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1.
Artigo em Inglês | MEDLINE | ID: mdl-38848223

RESUMO

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.


Assuntos
Algoritmos , Aprendizado Profundo , Eletroencefalografia , Eletroculografia , Redes Neurais de Computação , Fases do Sono , Humanos , Eletroencefalografia/métodos , Fases do Sono/fisiologia , Eletroculografia/métodos , Masculino , Feminino , Adulto , Polissonografia/métodos , Processamento de Sinais Assistido por Computador , Adulto Jovem
2.
Talanta ; 275: 126180, 2024 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-38703480

RESUMO

Organic Electrochemical Transistors (OECTs) are integral in detecting human bioelectric signals, attributing their significance to distinct electrochemical properties, the utilization of soft materials, compact dimensions, and pronounced biocompatibility. This review traverses the technological evolution of OECT, highlighting its profound impact on non-invasive detection methodologies within the biomedicalfield. Four sensor types rooted in OECT technology were introduced: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Electrooculography (EOG), which hold promise for integration into wearable detection systems. The fundamental detection principles, material compositions, and functional attributes of these sensors are examined. Additionally, the performance metrics and delineates viable optimization strategies for assorted physiological electrical detection sensors are discussed. The overarching goal of this review is to foster deeper insights into the generation, propagation, and modulation of electrophysiological signals, thereby advancing the application and development of OECT in medical sciences.


Assuntos
Transistores Eletrônicos , Humanos , Eletromiografia/métodos , Eletrocardiografia/métodos , Técnicas Eletroquímicas/métodos , Eletroculografia/métodos , Eletroencefalografia
3.
BMC Med Inform Decis Mak ; 24(1): 119, 2024 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-38711099

RESUMO

The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.


Assuntos
Eletroencefalografia , Eletromiografia , Eletroculografia , Aprendizado de Máquina , Polissonografia , Fases do Sono , Humanos , Fases do Sono/fisiologia , Adulto , Masculino , Feminino , Processamento de Sinais Assistido por Computador
4.
PLoS One ; 19(5): e0303565, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38781127

RESUMO

In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user's right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject's selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.


Assuntos
Estimulação Acústica , Atenção , Interfaces Cérebro-Computador , Eletroencefalografia , Humanos , Masculino , Feminino , Eletroencefalografia/métodos , Adulto , Atenção/fisiologia , Estimulação Acústica/métodos , Percepção Auditiva/fisiologia , Adulto Jovem , Potenciais Evocados P300/fisiologia , Eletroculografia/métodos
5.
Biosensors (Basel) ; 14(4)2024 Apr 11.
Artigo em Inglês | MEDLINE | ID: mdl-38667181

RESUMO

Carbon nanotube (CNT)-based nanocomposites have found applications in making sensors for various types of physiological sensing. However, the sensors' fabrication process is usually complex, multistep, and requires longtime mixing and hazardous solvents that can be harmful to the environment. Here, we report a flexible dry silver (Ag)/CNT/polydimethylsiloxane (PDMS) nanocomposite-based sensor made by a solvent-free, low-temperature, time-effective, and simple approach for electrophysiological recording. By mechanical compression and thermal treatment of Ag/CNT, a connected conductive network of the fillers was formed, after which the PDMS was added as a polymer matrix. The CNTs make a continuous network for electrons transport, endowing the nanocomposite with high electrical conductivity, mechanical strength, and durability. This process is solvent-free and does not require a high temperature or complex mixing procedure. The sensor shows high flexibility and good conductivity. High-quality electroencephalography (EEG) and electrooculography (EOG) were performed using fabricated dry sensors. Our results show that the Ag/CNT/PDMS sensor has comparable skin-sensor interface impedance with commercial Ag/AgCl-coated dry electrodes, better performance for noninvasive electrophysiological signal recording, and a higher signal-to-noise ratio (SNR) even after 8 months of storage. The SNR of electrophysiological signal recording was measured to be 26.83 dB for our developed sensors versus 25.23 dB for commercial Ag/AgCl-coated dry electrodes. Our process of compress-heating the functional fillers provides a universal approach to fabricate various types of nanocomposites with different nanofillers and desired electrical and mechanical properties.


Assuntos
Dimetilpolisiloxanos , Nanocompostos , Nanotubos de Carbono , Prata , Nanocompostos/química , Nanotubos de Carbono/química , Prata/química , Dimetilpolisiloxanos/química , Eletroencefalografia , Condutividade Elétrica , Técnicas Biossensoriais , Humanos , Eletroculografia , Eletrodos , Razão Sinal-Ruído
6.
Artigo em Inglês | MEDLINE | ID: mdl-38635384

RESUMO

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.


Assuntos
Algoritmos , Eletroencefalografia , Eletroculografia , Polissonografia , Fases do Sono , Humanos , Eletroencefalografia/métodos , Fases do Sono/fisiologia , Polissonografia/métodos , Eletroculografia/métodos , Masculino , Adulto , Feminino , Adulto Jovem
7.
Sleep Med ; 117: 201-208, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38583319

RESUMO

OBJECTIVE: The current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages. METHODS: Type II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages. RESULTS: The results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 µv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages. CONCLUSIONS: Technical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.


Assuntos
Fases do Sono , Sono , Humanos , Eletroencefalografia/métodos , Polissonografia , Eletroculografia
8.
Physiol Meas ; 45(5)2024 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-38653318

RESUMO

Objective.Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.Approach.We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.Main results.For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.Significance.The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.


Assuntos
Eletroculografia , Fases do Sono , Transtornos do Sono-Vigília , Humanos , Fases do Sono/fisiologia , Eletroculografia/métodos , Transtornos do Sono-Vigília/diagnóstico , Transtornos do Sono-Vigília/fisiopatologia , Masculino , Feminino , Adulto , Estudos de Coortes , Pessoa de Meia-Idade , Processamento de Sinais Assistido por Computador , Redes Neurais de Computação , Adulto Jovem , Polissonografia
9.
IEEE J Biomed Health Inform ; 28(6): 3466-3477, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38502613

RESUMO

Over recent decades, electroencephalogram (EEG) has become an essential tool in the field of clinical analysis and neurological disease research. However, EEG recordings are notably vulnerable to artifacts during acquisition, especially in clinical settings, which can significantly impede the accurate interpretation of neuronal activity. Blind source separation is currently the most popular method for EEG denoising, but most of the sources it separates often contain both artifacts and brain activity, which may lead to substantial information loss if handled improperly. In this paper, we introduce a dual-threshold denoising method combining spatial filtering with frequency-domain filtering to automatically eliminate electrooculogram (EOG) and electromyogram (EMG) artifacts from multi-channel EEG. The proposed method employs a fusion of second-order blind identification (SOBI) and canonical correlation analysis (CCA) to enhance source separation quality, followed by adaptive threshold to localize the artifact sources, and strict fixed threshold to remove strong artifact sources. Stationary wavelet transform (SWT) is utilized to decompose the weak artifact sources, with subsequent adjustment of wavelet coefficients in respective frequency bands tailored to the distinct characteristics of each artifact. The results of synthetic and real datasets show that our proposed method maximally retains the time-domain and frequency-domain information in the EEG during denoising. Compared with existing techniques, the proposed method achieves better denoising performance, which establishes a reliable foundation for subsequent clinical analyses.


Assuntos
Artefatos , Eletroencefalografia , Processamento de Sinais Assistido por Computador , Humanos , Eletroencefalografia/métodos , Algoritmos , Eletromiografia/métodos , Adulto , Análise de Ondaletas , Eletroculografia/métodos , Masculino , Adulto Jovem , Feminino
10.
J Fr Ophtalmol ; 47(6): 104097, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38518704

RESUMO

PURPOSE: To report the ocular manifestations, multimodal imaging characteristics and genetic testing results of six patients with autosomal recessive bestrophinopathy (ARB). METHODS: This was an observational case series including 12 eyes of 6 patients who were diagnosed with ARB. All patients underwent a complete ophthalmic examination including refraction, slit-lamp biomicroscopy, dilated fundus examination, fundus autofluorescence, optical coherence tomography and electrooculography. BEST1 gene sequencing was also performed for all patients. RESULTS: The mean age was 22.8years and the male-female ratio was 0.50. All ARB patients had a hyperopic refractive error. A spectrum of fundus abnormalities, including multifocal yellowish subretinal deposits in the posterior pole, subfoveal accumulation of vitelliform material and cystoid macular edema, was observed. Fundus autofluorescence imaging demonstrated marked hyperautofluorescence corresponding to the yellowish subretinal deposits. Optical coherence tomography revealed serous retinal detachment, intraretinal cysts, brush border appearance caused by elongation of the outer segments of photoreceptors, and hyperreflective dome-shaped deposits at the level of the retinal pigment epithelium. Fundus fluorescein angiography showed hyperfluorescence with staining of the yellowish subretinal deposits. Electrooculography showed reduced Arden ratio in all patients. In addition, biallelic pathogenic variants in the BEST1 gene were detected in all patients. CONCLUSION: ARB is a rare autosomal recessive inherited retinal disorder with biallelic pathogenic variants in the BEST1 gene and may present with a wide range of ocular abnormalities that may not be easily diagnosed. Multimodal retinal imaging in conjunction with EOG is helpful to establish the correct diagnosis.


Assuntos
Bestrofinas , Oftalmopatias Hereditárias , Imagem Multimodal , Doenças Retinianas , Tomografia de Coerência Óptica , Humanos , Feminino , Masculino , Oftalmopatias Hereditárias/genética , Oftalmopatias Hereditárias/diagnóstico , Adulto , Adulto Jovem , Doenças Retinianas/genética , Doenças Retinianas/diagnóstico , Doenças Retinianas/patologia , Bestrofinas/genética , Adolescente , Angiofluoresceinografia , Eletroculografia , Genes Recessivos , Criança
11.
Sensors (Basel) ; 24(5)2024 Feb 28.
Artigo em Inglês | MEDLINE | ID: mdl-38475079

RESUMO

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.


Assuntos
Condução de Veículo , Acidentes de Trânsito/prevenção & controle , Eletroculografia , Algoritmos , Vigília
12.
Comput Biol Med ; 173: 108314, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38513392

RESUMO

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.


Assuntos
Fases do Sono , Sono , Polissonografia/métodos , Eletroencefalografia/métodos , Eletroculografia/métodos
13.
Adv Healthc Mater ; 13(15): e2303581, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38386698

RESUMO

Abnormal oculomotor movements are known to be linked to various types of brain disorders, physical/mental shocks to the brain, and other neurological disorders, hence its monitoring can be developed into a simple but effective diagnostic tool. To overcome the limitations in the current eye-tracking system and electrooculography, a piezoelectric arrayed sensor system is developed using single-crystalline III-N thin-film transducers, which offers advantages of mechanical flexibility, biocompatibility, and high electromechanical conversion, for continuous monitoring of oculomotor movements by skin-attachable, safe, and highly sensitive sensors. The flexible piezoelectric eye movement sensor array (F-PEMSA), consisting of three transducers, is attached to the face temple area where it can be comfortably wearable and can detect the muscles' activity associated with the eye motions. Output voltages from upper, mid, and lower sensors (transducers) on different temple areas generate discernable patterns of output voltage signals with different combinations of positive/negative signs and their relative magnitudes for the various movements of eyeballs including 8 directional (lateral, vertical, and diagonal) and two rotational movements, which enable various types of saccade and pursuit tests. The F-PEMSA can be used in clinical studies on the brain-eye relationship to evaluate the functional integrity of multiple brain systems and cognitive processes.


Assuntos
Movimentos Oculares , Humanos , Movimentos Oculares/fisiologia , Dispositivos Eletrônicos Vestíveis , Monitorização Fisiológica/instrumentação , Monitorização Fisiológica/métodos , Eletroculografia/instrumentação , Eletroculografia/métodos
14.
Sensors (Basel) ; 24(2)2024 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-38257633

RESUMO

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.


Assuntos
Aceleração , Movimentos Sacádicos , Humanos , Eletroculografia , Algoritmos , Encéfalo
15.
Comput Methods Programs Biomed ; 244: 107992, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38218118

RESUMO

BACKGROUND AND OBJECTIVE: Sleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process. METHODS: A novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1). RESULTS: Based on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively. CONCLUSION: The results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.


Assuntos
Fases do Sono , Sono , Fases do Sono/fisiologia , Fatores de Tempo , Eletroencefalografia/métodos , Eletroculografia/métodos
16.
IEEE Trans Biomed Eng ; 71(2): 504-513, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-37616137

RESUMO

OBJECTIVE: Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance. METHODS: A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection. RESULT: The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03±4.65%, an average information transfer rate (ITR) of 31.42±7.39 bits/min and an average false-positive rate (FPR) of 1.78%. CONCLUSION: The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system. SIGNIFICANCE: Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.


Assuntos
Interfaces Cérebro-Computador , Dispositivos Eletrônicos Vestíveis , Eletroculografia , Eletroencefalografia/métodos , Comunicação
17.
J Sleep Res ; 33(2): e13977, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-37400248

RESUMO

Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin-electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%-43.9%, p < 0.001) and less absolute power (at 1-40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3-1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.


Assuntos
Eletroencefalografia , Sono , Adulto , Humanos , Polissonografia/métodos , Estudos de Viabilidade , Eletroculografia/métodos , Fases do Sono , Eletrodos
18.
IEEE Trans Biomed Circuits Syst ; 18(2): 322-333, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-37851555

RESUMO

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.


Assuntos
Movimentos Oculares , Radar , Humanos , Estudos de Viabilidade , Eletroculografia/métodos , Piscadela
19.
Psychophysiology ; 61(3): e14461, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-37855151

RESUMO

This study aimed to evaluate the utility and applicability of electrooculography (EOG) when studying ocular activity during complex motor behavior. Due to its lower spatial resolution relative to eye tracking (ET), it is unclear whether EOG can provide valid and accurate temporal measurements such as the duration of the Quiet Eye (QE), that is the uninterrupted dwell time on the visual target prior to and during action. However, because of its greater temporal resolution, EOG is better suited for temporal-spectral decomposition, a technique that allows us to distinguish between lower and higher frequency activity as a function of time. Sixteen golfers of varying expertise (novices to experts) putted 60 balls to a 4-m distant target on a flat surface while we recorded EOG, ET, performance accuracy, and putter kinematics. Correlational and discrepancy analyses confirmed that EOG yielded valid and accurate QE measurements, but only when using certain processing parameters. Nested cross-validation indicated that, among a set of ET and EOG temporal and spectral oculomotor features, EOG power was the most useful when predicting performance accuracy through robust regression. Follow-up cross-validation and correlational analyses revealed that more accurate performance was preceded by diminished lower-frequency activity immediately before movement initiation and elevated higher-frequency activity during movement recorded from the horizontal channel. This higher-frequency activity was also found to accompany a smoother movement execution. This study validates EOG algorithms (code provided) for measuring temporal parameters and presents a novel approach to extracting temporal and spectral oculomotor features during complex motor behavior.


Assuntos
Algoritmos , Movimentos Oculares , Humanos , Eletroculografia/métodos , Tecnologia de Rastreamento Ocular , Fenômenos Biomecânicos
20.
Clin J Sport Med ; 34(3): 280-287, 2024 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-38150378

RESUMO

OBJECTIVE: Investigate the effect of cumulative head impacts on saccade latency and errors, measured across two successive football seasons. DESIGN: Participants were acquired from a sample of convenience-one Canadian university football team. Head impacts were collected during training camp, practices, eight regular season games, and four playoff games in each season. Saccade measurements were collected at five time points-before and after training camp, at midseason, after regular season, and after playoffs. SETTING: Two seasons following players from a single USports football team during practices and games. PARTICIPANTS: Players who completed a baseline saccade measurement and a minimum of one follow-up measurement were included in the study. A total of 127 players were monitored across two competitive seasons, including 61 players who participated in both seasons. INDEPENDENT VARIABLES: Head impact measurements were collected using helmet-mounted sensors. MAIN OUTCOME MEASURES: Saccade latency and number of errors were measured using high-speed video or electro-oculography. RESULTS: On average, each head impact increased prosaccade latency by 5.16 × 10 -3 ms (95% confidence interval [CI], 2.26 × 10 -4 -1.00 × 10 -2 , P = 0.03) and antisaccade latency by 5.74 × 10 -3 ms (95% CI, 7.18 × 10 -4 -1.06 × 10 -2 , P = 0.02). These latency increases did not decrease between the two seasons; in fact, prosaccade latencies were 23.20 ms longer (95% CI, 19.40-27.14, P < 0.001) at the second season's baseline measurement than the first. The number of saccade errors was not affected by cumulative head impacts. CONCLUSIONS: Repetitive head impacts in Canadian university football result in cumulative declines in brain function as measured by saccade performance. CLINICAL RELEVANCE: Football organizations should consider implementing policies focused on reducing head impacts to improve player safety.


Assuntos
Futebol Americano , Movimentos Sacádicos , Humanos , Movimentos Sacádicos/fisiologia , Futebol Americano/fisiologia , Futebol Americano/lesões , Canadá , Masculino , Universidades , Adulto Jovem , Concussão Encefálica/fisiopatologia , Dispositivos de Proteção da Cabeça , Tempo de Reação , Cabeça/fisiologia , Adulto , Eletroculografia
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