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 d...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.展开更多
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 select...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.展开更多
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 ...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.展开更多
At present,multi-channel electroencephalogram(EEG)signal acquisition equipment is used to collect motor imagery EEG data,and there is a problem with selecting multiple acquisition channels.Choosing too many channels w...At present,multi-channel electroencephalogram(EEG)signal acquisition equipment is used to collect motor imagery EEG data,and there is a problem with selecting multiple acquisition channels.Choosing too many channels will result in a large amount of calculation.Components irrelevant to the task will interfere with the required features,which is not conducive to the real-time processing of EEG data.Using too few channels will result in the loss of useful information and low robustness.A method of selecting data channels for motion imagination is proposed based on the time-frequency cross mutual information(TFCMI).This method determines the required data channels in a targeted manner,uses the common spatial pattern mode for feature extraction,and uses support vector ma-chine(SVM)for feature classification.An experiment is designed to collect motor imagery EEG da-ta with four experimenters and adds brain-computer interface(BCI)Competition IV public motor imagery experimental data to verify the method.The data demonstrates that compared with the meth-od of selecting too many or too few data channels,the time-frequency cross mutual information meth-od using motor imagery can improve the recognition accuracy and reduce the amount of calculation.展开更多
现有的脑-机接口系统大都只基于单模式的脑电特征,系统能实现的功能非常有限,从而制约了脑-机接口系统的应用。采用基于多种模式脑电信号(electroencephalogram,EEG)的脑-机接口技术来实现虚拟键鼠系统,使得被试可以利用自身的脑电信号...现有的脑-机接口系统大都只基于单模式的脑电特征,系统能实现的功能非常有限,从而制约了脑-机接口系统的应用。采用基于多种模式脑电信号(electroencephalogram,EEG)的脑-机接口技术来实现虚拟键鼠系统,使得被试可以利用自身的脑电信号控制鼠标和键盘的操作。研究了脑-机接口中常用的3种脑电信号,分别是P300波、alpha波以及稳态视觉诱发电位(steady state visual evoked potential,SSVEP),通过设计实验成功的诱发出了被试相应的特征脑电信号。利用SSVEP的脑电特征设计6频率LED闪烁刺激的虚拟鼠标系统,实现控制鼠标光标移动、单击左键和单击右键的任务;利用P300波的脑电特征设计6×6的字符矩阵虚拟键盘系统,实现字符输入的任务;利用被试自主闭眼增强alpha波的脑电特征,实现鼠标和键盘应用切换的任务。研究了适宜这3种脑电特征的最佳测量电极组合及模式识别算法,使得对3种脑电信号的识别正确率均达到了85%以上。测试结果显示,文中设计的基于多模式EEG的脑-机接口虚拟键鼠系统能有效地实现鼠标控制以及键盘输入的任务。展开更多
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 ...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.展开更多
Based on the variations of wavelet transform modulus maxima at multi-scales, the singularity of chaotic signals are studied, and the singularity of these signals are measured by the Lipschitz exponent.In the meantime,...Based on the variations of wavelet transform modulus maxima at multi-scales, the singularity of chaotic signals are studied, and the singularity of these signals are measured by the Lipschitz exponent.In the meantime, a nonlinear method is proposed based on the higher order statistics, on the other aspect, which characterizes the higher order singular spectrum (HOSS) of chaotic signals. All computations are done with Lorenz attractor, Rossler attractor and EEG(electroencephalogram) time series and the comparisions among these results are made. The experimental results show that the Lipschitz exponents and the higher order singular spectra of these signals are significantly different from each other, which indicates these methods are effective for studing the singularity of chaotic signals.展开更多
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 healthcared...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.展开更多
文摘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.
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China(No.51775325)National Key R&D Program of China(No.2018YFB1309200)the Young Eastern Scholars Program of Shanghai(No.QD2016033).
文摘At present,multi-channel electroencephalogram(EEG)signal acquisition equipment is used to collect motor imagery EEG data,and there is a problem with selecting multiple acquisition channels.Choosing too many channels will result in a large amount of calculation.Components irrelevant to the task will interfere with the required features,which is not conducive to the real-time processing of EEG data.Using too few channels will result in the loss of useful information and low robustness.A method of selecting data channels for motion imagination is proposed based on the time-frequency cross mutual information(TFCMI).This method determines the required data channels in a targeted manner,uses the common spatial pattern mode for feature extraction,and uses support vector ma-chine(SVM)for feature classification.An experiment is designed to collect motor imagery EEG da-ta with four experimenters and adds brain-computer interface(BCI)Competition IV public motor imagery experimental data to verify the method.The data demonstrates that compared with the meth-od of selecting too many or too few data channels,the time-frequency cross mutual information meth-od using motor imagery can improve the recognition accuracy and reduce the amount of calculation.
文摘现有的脑-机接口系统大都只基于单模式的脑电特征,系统能实现的功能非常有限,从而制约了脑-机接口系统的应用。采用基于多种模式脑电信号(electroencephalogram,EEG)的脑-机接口技术来实现虚拟键鼠系统,使得被试可以利用自身的脑电信号控制鼠标和键盘的操作。研究了脑-机接口中常用的3种脑电信号,分别是P300波、alpha波以及稳态视觉诱发电位(steady state visual evoked potential,SSVEP),通过设计实验成功的诱发出了被试相应的特征脑电信号。利用SSVEP的脑电特征设计6频率LED闪烁刺激的虚拟鼠标系统,实现控制鼠标光标移动、单击左键和单击右键的任务;利用P300波的脑电特征设计6×6的字符矩阵虚拟键盘系统,实现字符输入的任务;利用被试自主闭眼增强alpha波的脑电特征,实现鼠标和键盘应用切换的任务。研究了适宜这3种脑电特征的最佳测量电极组合及模式识别算法,使得对3种脑电信号的识别正确率均达到了85%以上。测试结果显示,文中设计的基于多模式EEG的脑-机接口虚拟键鼠系统能有效地实现鼠标控制以及键盘输入的任务。
文摘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.
基金Science Foundation of Educational Commission of Fujian Province of China (Grant NO:JAO04238)
文摘Based on the variations of wavelet transform modulus maxima at multi-scales, the singularity of chaotic signals are studied, and the singularity of these signals are measured by the Lipschitz exponent.In the meantime, a nonlinear method is proposed based on the higher order statistics, on the other aspect, which characterizes the higher order singular spectrum (HOSS) of chaotic signals. All computations are done with Lorenz attractor, Rossler attractor and EEG(electroencephalogram) time series and the comparisions among these results are made. The experimental results show that the Lipschitz exponents and the higher order singular spectra of these signals are significantly different from each other, which indicates these methods are effective for studing the singularity of chaotic signals.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP2/42/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R136)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘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.