ABSTRACT
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.
Subject(s)
Humans , Aged , Propofol/pharmacology , Body Weight , DNA , Telomere , Anesthetics, Intravenous/pharmacology , Electroencephalography , Anesthesia, General , LeukocytesABSTRACT
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)
Subject(s)
Drug Resistant Epilepsy , Sociodemographic Factors , Seizures/complications , ElectroencephalographyABSTRACT
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.
Subject(s)
Humans , Male , Adult , ElectroencephalographyABSTRACT
OBJECTIVE@#To explore the biomarkers of tinnitus in vestibular schwannoma patients using electroencephalographic (EEG) microstate technology.@*METHODS@#The EEG and clinical data of 41 patients with vestibular schwannoma were collected. All the patients were evaluated by SAS, SDS, THI and VAS scales. The EEG acquisition time was 10-15 min, and the EEG data were preprocessed and analyzed using MATLAB and EEGLAB software package.@*RESULTS@#Of the 41 patients with vestibular schwannoma, 29 patients had tinnitus and 12 did not have tinnitus, and their clinical parameters were comparable. The average global explanation variances of the non-tinnitus and tinnitus groups were 78.8% and 80.1%, respectively. The results of EEG microstate analysis showed that compared with those without tinnitus, the patients with tinnitus had an increased frequency (P=0.033) and contribution (P=0.028) of microstate C. Correlation analysis showed that THI scale scores of the patients were negatively correlated with the duration of microstate A (R=-0.435, P=0.018) and positively with the frequencies of microstate B (R=0.456, P=0.013) and microstate C (R=0.412, P=0.026). Syntax analysis showed that the probability of transition from microstate C to microstate B increased significantly in vestibular schwannoma patients with tinnitus (P=0.031).@*CONCLUSION@#EEG microstate features differ significantly between vestibular schwannoma patients with and without tinnitus. This abnormality in patients with tinnitus may reflect the potential abnormality in the allocation of neural resources and the transition of brain functional activity.
Subject(s)
Humans , Neuroma, Acoustic/complications , Electroencephalography , Patients , ProbabilityABSTRACT
Objective: To analyze the clinical features of children with uridine responsive developmental epileptic encephalopathy 50 (DEE50) caused by CAD gene variants. Methods: A retrospective study was conducted on 6 patients diagnosed with uridine-responsive DEE50 caused by CAD gene variants at Beijing Children's Hospital and Peking University First Hospital from 2018 to 2022. The epileptic seizures, anemia, peripheral blood smear, cranial magnetic resonance imaging (MRI), visual evoked potential (VEP), genotype features and the therapeutic effect of uridine were descriptively analyzed. Results: A total of 6 patients, including 3 boys and 3 girls, aged 3.5(3.2,5.8) years, were enrolled in this study. All patients presented with refractory epilepsy, anemia with anisopoikilocytosis and global developmental delay with regression. The age of epilepsy onset was 8.5 (7.5, 11.0) months, and focal seizures were the most common seizure type (6 cases). Anemia ranged from mild to severe. Four patients had peripheral blood smears prior to uridine administration, showing erythrocytes of variable size and abnormal morphology, and normalized at 6 (2, 8) months after uridine supplementation. Two patients suffered from strabismus, 3 patients had VEP examinations, indicating of suspicious optic nerve involvement, and normal fundus examinations. VEP was re-examined at 1 and 3 months after uridine supplementation, suggesting significant improvement or normalization. Cranial MRI were performed at 5 patients, demonstrating cerebral and cerebellar atrophy. They had cranial MRI re-examined after uridine treatment with a duration of 1.1 (1.0, 1.8) years, indicating significant improvement in brain atrophy. All patients received uridine orally at a dose of 100 mg/(kg·d), the age at initiation of uridine treatment was 1.0 (0.8, 2.5) years, and the duration of treatment was 2.4 (2.2, 3.0) years. Immediate cession of seizures was observed within days to a week after uridine supplementation. Four patients received uridine monotherapy and were seizure free for 7 months, 2.4 years, 2.4 years and 3.0 years respectively. One patient achieved seizure free for 3.0 years after uridine supplementation and had discontinued uridine for 1.5 years. Two patients were supplemented with uridine combined with 1 to 2 anti-seizure medications and had a reduced seizure frequency of 1 to 3 times per year, and they had achieved seizure free for 8 months and 1.4 years respectively. Conclusions: The clinical manifestations of DEE50 caused by CAD gene variants present a triad of refractory epilepsy, anemia with anisopoikilocytosis, and psychomotor retardation with regression, accompanied by suspected optic nerve involvement, all of which respond to uridine treatment. Prompt diagnosis and immediate uridine supplementation could lead to significant clinical improvement.
Subject(s)
Male , Female , Humans , Child , Infant , Epilepsy/genetics , Retrospective Studies , Drug Resistant Epilepsy , Uridine , Evoked Potentials, Visual , Anemia , Electroencephalography/adverse effects , Neurodegenerative DiseasesABSTRACT
The article presents an original method for the automatic assessment of the quality of event-related potentials (ERPs), based on the calculation of the coefficient ε, which describes the compliance of recorded ERPs with some statistically significant parameters. This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines. The frequency of migraine attacks was correlated with the spatial distribution of the coefficients ε, calculated for EEG channels. More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region. Patients with infrequent migraines exhibited maximum quality in the frontal areas. The automatic analysis of spatial maps of the coefficient ε demonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.
Subject(s)
Humans , Chronic Pain , Evoked Potentials , Migraine Disorders/psychology , Occipital Lobe , ElectroencephalographyABSTRACT
The difference between sleep and wakefulness is critical for human health. Sleep takes up one third of our lives and remains one of the most mysterious conditions; it plays an important role in memory consolidation and health restoration. Distinct neural behaviors take place under awake and asleep conditions, according to neuroimaging studies. While disordered transitions between wakefulness and sleep accompany brain disease, further investigation of their specific characteristics is required. In this study, the difference is objectively quantified by means of network controllability. We propose a new pipeline using a public intracranial stereo-electroencephalography (stereo-EEG) dataset to unravel differences in the two conditions in terms of system neuroscience. Because intracranial stereo-EEG records neural oscillations covering large-scale cerebral areas, it offers the highest temporal resolution for recording neural behaviors. After EEG preprocessing, the EEG signals are band-passed into sub-slow (0.1-1 Hz), delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz) band oscillations. Then, dynamic functional connectivity is extracted from time-windowed EEG neural oscillations through phase-locking value (PLV) and non-overlapping sliding time windows. Next, average and modal network controllability are implemented on these time-varying brain networks. Based on this preliminary study, it appears that significant differences exist in the dorsolateral frontal-parietal network (FPN), salience network (SN), and default-mode network (DMN). The combination of network controllability and dynamic functional networks offers new insight for characterizing distinctions between awake and asleep stages in the brain. In other words, network controllability captures the underlying brain dynamics under both awake and asleep conditions.
Subject(s)
Humans , Wakefulness , Electroencephalography/methods , Brain Mapping/methods , BrainABSTRACT
Non-suicidal self-injury (NSSI) is becoming increasingly common in adolescents and seriously affects their physical and mental health, and it is also a major risk factor for suicide among adolescents. NSSI has now become a public health issue of general concern; however, the identification of cognitive dysfunction in NSSI is still based on neuropsychological cognitive assessment and subjective questionnaire assessment, with a lack of objective evaluation indicators. As a method for studying the cognitive neural mechanism of NSSI, electroencephalography is a reliable tool for finding objective biomarkers of NSSI. This article reviews the recent research on electrophysiology associated with cognitive dysfunction in adolescents with NSSI.
Subject(s)
Humans , Adolescent , Self-Injurious Behavior , Cognitive Dysfunction , Electroencephalography , Neuropsychological Tests , Risk FactorsABSTRACT
The male neonate in this case study was admitted to the hospital at 15 hours of age due to respiratory distress for 15 hours and poor response for 3 hours after resuscitation from asphyxia. The neonate was highly unresponsive, with central respiratory failure and seizures. Serum ammonia was elevated (>1 000 μmol/L). Blood tandem mass spectrometry revealed a significant decrease in citrulline. Rapid familial whole genome sequencing revealed OTC gene mutations inherited from the mother. Continuous hemodialysis filtration and other treatments were given. Neurological assessment was performed by cranial magnetic resonance imaging and electroencephalogram. The neonate was diagnosed with ornithine transcarbamylase deficiency combined with brain injury. He died at 6 days of age after withdrawing care. This article focuses on the differential diagnosis of neonatal hyperammonemia and introduces the multidisciplinary management of inborn error of metabolism.
Subject(s)
Humans , Infant, Newborn , Male , Citrulline , Electroencephalography , Hyperammonemia , Ornithine Carbamoyltransferase Deficiency Disease/therapy , SeizuresABSTRACT
OBJECTIVES@#To investigate the clinical efficacy of mild therapeutic hypothermia (MTH) with different rewarming time on neonatal hypoxic-ischemic encephalopathy (HIE).@*METHODS@#A prospective study was performed on 101 neonates with HIE who were born and received MTH in Zhongshan Hospital, Xiamen University, from January 2018 to January 2022. These neonates were randomly divided into two groups: MTH1 group (n=50; rewarming for 10 hours at a rate of 0.25°C/h) and MTH2 group (n=51; rewarming for 25 hours at a rate of 0.10°C/h). The clinical features and the clinical efficacy were compared between the two groups. A binary logistic regression analysis was used to identify the factors influencing the occurrence of normal sleep-wake cycle (SWC) on amplitude-integrated electroencephalogram (aEEG) at 25 hours of rewarming.@*RESULTS@#There were no significant differences between the MTH1 and MTH2 groups in gestational age, 5-minute Apgar score, and proportion of neonates with moderate/severe HIE (P>0.05). Compared with the MTH2 group, the MTH1 group tended to have a normal arterial blood pH value at the end of rewarming, a significantly shorter duration of oxygen dependence, a significantly higher proportion of neonates with normal SWC on aEEG at 10 and 25 hours of rewarming, and a significantly higher Neonatal Behavioral Neurological Assessment score on days 5, 12, and 28 after birth (P<0.05), while there was no significant difference in the incidence rate of rewarming-related seizures between the two groups (P>0.05). There were no significant differences between the two groups in the incidence rate of neurological disability at 6 months of age and the score of Bayley Scale of Infant Development at 3 and 6 months of age (P>0.05). The binary logistic regression analysis showed that prolonged rewarming time (25 hours) was not conducive to the occurrence of normal SWC (OR=3.423, 95%CI: 1.237-9.469, P=0.018).@*CONCLUSIONS@#Rewarming for 10 hours has a better short-term clinical efficacy than rewarming for 25 hours. Prolonging rewarming time has limited clinical benefits on neonates with moderate/severe HIE and is not conducive to the occurrence of normal SWC, and therefore, it is not recommended as a routine treatment method.
Subject(s)
Infant, Newborn , Infant , Child , Humans , Child, Preschool , Prospective Studies , Rewarming , Hypoxia-Ischemia, Brain/therapy , Hypothermia, Induced/methods , Treatment Outcome , Electroencephalography/methodsABSTRACT
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.
Subject(s)
China , Sleep Stages , Sleep , Electroencephalography , Databases, FactualABSTRACT
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.
Subject(s)
Humans , Mental Disorders/diagnosis , Alzheimer Disease/diagnosis , Brain Injuries , Electroencephalography , Recognition, PsychologyABSTRACT
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.
Subject(s)
Humans , Depressive Disorder, Major/therapy , Electroconvulsive Therapy , Brain , Algorithms , ElectroencephalographyABSTRACT
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.
Subject(s)
Humans , Time Factors , Brain , Electroencephalography , Imagery, Psychotherapy , Neural Networks, ComputerABSTRACT
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.
Subject(s)
Humans , Brain-Computer Interfaces , Technology , Brain , User-Computer Interface , ElectroencephalographyABSTRACT
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.
Subject(s)
Humans , Sleep Stages , Algorithms , Sleep , Wavelet Analysis , Electroencephalography/methods , Machine LearningABSTRACT
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.
Subject(s)
Humans , Random Forest , Bayes Theorem , Sleep Stages , Sleep , Electroencephalography/methodsABSTRACT
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.
Subject(s)
Humans , Scalp , Brain Mapping/methods , Epilepsy/diagnosis , Electroencephalography/methods , BrainABSTRACT
OBJECTIVES@#To explore a new method for electroencephalography (EEG) background analysis in neonates with hypoxic-ischemic encephalopathy (HIE) and its relationship with clinical grading and head magnetic resonance imaging (MRI) grading.@*METHODS@#A retrospective analysis was performed for the video electroencephalography (vEEG) and amplitude-integrated electroencephalography (aEEG) monitoring data within 24 hours after birth of neonates diagnosed with HIE from January 2016 to August 2022. All items of EEG background analysis were enrolled into an assessment system and were scored according to severity to obtain the total EEG score. The correlations of total EEG score with total MRI score and total Sarnat score (TSS, used to evaluate clinical gradings) were analyzed by Spearman correlation analysis. The total EEG score was compared among the neonates with different clinical gradings and among the neonates with different head MRI gradings. The receiver operating characteristic (ROC) curve and the area under thecurve (AUC) were used to evaluate the value of total EEG score in diagnosing moderate/severe head MRI abnormalities and clinical moderate/severe HIE, which was then compared with the aEEG grading method.@*RESULTS@#A total of 50 neonates with HIE were included. The total EEG score was positively correlated with the total head MRI score and TSS (rs=0.840 and 0.611 respectively, P<0.001). There were significant differences in the total EEG score between different clinical grading groups and different head MRI grading groups (P<0.05). The total EEG score and the aEEG grading method had an AUC of 0.936 and 0.617 respectively in judging moderate/severe head MRI abnormalities (P<0.01) and an AUC of 0.887 and 0.796 respectively in judging clinical moderate/severe HIE (P>0.05). The total EEG scores of ≤6 points, 7-13 points, and ≥14 points were defined as mild, moderate, and severe EEG abnormalities respectively, which had the best consistency with clinical grading and head MRI grading (P<0.05).@*CONCLUSIONS@#The new EEG background scoring method can quantitatively reflect the severity of brain injury and can be used for the judgment of brain function in neonates with HIE.
Subject(s)
Infant, Newborn , Humans , Hypoxia-Ischemia, Brain/diagnostic imaging , Retrospective Studies , Brain Injuries , Electroencephalography , ROC CurveABSTRACT
Neonatal hypoxic-ischemic encephalopathy (HIE) is a common disease that affects brain function in neonates. At present, mild hypothermia and hyperbaric oxygen therapy are the main methods for the treatment of neonatal HIE; however, they are independent of each other and cannot be combined for synchronous treatment, without monitoring of brain function-related physiological information. In addition, parameter setting of hyperbaric oxygen chamber and mild hypothermia mattress relies on the experience of the medical practitioner, and the parameters remain unchanged throughout the medical process. This article proposes a new device for the treatment of neonatal HIE, which has the modules of hyperbaric oxygen chamber and mild hypothermic mattress, so that neonates can receive the treatment of hyperbaric oxygen chamber and/or mild hypothermic mattress based on their conditions. Meanwhile, it can realize the real-time monitoring of various physiological information, including amplitude-integrated electroencephalogram, electrocardiogram, and near-infrared spectrum, which can monitor brain function, heart rate, rhythm, myocardial blood supply, hemoglobin concentration in brain tissue, and blood oxygen saturation. In combination with an intelligent control algorithm, the device can intelligently regulate parameters according to the physiological information of neonates and give recommendations for subsequent treatment.