Natheer Khasawneh

Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyis entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

BACKGROUND The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. OBJECTIVES This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. METHODS The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. RESULTS Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. CONCLUSIONS This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

Web usage mining using rough sets

This paper studies the use of a rough set based learning program for predicting Web usage. In our approach, Web usage patterns are represented as rules generated by the inductive learning program, BLEM2. Inputs to BLEM2 are clusters generated by a hierarchical clustering algorithm applied to preprocessed Web log records. Empirical results show that the prediction accuracy of rules induced by the learning program is better than a centroid based method. In addition, the use of a learning program can generate shorter cluster descriptions.

Analysis and Identification of Malicious JavaScript Code

ABSTRACT Malicious JavaScript code has been actively and recently utilized as a vehicle for Web-based security attacks. By exploiting vulnerabilities such as cross-site scripting (XSS), attackers are able to spread worms, conduct Phishing attacks, and do Web page redirection to “typically” porn Web sites. These attacks can be preemptively prevented if the malicious code is detected before executing. Based on the fact that a malignant code will exhibit certain features, we propose a novel classification-based detection approach that will identify Web pages containing infected code. Using datasets of trusted and malicious Web sites, we analyze the behavior and properties of JavaScript code to point out its key features. These features form the basis of our identification system and are used to properly train the various classifiers on malicious and benign data. Performance evaluation results show that our approach achieves a 95% or higher detection accuracy, with very small (less than 3%) false positive and false negative ratios. Our solution surpasses the performance of the comparable literature.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG, EOG, AND EMG EXTRACTED FEATURES

A new system for sleep multistage level scoring by employing extracted features from twenty five polysomnographic recording is presented. For the new system, an adaptive neuro-fuzzy inference system (ANFIS) is developed for each sleep stage. Initially, three types of electrophysiological signals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were collected from twenty five healthy subjects. The input pattern used for training the ANFIS subsystem is a set of extracted features based on the entropy measure which characterize the recorded signals. Finally an output selection subsystem is utilized to provide the appropriate sleep stage according to the ANFIS stage subsystems outputs. The developed system was able to provide an acceptable estimation for six sleep stages with an average accuracy of about 76.43% which confirmed its ability for multistage sleep level scoring based on the extracted features from the EEG, EOG and EMG signals compared to other approaches.

New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes

This paper describes a new method for automatic detection of obstructive sleep apnea (OSA) based on artificial neural networks (ANN) using regular electrocardiogram (ECG) recordings. ECG signals were pre-processed and segmented to extract the P-waves; then three P-wave features were extracted: the P-wave duration (Tp), the P-wave dispersion (Pd), and the time interval from the peak of the P-wave to the R-wave (Tpr). Combinations of the three features were used as features for classification using ANN. For each feature combination studied, 70% of the input data was used for training the ANN, 15% for validating, and 15% for testing the results. Perfect agreement between expert’s scores and the ANN scores was achieved when the ANN was applied on Tp, Pd, and Tpr taken together, while substantial agreements were achieved when applying the ANN on the feature combinations Tp and Pd, and Tp and Tpr.

On Using Classification Techniques for Corpus Reduction in Arabic Text-To-Speech Systems

Abstract Text-to-speech tools are gaining an increasing momentum with the pervasiveness of today’s computer applications. These tools are typically implemented using diphones and syllables, with a body of knowledge (i.e., corpus) comprised of pre-recorded sounds. Although pre-recording achieves high intelligibility and a more natural experience, it requires a large memory size to store the sounds, which in turn leads to slowness in the conversion process. In this paper, we tackle the problem of reducing the size of memory required to store the pre-recordings of an Arabic text-to-speech system. We take a different approach and explore building a classification model based on predefined types of news documents, and propose a scheme for constructing an Arabic corpus based on this model. Performance evaluation results show that, using our scheme, a 29% reduction of the database size will only incur a 0.57% loss of recognition correctness, while a massive 89% reduction will lower the correctness by a mere 1.29%.

Multidimensional Sessions Comparison Method Using Dynamic Programming

This paper introduces a new multidimensional sessions comparison method (MSCM) using dynamic programming. Our method takes into consideration of different session dimensions such as the page list, the time spent on each page, and the length of each session. The method showed more accurate results than other known methods such as sequence alignment method (SAM), multidimensional sequence alignment method (MDSAM), and path feature space. Output of the MSCM is presented in the form of dissimilarity matrix, which can be used by different clustering techniques, such as the hierarchal, the k-mean, and the equivalence classes clustering algorithms.

Combining decision trees classifiers: a case study of automatic sleep stage scoring

This paper presents a new approach of classification in which multiple decision trees are combined together for achieving better accuracy compared to that achieved by each of the individual constituent decision trees. A major unit of the proposed system is the combination unit for which we present two algorithms; one is based on pre-pruning and true positive rate and the other is based on maximum probability voting. In presenting this new method, we use the case study of sleep stage scoring as a basis of demonstration. For such a task, two tree classifications are utilised. We performed a tree classification based on the training data and then combined the resulting model with another classification tree supplemented by the expert according to Rechtschaffen and Kales sleep scoring rules. We applied this method to nine recordings, six of which were used to construct the training tree and the remaining three were used for testing. The experiments showed that the combination method has a 7% better accuracy over a single model.

Analysis and classification of Arabic crowd-sourced news reports: A case study of the Syrian crisis

The prevalence of social media, in the whole world and the Arab region in particular, has fueled the active engagement and participation of large swaths of the Arabic society in current events. Social media has been used to rally public opinion, increase awareness, spread information/misinformation, and organize large events. Data analysis is necessary to drive decision making, advertisement, political campaigning, counter-intelligence, etc. The sheer volume of data and number of users calls for automated methods for analysis and classification of Arabic text. In this paper, the problem of analysis and classification of Arabic news reports was studied. Innovative methods, based on lexical analysis and machine learning, were employed to tame the complexity of the Arabic language. Different classification algorithms were compared and the classification accuracy results are promising. This research presents seminal steps toward specialized analysis of Arabic crowd-sourced data and social media.