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1.
Biomed Instrum Technol ; 54(5): 346-351, 2020 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-33049766

RESUMO

Electroencephalography (EEG) is a sensitive and weak biosignal that varies from person to person. It is easily affected by noise and artifacts. Hence, maintaining the signal integrity to design an EEG acquisition system is crucial. This article proposes an analog design for acquiring EEG signals. The proposed design consists of eight blocks: (1) a radio-frequency interference filter and electro-static discharge protection, (2) a preamplifier and second-order high-pass filter with feedback topology and an unblocking mechanism, (3) a driven right leg circuit, (4) two-stage main and variable amplifiers, (5) an eight-order anti-aliasing filter, (6) a six-order 50-Hz notch filter (optional), (7) an opto-isolator circuit, and (8) an isolated power supply. The maximum gain of the design is approximately 94 dB, and its bandwidth ranges from approximately 0.18 to 120 Hz. The depth of the 50-Hz notch filter is -35 dB. Using this filter is optional because it causes EEG integrity problems in frequencies ranging from 40 to 60 Hz.


Assuntos
Amplificadores Eletrônicos , Eletroencefalografia , Artefatos , Fontes de Energia Elétrica , Desenho de Equipamento/métodos
2.
Artigo em Russo | MEDLINE | ID: mdl-33081451

RESUMO

OBJECTIVE: To search for electroencephalographic markers of alexithymia in patients with moderate depression. MATERIAL AND METHODS: Sixty-four right-handed inpatients (20 men and 44 women, mean age 29.3+10.7 years), were studied. The level of alexithymia was assessed by the Russian version of the Toronto Alexithymia Scale (TAS-20-R). Patients were stratified into 4 groups by TAS-20-R scores. Two main groups of 22 patients each included people with high (>60 scores) and low (20-51 scores) levels of alexithymia. Control groups included 10 patients with 60-85 scores and 10 patients with 52-54 scores. Current methods of electroencephalographic analysis were used. RESULTS: Alexithymia in patients with moderate depression is characterized by a restructuring of the integrative activity of the brain detected by electroencephalography at rest. Patients with high alexithymia differ from those with low alexithymia by (1) lower values of the real part of the coherence between the frontal and anterior temporal leads of the left hemisphere in the band 28-30 Hz; (2) lower values of the imaginary part of coherence in the band 11-12 Hz between the posterior temporal and parietal as well as the posterior temporal and occipital cortical zones of the right hemisphere; (3) higher rates of the real part of the coherence between the right frontal and central leads in the frequency ranges 12-14 and 6-7 Hz; (4) large values of the imaginary part of the coherence between the left parietal and right posterior temporal cortex in the band 24-26 Hz; (5) higher values of the square of the coherence modulus between the left frontal and anterior temporal cortical zones in the band 17-18 Hz. CONCLUSION: Large cortical representations with involvement of theta, alpha, beta-1 and beta-2 rhythms can contribute to the pathogenesis of alexithymia.


Assuntos
Sintomas Afetivos , Depressão , Adolescente , Adulto , Sintomas Afetivos/diagnóstico , Encéfalo , Eletroencefalografia , Feminino , Humanos , Masculino , Federação Russa , Adulto Jovem
3.
Zh Nevrol Psikhiatr Im S S Korsakova ; 120(9. Vyp. 2): 22-25, 2020.
Artigo em Russo | MEDLINE | ID: mdl-33076641

RESUMO

OBJECTIVE: To evaluate an influence of intracerebral L-lactate concentration on sleep-wake cycle. MATERIAL AND METHODS: Twenty adult male white rats preliminary implanted (under general anesthesia) with the electrodes for neocortical EEG and a single cannula to a lateral ventricle were used as subjects. A 5 µl bolus of either saline or a solution of sodium L- or D-lactate (0.1 mg, 0.2 M, Sigma-Aldrich) was injected through the cannula and followed by a 6-hr recording. RESULTS AND CONCLUSION: Administration of L-lactate does not influence sleep-wake cycle of experimental animals. At the same time, its artificial optical analog D-lactate induces the significant (as compared to the control) decrease in wake (34.8% to 26.5%) and increase in slow wave sleep (57.4% to 69.2%). It has been suggested that D-lactate may be the antagonist of one or several L-lactate receptors.


Assuntos
Ácido Láctico , Vigília , Animais , Eletroencefalografia , Masculino , Ratos , Sono
4.
Zh Nevrol Psikhiatr Im S S Korsakova ; 120(9. Vyp. 2): 91-97, 2020.
Artigo em Russo | MEDLINE | ID: mdl-33076652

RESUMO

The phenomenon of local sleep, a concept that has come into somnology relatively recently, has been attracting more and more attention of researchers. Under this name, two groups of phenomena are considered. The first is the appearance in different parts of the cerebral cortex of different EEG patterns during general sleep. The second is the disconnection of certain cortical areas from the processing of extero- or proprioceptive signals and their transition to spiking and slow wave electrical activity, which is typical for sleep, while awake. The authors believe that it is the second phenomenon that can be called real local sleep. The appearance of local cortical sleep is inseparably linked with the occurrence of local wakefulness. It can be expected that the occurrence of local sleep will have detrimental consequences for behavior in urgent and complicated situations, while local wakefulness can adversely affect the visceral health of the body. A possible way of early detection of the local sleep development is proposed. In conclusion, some methodological problems on the way of electrophysiological studies of the local aspects of sleep and wakefulness are considered.


Assuntos
Eletroencefalografia , Sono , Córtex Cerebral , Vigília
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3889-3892, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018850

RESUMO

Speech imagery based brain-computer interface (BCI) has the potential to assist patients with communication disorders to recover their speech communication abilities. Mandarin is a tonal language, and its tones play an important role in language perception and semantic understanding. This work studied the electroencephalogram (EEG) based classification of Mandarin tones based on speech imagery, and also compared the classification performance of speech imagery based BCIs at two test conditions with visual-only and combined audio-visual stimuli, respectively. Participants imagined 4 Mandarin tones at each condition. Common spatial patterns were applied to extract feature vectors, and support vector machine was used to classify different Mandarin tones from EEG data. Experimental results showed that the tonal articulation imagination task achieved a higher classification accuracy at the combined audio-visual condition (i.e., 80.1%) than at the visual-only condition (i.e., 67.7%). The results in this work supported that Mandarin tone information could be decoded from EEG data recorded in a speech imagery task, particularly under the combined audio-visual condition.


Assuntos
Interfaces Cérebro-Computador , Fala , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Imaginação
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3901-3904, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018853

RESUMO

The major challenge in upper limbs neuroprosthetic improvement is the implementation of effective sensory feedback. Transcutaneous electrical nerve stimulation (TENS) of the median and ulnar nerves confirmed, with electroencephalographic (EEG) recordings, the presence of appropriate responses in relevant cortical areas with induced sensation successfully located in the innervation regions of each nerve. The characterization of these elicited responses could be used to recreate precise somatotopic feedback from hand protheses. Using TENS and EEG, the purpose of this study was to detect distinctions in time-frequency cortical dynamics and connectivity occurring after stimulation of hand nerves. Region of interest (ROI) were selected according to topographical distributions and Somatosensory Evoked Potentials (SEP) localization and were named Contralateral Parietal (Cont P), Central Frontal (Cent F) and Superior Parietal (Sup P). The analysis of cortical oscillations showed spectral inflections in theta [4-7 Hz] and alpha [7.5-12.5 Hz] band which occurred at 60 ms in Cont P and 300 ms in Sup P and prominent for the ulnar condition over the median one. The beta band decrease [16-30 Hz] which occurred in the same ROIs was especially significant after ulnar stimulation too. Effective connectivity measures did not differ significantly across conditions but exhibited some slight difference in the alpha-band causal flow coming from Cent F in direction to Cont P and Sup P. Although pending completion of multiple-subjects study, these results already suggest magnitude differences in somatosensory spectral fluctuations and sensorimotor interactions flows.


Assuntos
Estimulação Elétrica Nervosa Transcutânea , Eletroencefalografia , Potenciais Somatossensoriais Evocados , Mãos , Nervo Ulnar
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3973-3976, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018870

RESUMO

Low electrode-skin impedance can be achieved if the interface has an electrolytic medium that allows the movement of ions across the interface. Maintaining good physical contact of the sensor with the skin is imperative. We propose a novel hydrophilic conductive sponge interface that encapsulates both of these fundamental concepts into an effective physical realization. Our implementation uses a hydrophilic polyurethane prepolymer doped with conductive carbon nanofibers and cured to form a flexible sponge material that conforms to uneven surfaces, for instance, on parts of the scalp with hair. Our results show that our sponges are able to stay in a hydrated state with a low electrode-skin impedance of around 5kΩ for more than 20 hours. The novelty in our conductive sponges also lies in their versatility: the carbon nanofibers make the electrode effective even when the electrode dries up. The sensors remain conductive with a skin impedance on the order of 20kΩ when dry, which is substantially lower than typical impedance of dry electrodes, and are able to extract alpha wave EEG activity in both wet and dry conditions.


Assuntos
Eletroencefalografia , Condutividade Elétrica , Impedância Elétrica , Eletrodos , Desenho de Equipamento
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4055-4058, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018889

RESUMO

Recent mobile and wearable electroencephalogram (EEG)-sensing technologies have been demonstrated to be effective for measuring rapid changes of spatio-spectral EEG correlates of brain and cognitive functions of interest with more ecologically natural settings. However, commercial EEG products are available commonly with a fixed headset in terms of the number of electrodes and their locations to the scalp practically constrains their generalizability for different demands of EEG and brain-computer interface (BCI) study. While most progress focused on innovation of sensing hardware and conductive electrodes, less effort has been done to renovate mechanical structures of an EEG headset. Recently, an electrode-holder assembly infrastructure was designed to be capable of unlimitedly (re)assembling a desired n-channel electrode headset through a set of primary elements (i.e., LEGO-like headset). The present work empirically demonstrated one of its advantage regarding coordinating the homogeneous or heterogeneous sensors covering the target regions of the brain. Towards this objective, an 8-channel LEGO headset was assembled to conduct a simultaneous event-related potential (ERP) recording of the wet- and dry-electrode EEG systems and testify their signal quality during standing still versus treadmill walking. The results showed that both systems returned a comparable P300 signal-to-noise ratio (SNR) for standing, yet the dry system was more susceptible to the movement artifacts during slow walking. The LEGO headset infrastructure facilitates a desired benchmark study, e.g., comparing the signal quality of different electrodes on non-stationary subjects conducted in this work, or a specific EEG and BCI application.


Assuntos
Interfaces Cérebro-Computador , Caminhada , Eletrodos , Eletroencefalografia , Potenciais Evocados
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4226-4228, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018929

RESUMO

The purpose of this paper is to develop an inexpensive, wearable, and portable monitoring system with wireless capabilities for signal acquisition of the user's surrounding soundscape and electroencephalography (EEG). The end-goal of this device is to monitor high-risk populations that are developing into earlier stages of Alzheimer's Disease (AD). Currently, the development of such device is still within preliminary phase and has only been tested in healthy individuals. Future applications of our monitoring system may be used as a non-invasive and inexpensive diagnostic tool for early detection of AD, potentially paving a new platform for therapeutic intervention. The system consists of low-weight bearing components, including an analog front-end and a single-board computer. The analog front-end contains three independent EEG, reference, bias, and auditory recording channels. The single-board computer timestamps and encrypts the incoming channels prior to local or "cloud" storage. Cloud storage provides ease-of-access and offline data analysis without the need to physically extract the data from the monitoring system. A portable/rechargeable battery provides power to the entire monitoring system for over 4 hours of operation. A graphical user-interface (GUI) was developed for secured remote access to data, parameter settings, and system configurations. The performance of the system was tested by measuring the frequency following response (FFR) in the captured EEG signals with respect to periodic auditory stimuli.


Assuntos
Doença de Alzheimer , Doença de Alzheimer/diagnóstico , Eletrocardiografia , Eletroencefalografia , Desenho de Equipamento , Humanos , Monitorização Fisiológica
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4248-4251, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018934

RESUMO

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.


Assuntos
Epilepsia , Dispositivos Eletrônicos Vestíveis , Eletroencefalografia , Epilepsia/diagnóstico , Humanos , Qualidade de Vida , Convulsões/diagnóstico
11.
BMJ Case Rep ; 13(10)2020 Oct 04.
Artigo em Inglês | MEDLINE | ID: mdl-33012721

RESUMO

We present a case of non-convulsive status epilepticus in a 57-year-old woman with a schizoaffective disorder, without an antecedent seizure history, with two possible aetiologies including SARS-CoV-2 infection and clozapine uptitration. We discuss the presentation, investigations, differential diagnosis and management. In particular, we focus on the electroencephalogram (EEG) findings seen in this case and the electroclinical response to antiepileptic medication. We review the literature and discuss the relevance of this case to the SARS-CoV-2 global pandemic. We emphasise the importance of considering possible neurological manifestations of SARS-CoV-2 infection and highlight seizure disorder as one of the possible presentations. In addition, we discuss the possible effects of clozapine on the electroclinical presentation by way of possible seizure induction as well as discuss the possible EEG changes and we highlight that this needs to be kept in mind especially during rapid titration.


Assuntos
Antipsicóticos/efeitos adversos , Betacoronavirus , Clozapina/efeitos adversos , Infecções por Coronavirus/complicações , Pneumonia Viral/complicações , Transtornos Psicóticos/tratamento farmacológico , Estado Epiléptico/etiologia , Clozapina/uso terapêutico , Diagnóstico Diferencial , Eletroencefalografia/métodos , Feminino , Humanos , Pessoa de Meia-Idade , Pandemias , Estado Epiléptico/fisiopatologia
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 447-450, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018024

RESUMO

The degradation of the subject-independent classification on a brain-computer interface is a challenging issue. One method mostly taken to overcome this problem is by collecting as many subjects as possible and then training the system across all subjects. This article introduces streaming online learning called autonomous deep learning (ADL) to classify five individual fingers based on electroencephalography (EEG) signals to overcome the issue above. ADL is a deep learning architecture that can construct its structure by itself through streaming learning and adapt its structure to the changes occurring in the input. In this article, the input of ADL is a common spatial pattern (CSP) extracted from the EEG signal of healthy subjects. The experimental results on the subject-dependence classification across four subjects using 5fold cross-validation show that that ADL achieved the classification accuracy of around 77%. This performance was excellent compared to a random forest (RF) and a convolutional neural network (CNN). They achieved accuracies of about 53% and 72%, respectively. On the subject-independent classification, ADL outperforms CNN by resulting stable accuracies for both training and testing, different from CNN that experience accuracy degradation to approximately 50%. These results imply that ADL is a promising machine learning in dealing with the issue in the subject-independent classification.


Assuntos
Interfaces Cérebro-Computador , Aprendizado Profundo , Eletroencefalografia , Humanos , Movimento , Redes Neurais de Computação
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 506-509, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018038

RESUMO

We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.


Assuntos
Eletroencefalografia , Imaginação , Encéfalo/diagnóstico por imagem , Humanos , Imagens, Psicoterapia , Movimento
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 514-518, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018040

RESUMO

Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a timefrequency analysis are performed for each experiment trial. Then, the optimal EEG signals for every experiment trials are selected based on the signal energy for CSP feature extraction. In the end, the extracted features are classified by three classifiers, linear discriminant analysis (LDA), naïve Bayes (NVB), and support vector machine (SVM), in parallel for classification accuracy comparison.The experiment results show the proposed algorithm average computation time is 37.22% less than the FBCSP (1st winner in the BCI Competition IV) and 4.98% longer than the conventional CSP method. For the classification rate, the proposed algorithm kappa value achieved 2nd highest compared with the top 3 winners in BCI Competition IV.


Assuntos
Interfaces Cérebro-Computador , Teorema de Bayes , Eletroencefalografia , Humanos , Imaginação , Processamento de Sinais Assistido por Computador
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 519-522, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018041

RESUMO

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.


Assuntos
Interfaces Cérebro-Computador , Algoritmos , Eletroencefalografia , Humanos , Imagens, Psicoterapia , Imaginação
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 565-568, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018052

RESUMO

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


Assuntos
Eletroencefalografia , Sono , Humanos , Latência do Sono
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 625-628, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018065

RESUMO

In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use marginal Hilbert spectrum (MHS) to depict time-frequency domain features of five sleep stages of 30-second (30s) EEG epochs. Second, the extracted MHSs features are input to a convolutional neural network (CNN) as multi-channel sequences for the sleep stage classification task. Third, a focal loss function is introduced into the CNN classifier to alleviate the classes imbalance problem of sleep data. Experimental results show that the proposed method can obtain an overall accuracy of 86.14% on the public Sleep-EDF dataset, which is competitive and worth exploring among a series of deep learning methods for the automatic sleep stage classification task.


Assuntos
Redes Neurais de Computação , Fases do Sono , Eletroencefalografia , Humanos , Sono
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 641-644, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018069

RESUMO

Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the existing models, however, have been overengineered to consist of many layers or have introduced additional steps in the processing pipeline, such as converting signals to spectrogram-based images. They require to be trained on a large dataset to prevent the overfitting problem (but most of the sleep datasets contain a limited amount of class-imbalanced data) and are difficult to be applied (as there are many hyperparameters to be configured in the pipeline). In this paper, we propose an efficient deep learning model, named TinySleepNet, and a novel technique to effectively train the model end-to-end for automatic sleep stage scoring based on raw single-channel EEG. Our model consists of a less number of model parameters to be trained compared to the existing ones, requiring a less amount of training data and computational resources. Our training technique incorporates data augmentation that can make our model be more robust the shift along the time axis, and can prevent the model from remembering the sequence of sleep stages. We evaluated our model on seven public sleep datasets that have different characteristics in terms of scoring criteria and recording channels and environments. The results show that, with the same model architecture and the training parameters, our method achieves a similar (or better) performance compared to the state-of-the-art methods on all datasets. This demonstrates that our method can generalize well to the largest number of different datasets.


Assuntos
Aprendizado Profundo , Eletroencefalografia , Humanos , Rememoração Mental , Sono , Fases do Sono
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 645-648, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018070

RESUMO

When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring. We compare this estimate to the actual quality, calculated using a manual scoring of a concurrent polysomnography recording. We find that it is generally possible to estimate the quality of a sleep scoring, but with some uncertainty ('root mean squared error' between estimated and true Cohen's kappa is 0.078). We expect that this method could be useful in situations with many scored nights from the same subject, where an overall picture of scoring quality is needed, but where uncertainty on single nights is less of an issue.


Assuntos
Eletroencefalografia , Sono , Algoritmos , Humanos , Polissonografia , Reprodutibilidade dos Testes
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1007-1010, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018155

RESUMO

Muscle activation during sleep is an important biomarker in the diagnosis of several sleep disorders and neurodegenerative diseases. Muscle activity is typically assessed manually based on the EMG channels from polysomnography recordings. Ear-EEG provides a mobile and comfortable alternative for sleep assessment. In this study, ear-EEG was used to automatically detect muscle activities during sleep. The study was based on a dataset comprising four full night recordings from 20 healthy subjects with concurrent polysomnography and ear-EEG. A binary label, active or relax, extracted from the chin EMG was assigned to selected 30 s epoch of the sleep recordings in order to train a classifier to predict muscle activation. We found that the ear-EEG based classifier detected muscle activity with an accuracy of 88% and a Cohen's kappa value of 0.71 relative to the labels derived from the chin EMG channels. The analysis also showed a significant difference in the distribution of muscle activity between REM and non-REM sleep.


Assuntos
Fenômenos Fisiológicos Musculoesqueléticos , Fases do Sono , Eletroencefalografia , Humanos , Polissonografia , Sono
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