Approach for epileptic EEG detection based on gradient boosting

The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.

基于有效注意力和GAN结合的脑卒中EEG增强算法

在基于脑电的卒中分类诊断任务中,以卷积神经网络为基础的深度模型得到广泛应用,但由于卒中类别病患样本数量少,导致数据集类别不平衡,降低了分类精度。现有的少数类数据增强方法大多采用生成对抗网络(GAN),生成效果一般,虽然可通过引入缩放点乘注意力改善样本生成质量,但存储及运算代价往往较大。针对此问题,构建一种基于线性有效注意力的渐进式数据增强算法LESA-CGAN。首先,算法采用双层自编码条件生成对抗网络架构,分别进行脑电标签特征提取及脑电样本生成,并使生成过程逐层精细化;其次,通过在编码部分引入线性有效自注意力(LESA)模块,加强脑电的标签隐层特征提取,并降低网络整体的运算复杂度。消融与对比实验结果表明,在合理的编码层数与生成数据比例下,LESA-CGAN与其他基准方法相比计算资源占用较少,且在样本生成质量指标上实现了10%的性能提升,各频段生成的脑电特征样本均更加自然,同时将病患分类的准确率和敏感度提高到了98.85%和98.79%。

Epileptic seizure detection using EEG signals and extreme gradient boosting

The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.

An Efficient Method for Epileptic Seizure Detection in Long-Term EEG Recordings

Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

We are here to present a new method for the classification of epileptic seizures from electroencephalogram(EEG) signals.It consists of applying empirical mode decomposition(EMD) to extract the most relevant intrinsic mode functions(IMFs) and subsequent computation of the Teager and instantaneous energy,Higuchi and Petrosian fractal dimension,and detrended fluctuation analysis(DFA) for each IMF.We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures,even with segments of six seconds and a smaller number of channels(e.g.,an accuracy of0.93 using five channels).We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects,after reducing the number of instances,based on the k-means algorithm.

抑郁症EEG诊断的类脑学习模型

抑郁症是一种全球性精神疾病,传统诊断方法主要依靠量表与医生的主观评估,无法有效识别症状,甚至存在误诊的风险。基于生理信号的深度学习辅助诊断有望改善传统缺乏生理学依据的方法。然而,传统深度学习方法依赖巨大算力,且大多是端到端的网络学习。这些学习方法也缺乏生理可解释性,限制了辅助诊断临床应用。提出一种用于抑郁症脑电图(electroencephalogram,EEG)诊断的类脑学习模型,在功能层面,构建脉冲神经网络对抑郁症与健康个体进行分类,精度超过97.5%,相比深度卷积方法,脉冲方法降低了能耗;在结构层面,利用复杂网络建立脑连接的空间拓扑并分析其图特征,找出了抑郁症个体潜在的脑功能连接异常机制。

EEG光子处理器——基于衍射光子计算单元的癫痫发作检测

Electroencephalography(EEG)analysis extracts critical information from brain signals,enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces.However,performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices,especially with large neural network models.Herein,we propose an EEG opto-processor based on diffractive photonic computing units(DPUs)to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures.The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification,which monitors the brain state to identify symptoms of an epileptic seizure.We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets,that is,the Children’s Hospital Boston(CHB)–Massachusetts Institute of Technology(MIT)extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets,with excellent computing performance results.Along with the channel selection mechanism,both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis.Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.

Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction

This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles selected present important findings including new experimental results and theoretical studies.

Correlation between level of serum prolactin and epileptic discharges of electroencephalogram from 24 to 36 hours after epileptic onset

BACKGROUND： Researchers discovered that serum prolactin could rise following an epileptic seizure. The prolactin level might reach three times more than basic level within 30 minutes and decrease to the normal value 2 hours after the seizure occurred. The mechanism might result in an increase of serum prolactin concentrations with the activation of the hypothalamic-pituitary axis. OBJECTIVE：To probe into the correlation between changes of serum prolactin and incidence of epileptic discharges of electroencephalogram （EEG） at 24-36 hours after epileptic onset of patients with secondary epilepsy. DESIGN ： Clinical observational study SEI-FING： Department of Neurology, First Hospital affiliated to Soochow University PARTICIPANTS： A total of 21 patients with secondary epilepsy were selected from the Department of Neurological Emergency or Hospital Room of the First Hospital affiliated to Soochow University from November 2005 to April 2006. There were 14 males and 7 females aged from 25 to 72 years. All patients met International League Anti-epileptic （ILAE） criteria in 1981 for secondary generalized tonic clonic seizure through CT or MRI and previous EEG. All patients were consent. Primary diseases included cerebral trauma （3 cases）, tumor （2 cases）, stroke （7 cases） and intracranial infeion （9 cases）. METHODS ： Venous blood of all patients was collected at 24-36 hours after epileptic onset. Serum prolactin kit （Beckman Coulter, Inc in USA） was used to measure value of serum prolactin according to kit instruction. Then, value of serum prolactin was compared with the normal value （male： 2.64-13.13 mg/L; female： 3.34- 26.72 mg/L）; meanwhile, EEG equipment （American Nicolet Incorporation） was used in this study. MAIN OUTCOME MEASURES ： ① Abnormal rate of serum prolactin of patients with secondary epilepsy; ②Comparison between normal and abnormal level of serum prolactin and incidence of EEG epileptic discharge of patients with secondary epilepsy. RESULTS：All 21 patients with secondary epilepsy were involved in the final analysis. ① Results of serum prolactin level： Among 21 patients with of secondary epilepsy, 10 of them had normal serum prolactin and 11 had abnormal one, and the abnormal rate was 52% （11/21）. ② Detecting results of EEG： EEG results showed that 6 cases were normal and 15 were abnormal, and the abnormal rate was 71% （15/21）. The symptoms were sharp wave, spike wave or sharp slow wave, spike slow wave of epileptic discharges in 8 cases, which was accounted for 38%. ③ Correlation between abnormality of serum prolactin and EEG epileptic wave： Eleven cases had abnormal serum prolactin, and the incidence was 64% （7/11）, which was higher of epileptic wave than that of non-epileptic wave [36% （4/11）, P 〈 0.05]; however, 10 cases had normal serum prolactin, and the incidence was 10% （1/10）. Epileptic wave was lower than non-epileptic wave [90% （9/10）, P 〈 0.01]. CONCLUSION ： The level of serum prolactin of patients with secondary epilepsy is abnormally increased at 24- 36 hours after epileptic onset; in addition, incidence of epileptic discharge is also increased remarkably.

Synthesized Multi-Method to Detect and Classify Epileptic Waves in EEG

In order to sufficiently exploit the advantages of different signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER),a synthesized multi-method was introduced to detect and classify the epileptic waves in the EEG data. Using this method, at first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves,and at last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering which are usually non-placid and nonlinear.

A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. Substantial data is generated by the EEG recordings of ambulatory recording systems, and detection of epileptic activity requires a time-consuming analysis of the complete length of the EEG time series data by a neurology expert. A variety of automatic epilepsy detection systems have been developed during the last ten years. In this paper, we investigate the potential of a recently-proposed statistical measure parameter regarded as Sample Entropy (SampEn), as a method of feature extraction to the task of classifying three different kinds of EEG signals (normal, interictal and ictal) and detecting epileptic seizures. It is known that the value of the SampEn falls suddenly during an epileptic seizure and this fact is utilized in the proposed diagnosis system. Two different kinds of classification models, back-propagation neural network (BPNN) and the recently-developed extreme learning machine (ELM) are tested in this study. Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed.

Low-frequency repetitive transcranial magnetic simulation prevents chronic epileptic seizure

Although low-frequency repetitive transcranial magnetic simulation can potentially treat epilepsy, its underlying mechanism remains unclear. This study investigated the influence of low-frequency re-petitive transcranial magnetic simulation on changes in several nonlinear dynamic electroenceph-alographic parameters in rats with chronic epilepsy and explored the mechanism underlying repeti-tive transcranial magnetic simulation-induced antiepileptic effects. An epilepsy model was estab-lished using lithium-pilocarpine intraperitoneal injection into adult Sprague-Dawley rats, which were then treated with repetitive transcranial magnetic simulation for 7 consecutive days. Nonlinear elec-electroencephalographic parameters were obtained from the rats at 7, 14, and 28 days post-stimulation. Results showed significantly lower mean correlation-dimension and Kolmogo-rov-entropy values for stimulated rats than for non-stimulated rats. At 28 days, the complexity and point-wise correlation dimensional values were lower in stimulated rats. Low-frequency repetitive transcranial magnetic simulation has suppressive effects on electrical activity in epileptic rats, thus explaining its effectiveness in treating epilepsy.

Epilepsy versus non-epileptic attack disorder: A diagnostic and therapeutic challenge

Epilepsy and non-epileptic attack disorder (NEAD) share a vast number of clinical features, however the aetiology and management are very different. Video-EEG is the gold standard diagnostic tool and relies on the occurrence of seizure activity during assessment to make a diagnosis. Added complexity arises from the co-existence of epilepsy and NEAD, occurring in a significant proportion of patients. Comprehensive assessment and investigation is therefore required to prevent gross mistreatment in this diagnostically difficult subgroup. We present a case of NEAD with co-existing epilepsy and the challenges that this may present in clinical practice.

THE EFFECT OF ACUPUNCTURING ACUPOINTS ON THE CHANGE OF ELECTROENCEPHALOGRAM (EEG) IN ENDOTOXIC SHOCKED RATS

In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/46 cases.Simultaneously,there was a markeddrop in Bp,P【0.05.Following the shocked time prolonged,dysrhythmia was getting severe。AfterEA”Rengzhong"(n=14)or“Zusanli”(n=12),BP was significantly increased(P【0.05),anddysrhythmia of EEG showed clear improvement in most of the rats。There was a close relation be-tween the changes of EEG and BP,the change of EEG had a direct bearing on the change of BP.

基于改进Renyi熵算法的EEG心算任务识别

结构熵是度量网络复杂度的重要手段,为了弥补传统结构熵仅仅关注网络单一特性的问题,提出了一种改进Renyi熵算法来研究心算任务下的EEG脑网络,引入了两个重要网络属性——分形维数和介数中心性来提高网络复杂性的度量能力。之后,基于心算EEG数据计算两两电极间的相位锁定值(PLV),构建了复杂脑网络,并进行复杂度分析。结果表明,在α频段,心算状态下额叶与顶枕叶的脑同步性低于休息状态,心算状态的脑网络复杂性高于休息状态。利用支持向量机(SVM)实现了休息、心算状态的识别,算法识别准确率达到了88.42%。

Statistical analysis of Epileptic activities based on Histogram and Wavelet-Spectral entropy

Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analysed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose the EEG into its sub-bands. Applying histogram and Spectral entropy approaches to the EEG sub-bands, normal and abnormal states of brain can be distinguished with more than 99% probability.

Intranasal ginsenoside Rb1 protects pentyl⁃enetetrazole-induced epileptic mice

OBJECTIVE To evaluate whether ginsenoside Rb1 has antiepileptic effects on pen⁃tylenetetrazole(PTZ)-induced epileptic mice via intranasal therapeutic administration.METHODS Rb1 monoclonal antibody was used to observe the distribution of Rb120 mg·kg-1 in mouse brain tissues under different administration routes and to explore the feasibility of intranasal Rb1.PTZ was injected intraperitoneally into healthy ICR mice every 48 hours to construct a tonic-clonic epileptic model.Then Rb120 or 40 mg·kg-1 or valproate 300 mg·kg-1 or saline was administered intranasally for 30 d,and PTZ was continued every five days to imitate occa⁃sional convulsions in the clinic.Racine scale(RCS)and wireless electroencephalogram(EEG)monitoring were used to assess the presence and severity of seizure.Immunofluorescence(IF)was performed after drug treatment to evalu⁃ate the effect of Rb1 on brain neuron,microglia and astrocyte in epileptic mice.RESULTS Rb1 had specific binding with anti-Rb1 in the brain under different administration routes,and intrana⁃sal Rb1 was able to enter the brain and play a therapeutic role(P<0.01).PTZ-injured mice pre⁃sented body mass loss,higher seizure stage and shorter seizure latency.At the same time,epilep⁃tic waves,mainly spikes,were detected by wire⁃less EEG.Compared with PTZ group,intranasal Rb1 increased mice weight(P<0.01)and seizure latency(P<0.05),reduced seizure stage(P<0.01)and EEG spikes.In addition,Rb1 significantly reduced neuron loss(P<0.01)indicated by NeuN staining and decreased the number of acti⁃vated microglia(P<0.01)indicated by Iba-1 staining in the cortex and CA1 area of hippocam⁃pus.Moreover,Rb1 reduced the decrease of GLT-1 and GS expression(P<0.05)induced by PTZ.CONCLUTION Intranasal Rb1 has anti-epi⁃leptic effects on PTZ mice.Moreover,Intranasal Rb1 affects the functions of neurons,astrocytes and microglia through regulating the expression of GLT and GS in astrocytes,which may be related to its anti-epileptic effect.

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and classification.The proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably enhanced.The experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

3DMKDR:3D Multiscale Kernels CNN Model for Depression Recognition Based on EEG

Depression has become a major health threat around the world,especially for older people,so the effective detection method for depression is a great public health challenge.Electroencephalogram(EEG)can be used as a biomarker to effectively explore depression recognition.Motivated by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel,this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition(3DMKDR),which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals.A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode matrix.By the major depressive disorder(MDD)and the multi-modal open dataset for mental-disorder analysis(MODMA)datasets,the experiment shows that the accuracies of depression recognition are up to99.86%and 98.01%in the subject-dependent experiment,and 95.80%and 82.27%in the subjectindependent experiment,which are higher than alternative competitive methods.The experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.