Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier

Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p < 0.05) and z-score (?1.96 > z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey’s range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.     

Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.     

An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters

Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.     

Neural Networks with Periodogram and Autoregressive Spectral Analysis Methods in Detection of Epileptic Seizure

Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. The purpose of this work was to investigate the performance of the periodogram and autoregressive (AR) power spectrum methods to extract classifiable features from human electroencephalogram (EEG) by using artificial neural networks (ANN). The feedforward ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. We present a method for the automatic comparison of epileptic seizures in EEG, allowing the grouping of seizures having similar overall patterns. Each channel of the EEG is first broken down into segments having relatively stationary characteristics. Features are then calculated for each segment, and all segments of all channels of the seizures of a patient are grouped into clusters of similar morphology. This clustering allows labeling of every EEG segment. Examples from 5 patients with scalp electrodes illustrate the ability of the method to group seizures of similar morphology. It was observed that ANN classification of EEG signals with AR preprocessing gives better results, and these results can also be used for the deduction of epileptic seizure.     

A New Approach for Concealed Information Identification Based on ERP Assessment

Recently, numerous concealed information test (CIT) studies have been done with event related potential (ERP) for its sufficient validity in applied use. In this study, a new approach based on wavelet coefficients (WCs) and kernel learning algorithm is proposed to identify concealed information. Totally 16 subjects went through the designed CIT paradigm and the multichannel electroencephalogram (EEG) signals were recorded. Then, the high-dimensional WCs of ERP in delta, theta, alpha and beta rhythms were extracted. For the analysis of the data, kernel principle component analysis (KPCA) and a support vector machines (SVM) classifier are implemented. The results show that WCs features are significant differences between concealed information and irrelevant information (P?<?0.05). The KPCA is able to effectively reduce feature dimensionalities and increase generalization performance of SVM. A high accuracy (93.6%) in recognizing concealed information and irrelevant information is achieved, which indicates the combination KPCA and SVM may provide a useful tool for detecting the concealed information.     

Computer-Based Identification of Breast Cancer Using Digitized Mammograms

High-quality mammography is the most effective technology presently available for breast cancer screening. Efforts to improve mammography focus on refining the technology and improving how it is administered and X-ray films are interpreted. Computer-based intelligent system for identification of the breast cancer can be very useful in diagnosis and its management. This paper presents a comparative approach for classification of three kinds of mammogram namely normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers namely: the feedforward architecture neural network classifier, and Gaussian mixture model (GMM) for comparison.. Our protocol uses, 360 subjects consisting of normal, benign and cancer breast conditions. We demonstrate a sensitivity and specificity of more than 90% for these classifiers.     

Backpropagation Artificial Neural Network Detects Changes in Electro-Encephalogram Power Spectra of Syncopic Patients

This paper presents an effective application of backpropagation artificial neural network (ANN) in differentiating electroencephalogram (EEG) power spectra of syncopic and normal subjects. Digitized 8-channel EEG data were recorded with standard electrodes placement and amplifier settings from five confirmed syncopic and five normal subjects. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation and analysis of changes due to syncope. The results revealed significant increase in percentage δ and α (p<0.5 or better) with significant reduction in percentage θ activity (p<0.05). The backpropagation ANN used for classification contains 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from syncopic EEG power spectra and the normal EEG power spectra with an accuracy of 88.87% (85.75% for syncopic and 92% for normal). Certificate of Originality—This is to certify that the article submitted for publication in ‘Journal of Medical Systems’ has not been publ-ished, nor is being considered for publication, elsewhere. (Rakesh Kumar Sinha)     

Research and applications: N-gram support vector machines for scalable procedure and diagnosis classification,with applications to clinical free text data from the intensive care unit

Background

Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times.

Objective

To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment.

Materials and methods

We selected notes from 2001–2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx).

Results

Our classifier accurately identified mechanical ventilation (accuracy=0.982, F₁=0.954) and phototherapy use (accuracy=0.940, F₁=0.912), as well as jaundice (accuracy=0.898, F₁=0.884) and ICH diagnoses (accuracy=0.938, F₁=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task.

Discussion

Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures.

Conclusions

SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods.     

A Special Electroencephalography Pattern Might Help in the Diagnosis of Antibody-positive Encephalitis

Background:

Patterns observed with electroencephalography (EEG) for patients who have encephalitis are usually known as generalized nonspecific cerebral abnormalities. The aim of this study was to investigate the presence of a special EEG pattern for patients with encephalitis and to explore features related to this special and uncommon pattern.

Methods:

EEG monitoring was performed for every patient aged >15 years with encephalitis who was hospitalized between December 2011 and March 2014. Clinical characteristics and EEG recordings were collected and evaluated.

Results:

Fifty-two patients with encephalitis were enrolled in our study with a 2-h median EEG recording time, and extreme beta brushes (EBBs) occurred in 17 patients (32.7%). Its presence was not significant regarding gender, age, psychiatric medication use, EEG rhythmic disorganization (P > 0.05). Nevertheless, among the patients with EBBs, nine patients (52.9%) had epileptic seizures that had a significant detection rate (P < 0.05); moreover, the cerebrospinal fluid (CSF) or serum of 15 patients (88.2%) with EBBs was positive for antibodies (P < 0.05). Four patients (23.5%) who had EBB had corresponding regional distributions on neuroimaging scans. The EBBs completely correlated with the regional distributions of spike discharges for four patients.

Conclusion:

EBB is a special EEG pattern for patients with encephalitis, especially those with epileptic seizures or who have antibody-positive CSF/serum, and should be considered in clinical practice.     

Research and applications: Learning regular expressions for clinical text classification

Objectives

Natural language processing (NLP) applications typically use regular expressions that have been developed manually by human experts. Our goal is to automate both the creation and utilization of regular expressions in text classification.

Methods

We designed a novel regular expression discovery (RED) algorithm and implemented two text classifiers based on RED. The RED+ALIGN classifier combines RED with an alignment algorithm, and RED+SVM combines RED with a support vector machine (SVM) classifier. Two clinical datasets were used for testing and evaluation: the SMOKE dataset, containing 1091 text snippets describing smoking status; and the PAIN dataset, containing 702 snippets describing pain status. We performed 10-fold cross-validation to calculate accuracy, precision, recall, and F-measure metrics. In the evaluation, an SVM classifier was trained as the control.

Results

The two RED classifiers achieved 80.9–83.0% in overall accuracy on the two datasets, which is 1.3–3% higher than SVM''s accuracy (p<0.001). Similarly, small but consistent improvements have been observed in precision, recall, and F-measure when RED classifiers are compared with SVM alone. More significantly, RED+ALIGN correctly classified many instances that were misclassified by the SVM classifier (8.1–10.3% of the total instances and 43.8–53.0% of SVM''s misclassifications).

Conclusions

Machine-generated regular expressions can be effectively used in clinical text classification. The regular expression-based classifier can be combined with other classifiers, like SVM, to improve classification performance.     

基于肌音信号的头部动作模式识别

肌音信号（MMG）是一种肌肉收缩时发出的低频信号,通过测量分析颈部前后两侧的胸锁乳突肌和头夹肌的肌音信号,成功识别点头、抬头、左摆、右摆、左转、右转6个头部动作模式。实验中采集了4个通道的数据,经滤波、归一化的预处理后,用不等长分割法分割出动作帧。提取了动作帧的小波包系数能量及双谱对角切片特征,经主元分析法（PCA）和Fisher线性判别分析（FLDA）降维,用支持向量机（SVM）分类。最后对小波包系数能量和双谱对角切片特征进行FLDA降维,识别率达95.92%。     

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.     

Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis

The classification of epileptic electroencephalogram (EEG) signals is challenging because of high nonlinearity, high dimensionality, and hidden states in EEG recordings. The detection of the preictal state is difficult due to its similarity to the ictal state. We present a framework for using principal components analysis (PCA) and a classification method for improving the detection rate of epileptic classes. To unearth the nonlinearity and high dimensionality in epileptic signals, we extract principal component features using PCA on the 15 high-order spectra (HOS) features extracted from the EEG data. We evaluate eight classifiers in the framework using true positive (TP) rate and area under curve (AUC) of receiver operating characteristics (ROC). We show that a simple logistic regression model achieves the highest TP rate for class “preictal” at 97.5% and the TP rate on average at 96.8% with PCA variance percentages selected at 100%, which also achieves the most AUC at 99.5%.     

Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback–Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier’s performance from 80.69% to 90.75%. Results are here studied and discussed.     

视频脑电图诊断儿童癫痫128例

目的 分析视频脑电图（Video-EEG)在小儿癫痫诊断，分类中的应用价值及癫痫发作期的EEG特点。临床诊断的癫痫患儿128（男94，女34）例，1mo-14岁，平均年龄5．4岁，应用伟思VEEG1161B型视频脑电图仪，对患儿进行至少4－6h包括清醒与睡眠期的临床和EEG监测。监测中对各种状态及事件进行标记，结束后将EEG信号与临床录象资料同步回放并逐秒分析。结果 128例癫痫患儿中有54例（42．2％）进一步确定了癫痫综合征的诊断，其中儿童良性癫痫伴中央－颞区棘波25例，儿童失神癫痫3例，少年肌阵挛癫痫1例，额叶癫痫5例，West综合征13例，Lennox-Gastaut综合征7例，有49例（38．3％）可明确其发作类型，其中强直或（和）阵挛发作10例，部分性发作33例，肌阵挛发作5例，失张力发作1例，另外有25例（19．5）尚无法分类。结论 视频脑电图 是诊断癫痫及明确癫分类的可靠检查方法。     

Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization

Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%.     

62例枕叶癫痫的临床分析

目的：分析总结枕叶癫痫的临床表现及脑电图特点以提高对其的临床诊断水平。方法：回顾性分析62例确诊为枕叶癫痫患者的临床表现及脑电图特点。结果：枕叶癫痫的发作形式有：（1）视觉症状;（2）运动症状：1）偏转发作;2）继发性全身泛化;3）偏身抽搐,偏身强直;4）部分癫痫持续状态。（3）植物神经症状。（4）复杂部分性发作伴自动症;（5）肢体麻木。脑电图特点：一侧或双侧枕叶的痫性放电,本组10例儿童期起病的枕叶癫痫,夜间发作频敏,无器质性脑损害,对抗癫痫药物治疗反应好,提示可能为儿童良性癫痫,结论：枕叶癫痫是一组较具特征的癫痫综合征,掌握临床表现及脑电图特点,常可做出正确诊断。     

基于BP神经网络的左右手击键动作的意识任务识别

目的研究基于神经网络的左右手运动的意识任务识别方法,探讨神经网络在脑机接口中的作用。方法在特征提取的基础上,设计3层BP神经网络。选用对数Sigmoid函数,实现输入到输出的非线性映射;采用梯度最速下降算法训练神经网络。结果应用BP神经网络和线性分类器分别对测试样本进行意识任务识别。以脑电信号两个频段的功率谱以及击键前-100￣-50ms和-50￣0ms均值组成特征向量。应用线性分类器,对测试样本的识别正确率为71%,采用本文设计的BP神经网络,识别正确率为84%。结论BP神经网络是意识任务识别的有效方法,在基于脑电信号的脑机接口中有良好的应用前景。     

104例轻度胃肠炎伴良性婴幼儿惊厥的临床及脑电图分析

目的研究轻度胃肠炎伴良性婴幼儿惊厥的临床及脑电图特点。方法收集我院2010年1月～2011年12月诊断该病的患儿104例,回顾性分析其临床及脑电图特征。结果惊厥多发生于胃肠症状出现后2d内,共85例(81.7%),14例发生于胃肠症状出现后3～5d(13.5%),有5例胃肠症状出现于惊厥后(4.8%);惊厥表现形式多为全身性强直一阵挛性发作,仅4例表现为部分发作继发全身性发作;病程中惊厥发作次数为1～6次,平均发作次数2.46次;发作次数超过1次的86例中,72例在24h内连续发作,14例在48h内连续发作;一年四季散发,其中秋冬季节发病率明显增加,特别是9～12月;4～8月发病率较低。62例做视频脑电监测,25例见小棘波,占40.3%;同期我院视屏脑电监测患儿(0～3岁)1628例中,有206例见小棘波,占12.6%,两者比较,差异有统计学意义。结论该病惊厥多为成簇样发作,惊厥多发生于胃肠症状出现后48h内;惊厥发作形式多为全身强直-阵挛发作,少数为部分发作全身泛化;一次病程中惊厥发作次数不等;大多数患儿惊厥复发可能性小;较多患儿脑电图可见小棘波。     

Singular Spectrum Analysis of Sleep EEG in Insomnia

In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.