Multichannel interictal spike activity detection using time–frequency entropy measure

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time–Frequency (T–F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T–F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T–F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.     

Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations

An automated sleep staging based on analyzing long-range time correlations in EEG is proposed. These correlations, indicating time-scale invariant property or self-similarity at different time scales, are known to be salient dynamical characteristics of stage succession for a sleeping brain even when the subject suffers a destructive disorder such as Obstructive Sleep Apnea (OSA). The goal is to extract a set of complementary features from cerebral sources mapped onto the scalp electrodes or from a number of denoised EEG channels. For this purpose, source localization/extraction and noise reduction approaches based on Independent Component Analysis were used prior to correlation analysis. Feature extracted segments were then classified in one of the five classes including WAKE, STAGE1, STAGE2, SWS and REM via an ensemble neuro-fuzzy classifier. Some techniques were employed to improve the classifier’s performance including Scaled Conjugate Gradient Method to speed up learning the ANFIS classifiers, a pruning algorithm to eliminate irrelevant fuzzy rules and the 10-fold cross-validation technique to train and test the system more efficiently. The performance of classification for two strategies including (1) feature extraction from effective cerebral sources and (2) feature extraction from selected channels of denoised EEG signals was compared and contrasted in terms of training errors and test accuracies. The first and second strategies achieved 92.23 and 88.74% agreement with human expert respectively which indicates the effectiveness of the staging system based on cerebral sources of activity. Our results further indicate that the misclassification rates were almost below 10%. The proposed automated sleep staging system is reliable due to the fact that it is based on the underlying dynamics of sleep staging which is less likely to be affected by sleep fragmentations occurred repeatedly in OSA.     

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha–beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.     

A stable feature extraction method in classification epileptic EEG signals

Epilepsy is one of the most common neurological disorders. Electroencephalogram (EEG) signals are generally employed in diagnosing epilepsy. Therefore, extracting relevant features from EEG signals is one of the major tasks in an accurate diagnosis. In this study, the local ternary patterns, which is an image processing method, was improved in order to extract robust features from epileptic EEG signals. The EEG signals that were recorded by the Department of Etymology in the Bonn University were employed in the evaluation and validation of the proposed approach. Low and up features, which were extracted by the proposed one-dimensional ternary patterns, were classified by some machine learning methods such that support vector machine, functional trees, random forest (RF), Bayes networks (BayesNet), and artificial neural network, while the highest accuracies were obtained by RF. Achieved accuracies were found successful according to the current literature.     

Performance of epileptic single-channel scalp EEG classifications using single wavelet-based features

Classification of epileptic scalp EEGs are certainly ones of the most crucial tasks in diagnosis of epilepsy. Rather than using multiple quantitative features, a single quantitative feature of single-channel scalp EEG is applied for classifying its corresponding state of the brain, i.e., during seizure activity or non-seizure period. The quantitative features proposed are wavelet-based features obtained from the logarithm of variance of detail and approximation coefficients of single-channel scalp EEG signals. The performance on patient-dependent based epileptic seizure classifications using single wavelet-based features are examined on scalp EEG data of 12 children subjects containing 79 seizures. The 4-fold cross validation is applied to evaluate the performance on patient-dependent based epileptic seizure classifications using single wavelet-based features. From the computational results, it is shown that the wavelet-based features can provide an outstanding performance on patient-dependent based epileptic seizure classification. The average accuracy, sensitivity, and specificity of patient-dependent based epileptic seizure classification are, respectively, 93.24%, 83.34%, and 93.53%.     

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.     

视频脑电图在小儿癫痫诊断中的应用价值

目的通过视频脑电图（Video-EEG）在癫痫患儿中的临床应用,判断其发作类型和定位,提高对癫痫患儿的诊断水平。方法采用SOLARl848定量数字视频脑电图仪,对407例发作性疾病患者进行连续监测（包括清醒、睡眠期）,分析临床发作和异常放电的关系、癫痫分型与异常放电的关系等。结果407例中101例（25%）在发作问期或发作期发现癫痫样放电,57例（14%）为非癫痫样发作,79例（19％）监测到癫痫样放电与临床发作同步出现,170例（41170）监测中未见发作,且发作间期无癫痫样放电,确诊的病例中例60％重新明确癫痫分型,42％患儿找出了放电的起源。结论视频脑电图在排除非癫痫发作,明确发作类型和定位,评价脑电图与临床关系,提供可靠正确的依据,提高了癫痫的临床诊断水平。     

脑电图在癫痫诊断和治疗中的研究进展

癫痫主要是指大脑内部神经元突然兴奋放电而导致的患者出现暂时功能障碍。癫痫主要是在夜间发作,并且患者自身对于癫痫发作过程没有明显印象。医生主要依据患者家属以及患者癫痫发作时其他在场人员对患者的病情进行诊断,但是这种诊断方法准确性较低。近些年随着脑电图技术的不断成熟和完善,人们发现脑电图对于癫痫症状具有良好的诊断价值,原发性癫痫和继发性癫痫在发作时,脑电图图像上均具有明确的相应。因此近些年人们关于脑电图在癫痫诊断和治疗中的应用开展了大量的研究工作,取得了一些新认识和新进展,本文对这些进展进行综述,以期为临床癫痫诊断和治疗提供借鉴。     

Prediction of CTL epitopes using QM, SVM and ANN techniques

Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes. In this study, a systematic attempt has been made to develop a direct method for predicting CTL epitopes from an antigenic sequence. This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). This method has been trained and tested on non-redundant dataset of T cell epitopes and non-epitopes that includes 1137 experimentally proven MHC class I restricted T cell epitopes. The accuracy of QM-, ANN- and SVM-based methods was 70.0, 72.2 and 75.2%, respectively. The performance of these methods has been evaluated through Leave One Out Cross-Validation (LOOCV) at a cutoff score where sensitivity and specificity was nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL epitopes. The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method. We also demonstrated through subgroup analysis that our methods can discriminate between T-cell epitopes and MHC binders (non-epitopes). In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes.     

Multi-class EEG classification of motor imagery signal by finding optimal time segments and features using SNR-based mutual information

The electroencephalogram signals are used to distinguish different motor imagery tasks in brain–computer interfaces. In most studies, in order to classify the EEG signals recorded in a cue-guided BCI paradigm, time segments for feature extraction after the onset of the visual cue were selected manually. In addition, in these studies the authors have selected a single identical time segment for different subjects. The present study emphasized on the inter-individual variability and difference between different motor imagery tasks as the potential source of erroneous results and used mutual information and the subject specific time interval to overcome this problem. More specifically, a new method was proposed to automatically find the best subject specific time intervals for the classification of four-class motor imagery tasks by using MI between the BCI input and output. Moreover, the signal-to-noise ratio was used to calculate the MI values, while the MI values were used as feature selection criteria to select the discriminative features. The time segments and the best discriminative features were found by using training data and used to assess the evaluation data. Furthermore, the CSP algorithm was used to extract signal features. The dataset 2A of BCI competition IV used in this study consisted of four different motor imagery signals, which were obtained from nine different subjects. One Vs One decomposition scheme was used to deal with the multi-class nature of the problem. The MI values showed that the obtained time segments not only varied between different subjects but also varied between different classifiers of different pair of classes. Finally, the results suggested that the proposed method was efficient in classifying multi-class motor imagery signals as compared to other classification strategies proposed by the other studies.     

脑血流灌注显像对难治性发作性癫痫诊断的研究

目的 评价99mTc-单光子发射计算机断层(SPECT)脑血流灌注显像对难治性发作性癫痫的诊断价值,并分析其影像学特点.方法 对难治性发作性癫痫患者共238例分别进行SPECT、MRI、CT和脑电图(EEG)检查,并对诊断结果进行统计分析和比较.结果 SPECT对强直性发作、失神发作、单纯部分性发作和复杂部分性发作的癫痫病灶检出率分别为85.7%、95.7%、89.8%和90.9%,显著高于其他检查方法(P＜0.01);接受SPECT病灶定位诊断进行伽马刀治疗的36例患者中,23例随访1年以上,有效率为78.26%.结论 SPECT对癫痫病灶的检出率较高,对立体定向放射外科靶区的确定具有重要的临床价值.     

A new automated assessment method for contrast–detail images by applying support vector machine and its robustness to nonlinear image processing

The automated contrast–detail (C–D) analysis methods developed so-far cannot be expected to work well on images processed with nonlinear methods, such as noise reduction methods. Therefore, we have devised a new automated C–D analysis method by applying support vector machine (SVM), and tested for its robustness to nonlinear image processing. We acquired the CDRAD (a commercially available C–D test object) images at a tube voltage of 120 kV and a milliampere-second product (mAs) of 0.5–5.0. A partial diffusion equation based technique was used as noise reduction method. Three radiologists and three university students participated in the observer performance study. The training data for our SVM method was the classification data scored by the one radiologist for the CDRAD images acquired at 1.6 and 3.2 mAs and their noise-reduced images. We also compared the performance of our SVM method with the CDRAD Analyser algorithm. The mean C–D diagrams (that is a plot of the mean of the smallest visible hole diameter vs. hole depth) obtained from our devised SVM method agreed well with the ones averaged across the six human observers for both original and noise-reduced CDRAD images, whereas the mean C–D diagrams from the CDRAD Analyser algorithm disagreed with the ones from the human observers for both original and noise-reduced CDRAD images. In conclusion, our proposed SVM method for C–D analysis will work well for the images processed with the non-linear noise reduction method as well as for the original radiographic images.     

Glossokinetic potential based tongue–machine interface for 1-D extraction

The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue–human computer interface or tongue–machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue–machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22–34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain–computer interfaces which have significant inadequacies arisen from the EEG signals.     

一种基于瞬态视觉诱发电位的实时脑机接口系统的研制

目的：研制一种基于瞬态视觉诱发电位的实时脑机接口系统,该系统可用于残障人士的自我康复。方法：利用穿戴式技术与无线网络技术设计相结合,穿戴式脑电采集头带将脑电信号和刺激同步信标信号利用紫蜂技术（Z追Bee）无线发送到计算机,计算机根据同步信标信号产生刺激图案,并实现脑电中诱发电位信号的实时检测、特征提取和识别分类．输出相应控制命令。结果：10名受试者的实验结果显示,该系统对4个图案目标的识别的正确率可达92％。结论：该系统结构紧凑、使用方便、准确率高,该系统的研制对脑机接口走向实用化做了有意义的探索。     

动态脑电图监测对癫痫诊断和鉴别诊断的意义

目的探索24h动态脑电图(AEEG)对癫痫诊断和鉴别诊断的意义。方法对225例临床确诊为癫痫及121例非癫痫性发作性疾病患者的AEEG诊断行回顾性分析。结果AEEG与EEG的异常率及痫样放电检出率均有非常显著性差异(P<0.01)。AEEG描记中睡眠期痫样放电检出比清醒期增加15.6%,且57.8%出现在NREM1～2期。癫痫组与非癫痫组两组之间异常率和痫样放电检出率均有显著性差异(P<0.05)。睡眠纺锤波的缺如及减弱,对癫痫患者具有重要的脑电图意义。AEEG可以记录到癫痫发作起始时的波形变化。结论AEEG对癫痫的诊断、鉴别诊断和治疗有重要意义。     

24h动态脑电图监测对儿童癫痫的诊断价值

目的探讨24h动态脑电图在儿童癫痫与非癫痫发作性疾病诊断与鉴别诊断中的价值。方法2004年11月~2007年9月在我院癫痫专科门诊就诊的具有发作性症状儿童病例845例,其中拟诊癫痫554例,非癫痫性发作性疾病291例,全部病例均作常规脑电图(EEG)和24h动态脑电图(AEEG)检查。结果拟诊癫痫554例,经24h动态脑电图检查,结合其临床表现,确诊为癫痫491例;而拟诊非癫痫性发作性疾病291例,经24h动态脑电图检查,结合其临床表现,276例被除外癫痫。结论24hAEEG是鉴别诊断儿童癫痫与非癫痫发作性疾病的可靠检查方法。     

基于支持向量机的乳腺病变检测

目的：利用支持向量机(SVM)对乳腺X光片图像中的病变区域进行检测和分类，识别出含钙化点区域和肿瘤区域。方法：在对目标区域加特定方形窗处理后，提取直接参数、灰度共生矩阵参数和频域参数，分别作为SVM分类器的输入进行训练和测试，并与3种参数同时输入的结果进行比较。结果：单独使用直接参数，频域参数和灰度共生矩阵参数的分类结果分别是92．28％、90.35％和91．12％，而3种参数结合的结果是99．23％。结论：所提取的3种参数可以较好地反映含钙化点区域、肿瘤区域和正常区域的特征，使用SVM分类器进行分类后取得了很好的效果，基本上可以准确识别出3种区域。     

基于SVM的尿液粒子识别算法研究

本文将支持向量机的算法引入到尿沉渣有形成分的分类问题上.在提取特征的基础上,采用交叉验证法和精度等高线图进行核函数及参数的选择.根据支持向量机和数据集特点,设计出由两级分类器集成的支持向量机多分类器.得到了相应的混淆矩阵.临床实验数据分类评测以及与神经网络方法比较结果表明,提出的算法具有一定的优势.     

Data selection in EEG signals classification

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.     

Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method

Objectives

Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of high-dimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented.

Methods

The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA).

Results

The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies.

Conclusion

The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework.