Mnemotecnias para la aplicación e interpretación del electroencefalograma de amplitud integrada en recién nacidos / Mnemonics for the use and interpretation of amplitude-integrated electroencephalography in newborn infants

La electroencefalografía (EEG) siempre ha sido considerada una materia especializada, que amerita de entrenamiento para su aplicación e interpretación; esto ha provocado que el acceso a estos estudios quedara confinado a neurólogos y neurofisiólogos. El recién nacido ingresado en la unidad de cuidados intensivos neonatales (UCIN) amerita de monitorización neurológica para establecer diagnóstico y pronóstico, por lo que se necesita una herramienta sencilla y accesible para el personal de la UCIN. Estas características han sido cubiertas por el electroencefalograma de amplitud integrada (aEEG) que, a través de patrones visuales simples de la actividad cerebral, permite el abordaje de la condición neurológica. El objetivo de este ensayo se orienta al manejo de mnemotecnias que faciliten la identificación de patrones visuales normales y patológicos en el aEEG. La nomenclatura empleada, aunque puede parecer simple, pretende crear una idea fácilmente asimilable de los conceptos básicos para la aplicación e interpretación de la neuromonitorización con aEEG.

An electroencephalography (EEG) has always been considered a specialized field, whose use and interpretation requires training. For this reason, access to these monitoring studies has been restricted to neurologists and neurophysiologists. Newborn infants admitted to the neonatal intensive care unit (NICU) require neurophysiological monitoring to establish their diagnosis and prognosis, so a simple and accessible tool is required for NICU staff. Such features have been covered by amplitude-integrated electroencephalography (aEEG), which, through simple visual patterns of brain activity, allows to approach neurological conditions. The objective of this study is to help with the management of mnemonics that facilitate the identification of normal and pathological visual patterns in an aEEG. Although simple in appearance, this nomenclature is intended to create an easy-to-understand idea of basic concepts for the use and interpretation of neurophysiological monitoring with aEEG.

Preliminary Study on Application of Combined Monitoring of Neuroelectrophysiology-Intracranial Pressure-Cerebral Perfusion Pressure in Craniotomy for Intracranial Aneurysm Clipping: An Anatomical and Clinical Study

SUMMARY: Intracranial aneurysm is a common cerebrovascular disease with high mortality. Neurosurgical clipping for the treatment of intracranial aneurysms can easily lead to serious postoperative complications. Studies have shown that intraoperative monitoring of the degree of cerebral ischemia is extremely important to ensure the safety of operation and improve the prognosis of patients. Aim of this study was to probe the application value of combined monitoring of intraoperative neurophysiological monitoring (IONM)-intracranial pressure (ICP)-cerebral perfusion pressure (CPP) in craniotomy clipping of intracranial aneurysms. From January 2020 to December 2022, 126 patients in our hospital with intracranial aneurysms who underwent neurosurgical clipping were randomly divided into two groups. One group received IONM monitoring during neurosurgical clipping (control group, n=63), and the other group received IONM-ICP-CPP monitoring during neurosurgical clipping (monitoring group, n=63). The aneurysm clipping and new neurological deficits at 1 day after operation were compared between the two groups. Glasgow coma scale (GCS) score and national institutes of health stroke scale (NIHSS) score were compared before operation, at 1 day and 3 months after operation. Glasgow outcome scale (GOS) and modified Rankin scale (mRS) were compared at 3 months after operation. All aneurysms were clipped completely. Rate of new neurological deficit at 1 day after operation in monitoring group was 3.17 % (2/63), which was markedly lower than that in control group of 11.11 % (7/30) (P0.05). Combined monitoring of IONM-ICP-CPP can monitor the cerebral blood flow of patients in real time during neurosurgical clipping, according to the monitoring results, timely intervention measures can improve the consciousness state of patients in early postoperative period and reduce the occurrence of early postoperative neurological deficits.

El aneurisma intracraneal es una enfermedad cerebrovascular común con alta mortalidad. El clipaje neuroquirúrgico para el tratamiento de aneurismas intracraneales puede provocar complicaciones posoperatorias graves. Los estudios han demostrado que la monitorización intraoperatoria del grado de isquemia cerebral es extremadamente importante para garantizar la seguridad de la operación y mejorar el pronóstico de los pacientes. El objetivo de este estudio fue probar el valor de la aplicación de la monitorización combinada de la monitorización neurofisiológica intraoperatoria (IONM), la presión intracraneal (PIC) y la presión de perfusión cerebral (CPP) en el clipaje de craneotomía de aneurismas intracraneales. Desde enero de 2020 hasta diciembre de 2022, 126 pacientes de nuestro hospital con aneurismas intracraneales que se sometieron a clipaje neuroquirúrgico se dividieron aleatoriamente en dos grupos. Un grupo recibió monitorización IONM durante el clipaje neuroquirúrgico (grupo de control, n=63) y el otro grupo recibió monitorización IONM-ICP-CPP durante el clipaje neuroquirúrgico (grupo de monitorización, n=63). Se compararon entre los dos grupos el recorte del aneurisma y los nuevos déficits neurológicos un día después de la operación. La puntuación de la escala de coma de Glasgow (GCS) y la puntuación de la escala de accidentes cerebrovasculares de los institutos nacionales de salud (NIHSS) se compararon antes de la operación, 1 día y 3 meses después de la operación. La escala de resultados de Glasgow (GOS) y la escala de Rankin modificada (mRS) se compararon 3 meses después de la operación. Todos los aneurismas fueron cortados por completo. La tasa de nuevo déficit neurológico 1 día después de la operación en el grupo de seguimiento fue del 3,17 % (2/63), que fue notablemente inferior a la del grupo de control del 11,11 % (7/30) (P 0,05). La monitorización combinada de IONM-ICP-CPP puede controlar el flujo sanguíneo cerebral de los pacientes en tiempo real durante el corte neuroquirúrgico; de acuerdo con los resultados de la monitorización, las medidas de intervención oportunas pueden mejorar el estado de conciencia de los pacientes en el período postoperatorio temprano y reducir la aparición de problemas postoperatorios tempranos y déficits neurológicos.

Multisensory Conflict Impairs Cortico-Muscular Network Connectivity and Postural Stability: Insights from Partial Directed Coherence Analysis / 神经科学通报·英文版

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.

Association between telomere length in the DNA of peripheral blood leukocytes and the propofol dose in anesthesia induction: an observational study

Abstract Introduction: Propofol is a widely used anesthetic and its dose is closely related to aging. Telomere length (TL) is a unique heritable trait, and emerging as a biomarker of aging, health and disease. Telomerase RNA component (TERC) plays an important role in maintaining TL. We proposed a hypothesis that propofol dose in general anesthesia can be predicted by measuring TL before operation, which greatly reduced the risk of anesthesia, especially the elderly. Methods: The association between the propofol dose in anesthesia induction and: TL in the DNA of peripheral blood leukocytes; body weight; sex; difference of the Bispectral Index (BIS) before and after anesthesia induction in patients was evaluated by multivariable linear regression analyses. The mutation at the 5'end or 3'end of TERC was detected. We recruited 100 patients of elective surgery. Results: We found that propofol dose in anesthesia induction was clearly correlated significantly with TL (r = 0.78, p < 0.001), body weight (r = 0.84, p = 0.004), sex (r = 0.83, p= 0.84, p = 0.004), sex (r = 0.83, p = 0.004), and difference of BIS before and after anesthesia induction (r = 0.85, p = 0.029). By comparing the absolute values of standardized regression coefficients (0.58, 0.21, 0.19, and 0.12) of the four variables, it can be seen that TL contributes the most to the propofol dose in anesthesia induction. However, the mutation at the 5' end or 3' end of TERC was not found. Conclusions: These findings provide preliminary evidence that the propofol dose in anesthesia induction was clearly correlated with genetically determined TL. TL may be a promising predictor of the propofol dose, which is beneficial to improve the safety of anesthesia and reduce perioperative complications.

Detección de novedad y codificación predictiva en el sistema auditivo: Impacto clínico en disfunciones auditivas y vestibulares. Revisión de la literatura / Deviance detection and predictive coding in the auditory system: Clinical impact in auditory and vestibular dysfunctions

Una propiedad fundamental de los sistemas sensoriales es su capacidad para detectar estímulos novedosos en el entorno. El sistema nervioso posee neuronas que disminuyen su respuesta a los estímulos sonoros que se repiten a lo largo del tiempo y otras neuronas que aumentan su frecuencia de disparo ante estímulos novedosos, siendo la diferencia entre ambas respuestas conocida como adaptación-específica a estímulos. En las últimas décadas, se ha propuesto que el cerebro establece, continuamente, predicciones de los estímulos novedosos y del entorno basándose en sus experiencias previas y en modelos de representación internos, teoría denominada codificación predictiva. En esta revisión, abordaremos algunos conceptos de la adaptación-específica a estímulos y codificación predictiva, centrándonos principalmente en el sistema auditivo. Por último, propondremos una explicación teórica basada en el marco de la codificación predictiva para algunas disfunciones neuropsiquiátricas, auditivas y vestibulares.

A fundamental property of sensory systems is their ability to detect novel stimuli in the environment. The nervous system possesses neurons that decrease their response to sound stimuli that are repeated over time and other neurons that increase their firing rate to novel stimuli, the difference between the two responses being known as stimulus-specific adaptation. In recent decades, it has been proposed that the brain continuously makes predictions of novel stimuli and the environment based on its previous experiences and internal representational models, a theory called predictive coding. In this review, we will address some concepts of stimulus-specific adaptation and predictive coding, focusing mainly on the auditory system. Finally, we will propose a theoretical explanation based on the predictive coding framework for some neuropsychiatric, auditory, and vestibular dysfunctions.

Factores asociados a epilepsia farmacorresistente en niños atendidos en un hospital de referencia nacional / Factors associated with drug-resistant epilepsy in children treated at a national referral hospital

Antecedentes: Según la Organización Mundial de la Salud (OMS) cerca de 70 millones de personas en el mundo padecen epilepsia. Los países de medianos y bajos ingresos presentan 70-80% de los casos; se estima que afecta 4-13% de los niños hasta los 16 años, de los cuales la epilepsia farmacorresistente (EFR) se desarrolla en 10-23%. Objetivo: Determinar factores de riesgo asociados a EFR en pacientes pediátricos atendidos en Hospital María, Especialidades Pediátricas (HMEP), Tegucigalpa, marzo 2017-marzo 2022. Métodos: Estudio de casos-controles. A partir del total de pacientes menores de 18 años con diagnóstico de epilepsia atendidos en el Servicio de Neurología HMEP, se definió Caso como pacientes con diagnóstico de EFR y Controles como pacientes con epilepsia no farmacorresistente (ENFR). A partir de expedientes clínicos, se evaluaron factores sociodemográficos, antecedentes personales y familiares, factores clínicos, estudios de imagen y electroencefalograma. El estudio fue aprobado por el Comité de Ética Institucional. Resultados: Se analizaron 81 casos y 162 controles. La edad más afectada en casos fue preescolar (35.8%), en controles fue edad escolar (41.4%). El sexo masculino presentó similar distribución en ambos grupos (51.8% y 51.2%). La procedencia rural fue más frecuente en los casos que en controles (58.0% versus 48.8%). Se identificaron los siguientes factores asociados a EFR: Antecedentes familiares de epilepsia (ORa 2.32, IC95%1.22­4.41, p=0.01), alteración focal en examen físico (ORa 2.23, IC95%1.10­4.55, p=0.02), neurodesarrollo anormal (ORa 2.78, IC95%1.18­6.54, p=0.02). Discusión: El control adecuado de las crisis epilépticas incide directamente en la calidad de vida y sobrevida de los pacientes. La identificación correcta de los niños con epilepsia con los factores asociados identificados en este estudio, que coinciden con lo descrito internacionalmente, permitirá hacer un mejor tamizaje y priorizar la referencia temprana a un neurólogo pediatra contribuyendo a mejorar la calidad de vida de los pacientes...(AU)

Estimulación de nervio vago en epilepsia refractaria y callosotomía previa sin mejoría. Reporte de caso / Vagus nerve stimulation in refractory epilepsy and previous callosotomy without vimprovement. Case report / Estimulação do nervo vago em epilepsia refratária e calosotomia prévia sem melhora. Relato de caso

La epilepsia refractaria tanto generalizada como focal, es una patología sumamente incapacitante, para el tratamiento de la misma se ha establecido a la callosotomía desde hace décadas como primera línea quirúrgica para su control, la cual puede presentar efectos secundarios importantes como síndrome de desconexión y pérdida de memoria, sin embargo, existen pacientes que no responden a la callosotomía y necesitan nuevas líneas de tratamiento, buscando en la estimulación de nervio vago una respuesta a su condición. Descripción del caso de estudio. Se presenta el caso de paciente masculino de 24 años de edad con antecedente patológico de convulsiones tipo tónico clónicas generalizadas confirmadas por video electroencefalograma de 24 horas, de predominio nocturno de 13 años de evolución, es sometido a 2 regímenes farmacológicos antiepilépticos diferentes en un período de 7 años de duración, posteriormente diagnosticado con epilepsia refractaria, por lo que se realiza callosotomía sin control de su cuadro clínico, el mismo año se realiza estimulación de nervio vago, presentando resultados favorables en su evolución. Conclusión. Luego de evidenciar el presente caso de estudio se concluye que el tratamiento de epilepsia refractaria con la colocación de un estimulador de nervio vago izquierdo asociado a un correcto régimen FAE es una alternativa muy eficaz para considerar en estos pacientes.

Refractory epilepsy, both generalized and focal, is an extremely disabling pathology. For its treatment, callosotomy has been established for decades as the first surgical line for its control, which can present important side effects such as disconnection and loss syndrome. by heart, however, there are patients who do not respond to callosotomy and need new lines of treatment, looking for an answer to their condition in vagus nerve stimulation. Description of the case study. We present the case of a 24-year-old male patient with a pathological history of generalized tonic-clonic seizures confirmed by a 24-hour video electroencephalogram, predominantly nocturnal for 13 years, undergoing 2 different antiepileptic pharmacological mechanisms over a period of 7 years in duration, later diagnosed with refractory epilepsy, for which callosotomy was performed without control of its clinical picture, the same year vagus nerve stimulation was performed, presenting favorable results in its evolution. Conclution. After evidencing the present case study, it is concluded that the treatment of refractory epilepsy with the placement of a left vagus nerve stimulator associated with a correct AED regimen is a very effective alternative to consider in these patients.

A epilepsia refratária, tanto generalizada quanto focal, é uma patologia extremamente incapacitante. Para seu tratamento, a calosotomia se estabeleceu há décadas como a primeira linha cirúrgica para seu controle, que pode apresentar importantes efeitos colaterais como desconexão e síndrome de perda., há pacientes que não respondem à calosotomia e precisam de novas linhas de tratamento, buscando resposta para sua condição na estimulação do nervo vago. Descrição do estudo de caso. Apresentamos o caso de um doente do sexo masculino, 24 anos, com antecedentes patológicos de crises tónico-clónicas generalizadas confirmadas por videoeletroencefalograma de 24 horas, predominantemente nocturnas há 13 anos, submetido a 2 mecanismos farmacológicos antiepilépticos diferentes ao longo de 7 anos de duração, posteriormente diagnosticada com epilepsia refratária, para a qual foi realizada calosotomia sem controle de seu quadro clínico, no mesmo ano foi realizada estimulação do nervo vago, apresentando resultados favoráveis em sua evolução. Conclusão. Depois de evidenciar o presente estudo de caso, conclui-se que o tratamento da epilepsia refratária com a colocação de um estimulador de nervo vago esquerdo associado a um esquema correto de DEA é uma alternativa muito eficaz a ser considerada nesses pacientes.

Epilepsy classification (ILAE Classification 2017) in resource-limited countries: A cross-sectional study on epilepsy in pediatric patients from Sudan

Background: The objective of this study is to utilize the ILAE 2017 to classify epilepsy patients and determine its applicability in Sudan. Methods: This study is a prospective, descriptive, cross-sectional research conducted in two pediatric epilepsy clinics in Khartoum State, Sudan. Results: In this cross-sectional study, 350 pediatric patients with epilepsy were included, with a mean age of 8.4 ± 4.7 years and a mean illness duration of 4.71 ± 3.91 years. The ILAE classification was applied, showing that 71.11% of patients had generalized onset seizures, 27.7% had focal onset seizures, and only 1.1% had unknown onset seizures. Among patients with focal onset seizures, 56.4% had intact awareness, while 43.6% had impaired levels of awareness. The majority of patients who had generalized onset seizures experienced motor onset seizures, with tonicclonic seizures being the most common (44.2%). Nearly all patients with unknown onset seizures experienced tonic-clonic convulsions. These findings provide insights into the prevalence and types of seizures among pediatric epilepsy patients in Sudan and can guide clinicians in developing appropriate treatment plans. Conclusion: This study highlights the importance of utilizing the latest ILAE classification 2017 in epilepsy classification and its potential utilization in resource limited areas like Sudan.

A preliminary study on a new method for evaluating brain maturation in preterm infants / 中国当代儿科杂志

OBJECTIVES@#To establish a new method for evaluating the brain maturation of preterm infants based on the features of electroencephalographic activity.@*METHODS@#A prospective study was conducted on the video electroencephalography (vEEG) and amplitude-integrated electroencephalography (aEEG) recordings within 7 days after birth of preterm infants who had a postmenstrual age (PMA) of 25-36 weeks and met the inclusion criteria. The background activity of aEEG+conventional electroencephalography (cEEG) was scored according to the features of brain maturation as a new evaluation system and was compared with the aEEG evaluation system. The correlations of the evaluation results of the two methods with gestational age (GA), PMA, and head circumference were evaluated. The intervals of the total scores of aEEG+cEEG and aEEG were calculated for preterm infants with different PMAs and were compared between groups. The consistency of the new scoring system was evaluated among different raters.@*RESULTS@#A total of 52 preterm infants were included. The total scores of aEEG+cEEG and aEEG were positively correlated with GA, PMA, and head circumference (P<0.05), and the correlation coefficient between the total scores of the two systems and PMA and GA was >0.9. The normal score intervals for aEEG+cEEG and aEEG scoring systems were determined in preterm infants with different PMAs as follows: infants with a PMA of less than 28 weeks had scores of 13.0 (11.0, 14.0) points for aEEG+cEEG and 6.0 (4.0, 7.0) points for aEEG; infants with a PMA between 28 and 29+6 weeks had scores of 16.0 (14.5, 17.0) points for aEEG+cEEG and 8.0 (6.0, 8.0) points for aEEG; infants with a PMA between 30 and 31+6 weeks had scores of 18.0 (17.0, 21.0) points for aEEG+cEEG and 9.0 (8.0, 10.0) points for aEEG; infants with between 32 and 33+6 weeks had scores of 22.0 (20.0, 24.5) points for aEEG+cEEG and 10.0 (10.0, 10.8) points for aEEG; infants with a PMA between 34 and 36 weeks had scores of 26.0 (24.5, 27.5) points for aEEG+cEEG and 11.0 (10.0, 12.0) points for aEEG. There were significant differences in the total scores of aEEG+cEEG and aEEG among the different PMA groups (P<0.05). There was a high consistency between different raters when using the scoring system to evaluate the brain maturation of preterm infants (κ=0.86).@*CONCLUSIONS@#The aEEG+cEEG scoring system established in this study can quantitatively reflect the brain maturation of preterm infants, with a good discriminatory ability between preterm infants with different PMAs and high consistency between different raters.

Application of brain functional connectivity and nonlinear dynamic analysis in brain function assessment for infants with controlled infantile spasm / 中国当代儿科杂志

OBJECTIVES@#To investigate the role of brain functional connectivity and nonlinear dynamic analysis in brain function assessment for infants with controlled infantile spasm (IS).@*METHODS@#A retrospective analysis was performed on 14 children with controlled IS (IS group) who were admitted to the Department of Neurology, Anhui Provincial Children's Hospital, from January 2019 to January 2023. Twelve healthy children, matched for sex and age, were enrolled as the control group. Electroencephalogram (EEG) data were analyzed for both groups to compare the features of brain network, and nonlinear dynamic indicators were calculated, including approximate entropy, sample entropy, permutation entropy, and permutation Lempel-Ziv complexity.@*RESULTS@#Brain functional connectivity showed that compared with the control group, the IS group had an increase in the strength of functional connectivity, and there was a significant difference between the two groups in the connection strength between the Fp2 and F8 channels (P<0.05). The network stability analysis showed that the IS group had a significantly higher network stability than the control group at different time windows (P<0.05). The nonlinear dynamic analysis showed that compared with the control group, the IS group had a significantly lower sample entropy of Fz electrode (P<0.05).@*CONCLUSIONS@#Abnormalities in brain network and sample entropy may be observed in some children with controlled IS, and it is suggested that quantitative EEG analysis parameters can serve as neurological biomarkers for evaluating brain function in children with IS.

A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model / 生物医学工程学杂志

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.

Automatic sleep staging based on power spectral density and random forest / 生物医学工程学杂志

The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.

Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning / 生物医学工程学杂志

The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.

Ethics considerations on brain-computer interface technology / 生物医学工程学杂志

The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.

Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement / 生物医学工程学杂志

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.

Effect of electroconvulsive therapy on brain functional network in major depressive disorder / 生物医学工程学杂志

Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

A method of mental disorder recognition based on visibility graph / 生物医学工程学杂志

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.

Automatic sleep staging model based on single channel electroencephalogram signal / 生物医学工程学杂志

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.

Resting-state electroencephalogram relevance state recognition of Parkinson's disease based on dynamic weighted symbolic mutual information and k-means clustering / 生物医学工程学杂志

At present, the incidence of Parkinson's disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band ( P = 0.034) and State5 of Gamma frequency band ( P = 0.010) could be used to differentiate health controls and off-medication Parkinson's disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson's disease.

Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism / 生物医学工程学杂志

In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.