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In this paper, we present an epilepsy analysis system, referred to as EAS, for long-term electroencephalography (EEG) monitoring of patients with epilepsy. In our previous works, a high accuracy seizure detection algorithm had been devised. Six support vector machines (SVMs) had been trained to collaboratively classify EEG data into four types, i.e., normal, spike, sharp wave, and seizure. The EAS had initially extracted a total of 980 features from raw EEG data of patients, and then, for each SVM, it used a naive genetic algorithm (GA) to determine a feature subset of the 980 features. However, the feature subsets still included some low-impact features for the EEG classification, and the training process of the seizure detector was time consuming. In this study, the GA is enhanced to further exclude low-impact features from the feature subsets and MapReduce parallel processing is adopted to speed up the training process. In the experiment, a 363-h clinical EEG records were acquired from 28 participants, 3 of which were normal, and 25 were patients with epilepsy. The experiment results show that average size of the feature subsets is reduced from 133.5 to 92.5 and the overall classification accuracy increases from 88.8 to 90.1 %. The new seizure detector processes a 10-s EEG record within 0.6 s, meaning that it meets the real-time requirement for online EEG monitoring gracefully. When the number of servers increases from 1 to 15, the training time of the detector is reduced from 38.3 to 4.9 h. Our new approach improves the EAS significantly.

We present a system to detect seizure and spike in Epilepsy Electroencephalogram (EEG) analysis and characterize different epilepsy EEG types. After extracting features from three EEG types, Normal, Seizure and Spike, with Empirical Mode Decomposition (EMD), we do Analysis of variance (ANOVA) to classify conspicuous features and low-resolution features, and build Gaussian distributions of conspicuous features for probability density function (PDF) to do classification. Using EMD, the recognition rate improved from 70% to 90%. With ANOVA, the recognition rate can reach 99%. The linear model accelerates the system from 2 hours to 90 seconds compare to the previous approach.

Multiclass classification is an important technique to many complex bioinformatics problems. However, their performance is limited by the computation power. Based on the Apache Hadoop design framework, this study proposes a two layer architecture that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have reduced 86.55% features and raised accuracy from 97.53% to 98.03%. With a user-friendly web interface, the system provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of bioinformatics data.

Recently, Event-Related Potential (ERP) has being the most popular method in evaluating brain waves of schizophrenia patients. ERP is one of the electroencephalography (EEG), which is measured the change of brain waves after giving patients certain stimulations instead of resting state. However, with traditional statistical analysis method, both P50 and MMN showed significant difference between controls and patients but not in Gamma band. Gamma band is a 30-50 Hz auditory stimulation which had been suggested may be abnormal in schizophrenia patients. Our data are recruited from 5 schizophrenia patients and 5 controls in National Taiwan University Hospital have been tested with this platform. The results showed that detection rate is 88.24% and we also analyzed the importance of features, including Standard Deviation (SD) and Total Variation (TotalVar) in different stage of wavelet transform. Therefore, this proposed methodology could serve as a valuable clinical decision support for physiologists in evaluating schizophrenia.