento
3.
Sensors (Basel) ; 21(10)2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-34065035

RESUMO

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant's head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant's left/right hand side. This identification process is based on "Levenberg-Marquardt" backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.


Assuntos
Eletroencefalografia , Mãos , Algoritmos , Encéfalo , Humanos , Redes Neurais de Computação
4.
BMJ Case Rep ; 14(6)2021 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-34117000

RESUMO

A 3-year-old boy presented with acute onset of prolonged right sided focal seizures with secondary generalisation. The investigation findings were suggestive of a neoplastic process more than an inflammatory process. Decision to perform brain biopsy from the lesion to establish the precise nature of lesion was undertaken.


Assuntos
Epilepsias Parciais , Estado Epiléptico , Pré-Escolar , Eletroencefalografia , Epilepsias Parciais/diagnóstico por imagem , Epilepsias Parciais/etiologia , Humanos , Masculino , Neuroimagem , Convulsões/etiologia
5.
Sensors (Basel) ; 21(11)2021 May 26.
Artigo em Inglês | MEDLINE | ID: mdl-34073602

RESUMO

Brain-computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.


Assuntos
Interfaces Cérebro-Computador , Encéfalo , Eletroencefalografia , Potencial Evocado P300 , Humanos , Smartphone , Interface Usuário-Computador
6.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(5): 609-612, 2021 May.
Artigo em Chinês | MEDLINE | ID: mdl-34112303

RESUMO

OBJECTIVE: To examine whether the combination of quantitative regional apparent diffusion coefficient (ADC) and amplitude-integrated electroencephalogram (aEEG) can predict the outcome of comatose patients with severe traumatic brain injury (sTBI). METHODS: A prospective study was conducted. The patients with coma caused by sTBI [Glasgow coma scale (GCS) < 8] admitted to Suqian First Hospital from January 2016 to June 2019 were enrolled. All patients underwent aEEG examination and magnetic resonance imaging (MRI) scan within 1 week after emergency treatment. The ADC values of 9 regions of interest (frontal gray matter and white matter, parietal gray matter and white matter, temporal gray matter and white matter, caudate nucleus of basal ganglia, lenticular nucleus and thalamus) were measured by head MRI, and the mean ADC values of frontal lobe, parietal lobe, temporal lobe and basal ganglia were calculated respectively. According to the follow-up results after 12 months, the differences of each index between patients with poor prognosis [Glasgow outcome score (GOS) 1-2] and patients with good prognosis (GOS 3-5) were compared; the receiver operating characteristic curve (ROC curve) was drawn to evaluate the predictive ability of aEEG and ADC for the good prognosis of patients with sTBI, and the predictive value of the combination of aEEG and ADC. RESULTS: A total of 52 patients with sTBI were enrolled, with mean age of (36.7±13.9) years old, 35 of whom were male. Within 12 months follow-up, 29 patients had achieved favorable outcomes and 23 patients had unfavorable outcome. There were 21, 17 and 14 patients with aEEG, and grade, respectively, and 19, 10 and 0 patients had good prognosis respectively. ADC values of 9 regions of interest in patients with good prognosis were significantly higher than those in patients with poor prognosis (×10-6 mm2/s: 924±107 vs. 531±87 in frontal gray matter, 804±95 vs. 481±74 in frontal white matter, 831±93 vs. 683±72 in temporal gray matter, 726±87 vs. 654±63 in temporal white matter, 767±79 vs. 690±75 in parietal gray matter, 716±84 vs. 642±62 in parietal white matter, 689±70 vs. 465±68 in caudate nucleus, 723±84 vs. 587±71 in lenticular nucleus, 807±79 vs. 497±67 in thalamus, all P < 0.01). ROC curve analysis showed that the area under ROC curve (AUC) of aEEG for predicting good prognosis of sTBI patients was 0.826, when the cut-off value of aEEG was < 1.5, the sensitivity was 94.7% and the specificity was 72.8%. Among the ADC value prediction abilities in the interested areas, the prediction of ADC value in frontal lobe and basal ganglia area were better than that in sTBI patients. AUC was 0.817 and 0.903 respectively. The best cut-off values were > 726×10-6 mm2/s and > 624×10-6 mm2/s respectively, the sensitivity of predicting prognosis were both 100%, and the specificity was 63.4% and 61.8%. A model combining frontal ADC and basal ganglia ADC with aEEG was 91.0% sensitive and 93.7% specific for favorable outcome of sTBI patients. CONCLUSIONS: Combination of the quantitative measurement of regional ADC and aEEG may be useful for predicting the outcome of the patients with sTBI.


Assuntos
Lesões Encefálicas Traumáticas , Coma , Adulto , Encéfalo , Lesões Encefálicas Traumáticas/complicações , Lesões Encefálicas Traumáticas/diagnóstico por imagem , Coma/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética , Eletroencefalografia , Feminino , Escala de Coma de Glasgow , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Estudos Prospectivos , Adulto Jovem
7.
Zhonghua Er Ke Za Zhi ; 59(6): 506-510, 2021 Jun 02.
Artigo em Chinês | MEDLINE | ID: mdl-34102826

RESUMO

Objective: To summarize the genotype and phenotype of epilepsy in patients with interferon regulatory factor 2 binding protein-like (IRF2BPL) gene variants. Methods: Data of 6 epilepsy patients with IRF2BPL gene variants seen from May 2017 to September 2020 in the Department of Pediatrics of Peking University First Hospital were retrospectively collected. The clinical characteristics and genetic test results were analyzed. Results: A total of 6 patients with IRF2BPL gene variants (1 boy and 5 girls) were identified. The age of seizure onset was from 3.5 to 7.0 months. Epileptic spasms were observed in 6 patients, tonic seizures and tonic-spasms were observed in 1 patient and focal seizure was observed in 1 patient. All 6 patients presented with developmental delay, 5 patients presented with hypotonia, and 2 patients presented with dysphagia. Microcephaly,nystagmus,chorea and athetosis were observed in 1 patient. The electroencephalography (EEG) showed slow background activity in 2 patients. Hypsarrhythmia was observed in all 6 patients. Focal epileptic discharges were observed in 2 patients. Epileptic spasms were monitored in all 6 patients. Focal seizure and tonic-spasm were monitored in 2 patients respectively. The brain magnetic resonance imaging (MRI) showed cerebral atrophy and dysplasia of the corpus callosum in 1 patient, delayed myelination in 2 patients and normal in 3 patients. Two patients had missense variants c.1280C>T/p.L474F and c.1420C>T/p.S427L, 3 patients had frameshift variants c.232delG/p.V78Sfs*73, c.244del/p.A82Pfs*70 and c.283-308del/p.Ala95Thrfs*29, 1 patient had non-frameshift deletion variant c.1453-c.1455delTTC/p.F485del, and all of the 6 cases had de novo variants. All patients were diagnosed with infantile spasms. The last follow-up age ranged from 1 year to 3.8 years. Four patients achieved seizure-free and 2 patients still had frequent seizures after the treatment with antiepileptic drugs (adrenocorticotropic hormone, topiramate, and vigabatrin). Conclusions: IRF2BPL gene variants are mainly de novo. The age of seizure onset is mainly in infancy, and epilepsy and developmental delay are the main clinical manifestations. Infantile spasm is the main phenotype, some patients have hypotonia and dysphagia. Cerebral atrophy can be observed in a few patients.


Assuntos
Epilepsia , Espasmos Infantis , Proteínas de Transporte , Criança , Eletroencefalografia , Epilepsia/tratamento farmacológico , Epilepsia/genética , Feminino , Humanos , Lactente , Masculino , Proteínas Nucleares , Estudos Retrospectivos , Convulsões , Espasmos Infantis/genética
8.
Sensors (Basel) ; 21(11)2021 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-34067397

RESUMO

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.


Assuntos
Substâncias para a Guerra Química , Eletroencefalografia , Animais , Eletrocardiografia , Cobaias , Aprendizado de Máquina , Respiração
9.
Int J Mol Sci ; 22(10)2021 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-34069993

RESUMO

Rett syndrome (RTT) is a rare neurodevelopmental disorder that is usually caused by mutations of the MECP2 gene. Patients with RTT suffer from severe deficits in motor, perceptual and cognitive domains. Electroencephalogram (EEG) has provided useful information to clinicians and scientists, from the very first descriptions of RTT, and yet no reliable neurophysiological biomarkers related to the pathophysiology of the disorder or symptom severity have been identified to date. To identify consistently observed and potentially informative EEG characteristics of RTT pathophysiology, and ascertain areas most worthy of further systematic investigation, here we review the literature for EEG abnormalities reported in patients with RTT and in its disease models. While pointing to some promising potential EEG biomarkers of RTT, our review identify areas of need to realize the potential of EEG including (1) quantitative investigation of promising clinical-EEG observations in RTT, e.g., shift of mu rhythm frequency and EEG during sleep; (2) closer alignment of approaches between patients with RTT and its animal models to strengthen the translational significance of the work (e.g., EEG measurements and behavioral states); (3) establishment of large-scale consortium research, to provide adequate Ns to investigate age and genotype effects.


Assuntos
Eletroencefalografia , Síndrome de Rett/diagnóstico , Síndrome de Rett/fisiopatologia , Animais , Biomarcadores , Modelos Animais de Doenças , Progressão da Doença , Fenômenos Eletrofisiológicos , Feminino , Regulação da Expressão Gênica , Humanos , Masculino , Proteína 2 de Ligação a Metil-CpG/deficiência , Proteína 2 de Ligação a Metil-CpG/genética , Proteína 2 de Ligação a Metil-CpG/fisiologia , Camundongos , Mutação , Fenótipo , Ratos , Síndrome de Rett/genética , Pesquisa Médica Translacional
10.
Sensors (Basel) ; 21(11)2021 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-34070732

RESUMO

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.


Assuntos
Eletroencefalografia , Vigília
11.
Sensors (Basel) ; 21(11)2021 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-34071124

RESUMO

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user's needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.


Assuntos
Mapeamento Encefálico , Eletroencefalografia , Algoritmos , Encéfalo , Simulação por Computador
12.
Sensors (Basel) ; 21(11)2021 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-34073915

RESUMO

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student's t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model.


Assuntos
Reanimação Cardiopulmonar , Parada Cardíaca , Animais , Eletroencefalografia , Parada Cardíaca/terapia , Hemodinâmica , Humanos , Suínos , Fibrilação Ventricular
13.
Sensors (Basel) ; 21(9)2021 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-34066595

RESUMO

EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper dataset partitioning and the resulting improper training, validation, and testing of a cross-participant model leads to overestimated model accuracy. We demonstrate this mathematically, and empirically, using five publicly available datasets. To build the cross-participant models for these datasets, we replicate published results and demonstrate how the model accuracies are significantly reduced when proper EEG cross-participant model guidelines are followed. Our empirical results show that by not following these guidelines, error rates of cross-participant models can be underestimated between 35% and 3900%. This misrepresentation of model performance for the general population potentially slows scientific progress toward truly high-performing classification models.


Assuntos
Eletroencefalografia , Individualidade , Humanos
14.
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
15.
Sensors (Basel) ; 21(10)2021 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-34068895

RESUMO

Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, sadness, anger, etc.) and have not attempted to predict exact values for valence and arousal, which would provide a wider range of emotional states. This paper describes our proposed model for predicting the exact values of valence and arousal in a subject-independent scenario. To create it, we studied the best features, brain waves, and machine learning models that are currently in use for emotion classification. This systematic analysis revealed that the best prediction model uses a KNN regressor (K = 1) with Manhattan distance, features from the alpha, beta and gamma bands, and the differential asymmetry from the alpha band. Results, using the DEAP, AMIGOS and DREAMER datasets, show that our model can predict valence and arousal values with a low error (MAE < 0.06, RMSE < 0.16) and a strong correlation between predicted and expected values (PCC > 0.80), and can identify four emotional classes with an accuracy of 84.4%. The findings of this work show that the features, brain waves and machine learning models, typically used in emotion classification tasks, can be used in more challenging situations, such as the prediction of exact values for valence and arousal.


Assuntos
Nível de Alerta , Ondas Encefálicas , Eletroencefalografia , Emoções , Aprendizado de Máquina
16.
Sensors (Basel) ; 21(10)2021 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-34069027

RESUMO

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.


Assuntos
Atenção , Eletroencefalografia , Aprendizado de Máquina
17.
Biosensors (Basel) ; 11(5)2021 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-34069456

RESUMO

Classification performances for some classes of electrocardiographic (ECG) and electroencephalographic (EEG) signals processed to dimensionality reduction with different degrees are investigated. Results got with various classification methods are given and discussed. So far we investigated three techniques for reducing dimensionality: Laplacian eigenmaps (LE), locality preserving projections (LPP) and compressed sensing (CS). The first two methods are related to manifold learning while the third addresses signal acquisition and reconstruction from random projections under the supposition of signal sparsity. Our aim is to evaluate the benefits and drawbacks of various methods and to find to what extent they can be considered remarkable. The assessment of the effect of dimensionality decrease was made by considering the classification rates for the processed biosignals in the new spaces. Besides, the classification accuracies of the initial input data were evaluated with respect to the corresponding accuracies in the new spaces using different classifiers.


Assuntos
Eletrocardiografia , Eletroencefalografia , Algoritmos , Humanos , Reconhecimento Automatizado de Padrão
18.
Zh Nevrol Psikhiatr Im S S Korsakova ; 121(4. Vyp. 2): 6-13, 2021.
Artigo em Russo | MEDLINE | ID: mdl-34078853

RESUMO

OBJECTIVE: To develop of a chronic sleep restriction model in rats by repeated sleep deprivation using an orbital shaker and to determine whether this model leads to disturbances in sleep homeostatic mechanisms. MATERIAL AND METHODS: Male Wistar rats (7-8 months old) underwent sleep restriction for five consecutive days: 3 h of sleep deprivation and 1 h of sleep opportunity repeating throughout each day. Polysomnograms were recorded telemetrically throughout the day before sleep restriction (baseline), on the 1st, 3rd, 5th day of sleep restriction and 2 days after the end of sleep restriction (recovery period). RESULTS: During the period of sleep restriction, the total amount of slow-wave sleep (SWS) and rapid eye movement (REM) sleep decreased by 61% and 55%, respectively, compared to baseline. On the first day of recovery, amount of SWS increased mainly in the dark (active) phase of the day, while REM sleep increased in both light and dark phases; there was no marked rebound of daily SWS amount, while REM sleep increased by 30% from baseline. On the first day of recovery, an elevation of EEG beta and sigma power in sleep states was observed mainly in the light phase of the day. The loss of deep SWS throughout the sleep restriction period increased from 50% on 1st day to 75% on 5th day. The level of deep SWS remained below the baseline by 15-20% on the two subsequent days of recovery. The findings suggest that homeostatic mechanisms of SWS are persistently impaired after 5-day chronic sleep restriction. Besides, a decline of wakefulness accompanied by an increase of SWS in the active phase of the recovery period indicates a disruption in circadian rhythm. CONCLUSION: The proposed model leads to the disruption of sleep homeostatic mechanisms, which, in turn, impede compensation of SWS loss caused by chronic insufficient sleep.


Assuntos
Eletroencefalografia , Sono , Animais , Masculino , Ratos , Ratos Wistar , Privação do Sono , Sono REM , Vigília
19.
Zh Nevrol Psikhiatr Im S S Korsakova ; 121(4. Vyp. 2): 14-18, 2021.
Artigo em Russo | MEDLINE | ID: mdl-34078854

RESUMO

The authors discuss modern ideas about the neurophysiological mechanisms of awakening from sleep and the results of own EEG studies of the spatio-temporal dynamics of the activity of the cerebral hemispheres using the own experimental model for studying consciousness in the sleep-wake paradigm. This model is based on continuous execution of a monotonous psychomotor test performed lying down with eyes closed and allows observing several short-term sleep episodes during a 1-hour experiment, followed by spontaneous awakening and restoration of the psychomotor test. A necessary condition for the restoration of activity during spontaneous awakening is the emergence of the EEG alpha rhythm, the parameters of which determine the effectiveness of the restoration of the psychomotor test and, accordingly, the achievement of a certain level of consciousness, and therefore can be considered as a neurophysiological correlate of consciousness activation upon awakening. The considered experimental model of consciousness can be useful for analyzing the neurophysiological mechanisms of consciousness activation in patients with chronic impairments of consciousness and for searching for effective methods for the rehabilitation of such patients.


Assuntos
Estado de Consciência , Vigília , Ritmo alfa , Eletroencefalografia , Humanos , Neurofisiologia , Sono
20.
BMC Bioinformatics ; 22(Suppl 6): 195, 2021 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-34078274

RESUMO

BACKGROUND: Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain-computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP). RESULTS: The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively. CONCLUSIONS: Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.


Assuntos
Ondas Encefálicas , Interfaces Cérebro-Computador , Algoritmos , Encéfalo , Eletroencefalografia , Imaginação , Processamento de Sinais Assistido por Computador
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