Effect of high-frequency repetitive transcranial magnetic stimulation in M1 region combined with dorsolateral prefrontal cortex on electroencephalogram θ frequency band amplitude of patients with neuropathic pain after spinal cord injury / 中国康复理论与实践

ObjectiveTo explore the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in M1 region combined with dorsolateral prefrontal cortex (DLPFC) on electroencephalogram (EEG) θ frequency band amplitude of patients with neuropathic pain (NP) after spinal cord injury. MethodsFrom June, 2022 to June, 2023, 50 NP patients after SCI in Qingdao University Affiliated Hospital were included and divided into M1 region stimulation group (n = 25) and M1 region combined with DLPFC stimulation group (the combined stimulation group, n = 25). M1 region stimulation group received 10 Hz rTMS in the left M1 region, while the combined stimulation group received same stimulation in left M1 region combined with DLPFC, for three weeks. Before and after intervention, the pain was assessed with Short Form of McGill Pain Questionnaire (SF-MPQ), the depression and anxiety status were evaluated using Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA), and the EEG θ frequency band amplitude was recorded to detect the changes of brain electrophysiological activity. ResultsFour cases in M1 region stimulation group, and two cases in the combined stimulation group were dropped. After intervention, the total score of SF-MPQ and the scores of the subscales, the scores of HMMD and HAMA decreased in both groups (|t| > 2.523, P < 0.05). The EEG θ frequency band amplitude significantly reduced in the prefrontal and frontal regions in M1 region stimulation group (|t| > 5.243, P < 0.001), and it also significantly reduced in the prefrontal, frontal regions, central and parietal regions in the combined stimulation group (|t| > 4.630, P < 0.001). All the scores were lower (|t| > 2.270, Z = -1.973, P < 0.05), and the EEG θ frequency band amplitude in the prefrontal, frontal regions, central and parietal regions were lower (P < 0.05) in the combined stimulation group than in M1 region stimulation group. ConclusionHigh frequency rTMS is an effective analgesic method on NP after SCI, which can improve their depression and anxiety symptoms and reduce the EEG θ frequency band amplitude. Compared with M1 region rTMS stimulation, the combination of M1 region and DLPFC rTMS is more effective.

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.

Marcadores Neurales como Indicadores de Funciones Predictivas en Pacientes con Traumatismo Cerebral Moderado a Severo / Neural Markers as Indicators of Predictive Functions in Patients with Moderate to Severe Brain Trauma

Introducción. El traumatismo encéfalo craneano moderado a severo (TEC-MS) es una condición compleja que cambia la estructura y función del cerebro, afectando a personas de distintas edades. Los problemas cognitivos y motores son la mayor causa de discapacidad en individuos con TEC-MS crónico. Sin embargo, muchas de estas dificultades no son visibles de inmediato clasificándose como una "Epidemia silenciosa". Las principales alteraciones reportadas por los pacientes tienen relación con problemas de la memoria, atención y lentitud psicomotora, los cuales tienen un impacto en su independencia y funcionalidad. Objetivo. Este estudio tiene por objetivo discutir y revisar la evidencia disponible acerca de la capacidad de los pacientes crónicos con TEC-MS para generar predicciones en diferentes niveles de procesamiento cerebral. Métodos. Para esto, utilizamos desde las neurociencias el modelo teórico del código predictivo para explicar las respuestas neurofisiológicas adquiridas bajo un paradigma de predicción auditiva. Esta información es complementada con el reporte de datos preliminares de sujetos con TEC-MS y sujetos control, con el fin de ilustrar los aspectos teóricos discutidos. Conclusiones. Esto podría contribuir a una mejor comprensión de los mecanismos neurales detrás de los déficits cognitivos en esta población, aportando una perspectiva que nos oriente al desarrollo de nuestras estrategias terapéuticas.

Background. Moderate to severe traumatic brain injury (TBI-MS) is a complex condition that changes the structure and function of the brain, affecting people of different ages. Cognitive and motor problems are the major cause of disability in individuals with chronic ECT-MS. However, many of these difficulties are not immediately visible, classifying them as a "Silent Epidemic." The main alterations reported by patients are related to problems with memory, attention and psychomotor slowness, which have an impact on their independence and functionality. Objetive. This study aims to discuss and review the available evidence about the ability of chronic ECT-MS patients to generate predictions at different levels of brain processing. Methods. For this, we use the theoretical model of the predictive code from neuroscience to explain the neurophysiological responses acquired under an auditory prediction paradigm. This information is complemented with the report of preliminary data from subjects with ECT-MS and control subjects, in order to illustrate the theoretical aspects discussed. Conclusions. This could contribute to a better understanding of the neural mechanisms behind cognitive deficits in this population, providing a perspective that guides us in the development of our therapeutic strategies.

Electroencefalografía básica para monitores de profundidad anestésica / Basic electroencephalography for anesthesia depth monitors

Resumen: Los monitores de profundidad anestésica permiten guiar el estado hipnótico del paciente durante la anestesia general. Debido a su sencillez, tradicionalmente se han empleado índices de profundidad anestésica, obtenidos a través del procesamiento del electroencefalograma mediante algoritmos matemáticos, para orientar la monitorización del nivel de consciencia. Sus beneficios han sido ampliamente recogidos en la literatura científica; sin embargo, no están exentos de importantes limitaciones. No todos los anestésicos actúan en las mismas dianas moleculares ni dichos índices tienen en cuenta las características propias del paciente (comorbilidades, edades extremas, etcétera). Estas limitaciones podrían reducirse si interpretamos directamente toda la información que nos ofrecen los monitores. Presentamos una revisión que describe los conceptos básicos necesarios para su valoración directa, así como su correlación con los estados de profundidad anestésica del paciente.

Abstract: Anesthesia depth monitors allow to guide the patient's hypnotic state during general anesthesia. Traditionally, anesthetic depth indices have been used due to their simplicity to guide the monitoring of the level of consciousness. They have been obtained by processing the electroencephalogram using mathematical algorithms and their benefits have been widely reported in the scientific literature. However, they are not exempt from important limitations. Neither all anesthetics act on the same molecular targets, nor these mentioned indices take into account the patient's own characteristics (comorbidities, extreme ages, etc.). These limitations could be far reduced if we are able to understand all the information provided by the monitors. We present a review describing the basic concepts necessary for its direct assessment, as well as their correlation with the patient's anesthetic depth states.

Recognition of motor imagery electroencephalogram based on flicker noise spectroscopy and weighted filter bank common spatial pattern / 生物医学工程学杂志

Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.

Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention / 生物医学工程学杂志

Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.

Research progress on attention level evaluation based on electroencephalogram signals / 生物医学工程学杂志

Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.

Advances in methods and applications of electroencephalogram microstate analysis / 生物医学工程学杂志

Electroencephalogram (EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.

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.

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.

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.

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.

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.

Electroencephalographic microstates in vestibular schwannoma patients with tinnitus / 南方医科大学学报

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.

Analysis of intermittent photic stimulation in children′s video electroencephalography / 中华实用儿科临床杂志

Objective:To explore the value of intermittent photic stimulation (IPS) in children′s video electroencephalography (EEG).Methods:The data of 8 994 children aged 1 to 18 years, who received IPS in the video EEG examination at the Liangjiang Campus of the Children′s Hospital of Chongqing Medical University from March 2021 to March 2022, were analyzed retrospectively.Patients were divided into non-epilepsy group and confirmed or suspected epilepsy group.Their IPS responses, clinical and EEG characteristics were collected.Categorical variables were expressed using frequencies and percentages.The relationship between IPS response and age was determined by Chi- square test or Fisher′ s exact probability method. Results:The median age of 8 994 children was 6.3 years.There were 2 310 (25.7%) children in the non-epileptic group, including 1 364 (59.0%) males and 946 (41.0%) females.There were 6 684 (74.3%) children in the confirmed or suspected epileptic group, including 3 842 (57.5%) males and 2 842 (42.5%) females.In the non-epileptic group, 141 cases (6.1%) had IPS photo-driving responses, and 1 case had photo paroxysmal response (PPR). In the confirmed or suspected epilepsy group, IPS photo-driving responses (2.4%) occurred in 160 cases, PPR (1.2%) in 82 cases, photo convulsion responses(0.3%) in 18 cases, and asymmetric photo-driving responses (0.2%) in 14 cases.The IPS photosensitivity responses varied among children of different ages, sexes, epileptic discharge types and seizure types in the confirmed or suspected epilepsy group.The children aged 6 to 18 years showed significantly stronger photosensitivity responses than those aged 1 to <6 years ( P<0.001). The photosensitivity activity in females was 1.9 times higher than that in males.The photosensitivity activity in patients with generalized discharges was 1.7 times more intense than that in patients with focal discharges.The photosensitivity reaction in patients with photo convulsive generalized seizures was 2.5 times stronger than that in patients with focal seizures. Conclusions:Routine standardized IPS is important for the detection of photosensitivity in children diagnosed or suspected with epilepsy.It can effectively guide the medical practice.

Effect of smart air cell mattress on sleep quality / 国际生物医学工程杂志

Objective:To investigate the effect of smart air cell mattresses on sleep quality.Methods:Twenty healthy young people were enrolled as subjects, and each subject underwent a four-night polysomnographic monitoring experiment, including two nights each on a smart air cell mattress and a general mattress. The differences in sleep quality were compared by self-assessment of sleep quality, objective sleep indicators, and electroencephalogram (EEG) spectral analysis.Results:In the comparison between the smart air cell mattress and the general mattress, the differences in self-assessment of sleep quality and objective sleep indicators were not statistically significant (all P > 0.05), but the smart air cell mattress had a slight overall advantage. The relative power of EEG in the low-frequency band and the relative power of EEG in the high-frequency band were higher in the subjects with the smart air cell mattress. Conclusions:For the healthy young population, the smart air cell mattress can positively influence sleep quality to some extent, and the change in EEG relative power indicates an increase in sleep depth.

Research progress in medical artificial intelligence and robotics / 国际生物医学工程杂志

Biomedical engineering uses engineering disciplines to solve medical-related problems efficiently and intelligently. It is a discipline that integrates multiple fields such as medicine, automation, computer science, data science, and integrated circuits, and is playing an increasingly important role in healthcare, the economy, and comprehensive national strength. In this paper, the research layout of the Center of Medical Artificial Intelligence and Robotics of the Institute of Biomedical Engineering, Chinese Academy of Medical Sciences was introduced in the field of biomedical engineering, including sound, light and electricity. The achievements in various fields under this framework were also introduced, and the development prospects and future vision of the Center in biomedical engineering were analyzed.

Effect of transcranial direct current stimulation combined with acupuncture on central and upper limb function in stroke patients based on central-peripheral-central theory / 中国康复理论与实践

ObjectiveTo explore the effect of transcranial direct current stimulation (tDCS) combined with acupuncture on central and upper limb function in stroke patients at flaccid stage based on central-peripheral-central theory. MethodsFrom September, 2018 to December, 2021, 120 patients with upper limb dysfunction after stroke in Guangdong Work Injury Rehabilitation Hospital were selected and randomly divided into control group 1 (n = 40), control group 2 (n = 40) and experimental group (n = 40). All the groups received conventional rehabilitation treatment. In addition, the control group 1 received acupuncture treatment, the control group 2 received anodal tDCS, and the experimental group received combined treatment of both, for four weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and modified Barthel Index (MBI) before and after treatment. Electroencephalograph (EEG) was used to detect brain symmetry index (BSI), and electromyography (EMG) was used to detect root mean square values (RMS) of triceps brachii, biceps brachii, extensor wrist and flexor wrist of the affected upper limbs. ResultsTwo cases in the control group 1, one in the control group 2 and one in the experimental group dropped off, respectively. After treatment, the scores of FMA-UE and MBI significantly increased in all the groups (t > 11.757, P < 0.001), and they were higer in the experimental group than in the control groups (P < 0.001); the BSI decreased in the control group 2 and the experimental group (t > 2.324, P < 0.05), and it was less in the experimental group than in the control group 2 (P < 0.05); the RMS of biceps increased in all the groups (t > 2.953, P < 0.01), and was higer in the experimental group than in the control groups (P < 0.05); the RMS of flexor wrist and triceps increased in the control group 1 and the experimental group (t > 2.230, P < 0.05), and were higher in the experimental group than in the control group 1 (P < 0.05); the RMS of wrist extensor muscle increased only in the experimental group (t = 3.350, P < 0.01). ConclusiontDCS combined with acupuncture based on central-peripheral-central theory could effectively improve the upper limb function of stroke patients at flaccid stage, with advantages in improving hemispheric asymmetry and enhancing the activation level of affected muscles.

Applications of neurofeedback training in cognitive rehabilitation / 中华行为医学与脑科学杂志

Cognitive impairment is a common dysfunction after central nervous system disease or injury, which seriously affects the daily life of patients and brings heavy economic burdens to the family and society. Neurofeedback training (NFT) based on electroencephalography (EEG) is a non-invasive method of neuroregulation, which can improve cognitive function and behavior by autonomously adjusting brain function through feedback. This paper reviews the application of EEG signal-based neurofeedback training in cognitive rehabilitation, and discusses the current problems and future development trends in this field, so as to provide new ideas for clinical research and treatment of cognitive disorders.

Clinical analysis of risk factors for adverse outcomes of neonatal seizure / 中国新生儿科杂志

Objective:To study the neurodevelopmental prognosis and risk factors for adverse outcomes of neonatal seizure.Methods:From December 2019 to November 2020, infants with neonatal seizure diagnosed in our hospital were enrolled in this retrospective study. Based on survival or not, mental development index (MDI), psychomotor development index (PDI) and seizure episodes at the age of 12 months, the infants were assigned into adverse outcome group and normal outcome group. The risk factors for adverse outcomes were statistically analyzed.Results:A total of 75 infants were enrolled,including 39 cases in adverse outcome group and 36 in normal outcome group. 69 cases showed abnormal amplitude-integrated electroencephalogram(aEEG), including 38 mildly abnormal cases,23 moderately abnormal cases and 8 severely abnormal cases, The incidences of adverse outcomes and mortality rates were significantly different ( P<0.05) among infants with different severity levels of aEEG abnormalities and the severity levels of aEEG abnormalities were positively correlated with adverse outcomes ( r=0.367, 0.471, P<0.05).Univariate analysis showed that adverse outcome group had significantly higher incidences of chorioamnionitis, seizure onset age ≤3 d, 5 min Apgar score ≤3, cranial ultrasound abnormalities, brain MR abnormalities and aEEG abnormalities than normal outcome group ( P<0.05).Logistic regression analysis showed that seizure onset age ≤3 d ( OR=3.988, 95% CI 1.376-11.674), abnormal brain MR ( OR=3.296, 95% CI 2.383-17.377) and bilirubin encephalopathy ( OR=3.792,95% CI 2.110-13.216) were independent risk factors for adverse outcomes of neonatal seizure. Conclusions:For neonatal seizure, the infants with more severe abnormal aEEG will have higher incidences of adverse outcomes and mortality. Seizure onset age ≤3 d, brain MR abnormalities and bilirubin encephalopathy were independent risk factors for adverse outcomes of neonatal seizure.