Search

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.

Biomedical data analytic system has played an important role in doing the clinical diagnosis for several decades. Today, it is an emerging research area of analyzing these big data to make decision support for physicians. This paper presents a parallelized web-based tool with cloud computing service architecture to analyze the epilepsy. There are many modern analytic functions which are wavelet transform, genetic algorithm (GA), and support vector machine (SVM) cascaded in the system. To demonstrate the effectiveness of the system, it has been verified by two kinds of electroencephalography (EEG) data, which are short term EEG and long term EEG. The results reveal that our approach achieves the total classification accuracy higher than 90%. In addition, the entire training time accelerate about 4.66 times and prediction time is also meet requirements in real time.

Patient self-management is necessary for care and management of chronic diseases. It can help patients to have a better understanding of their own lifestyle and health behavior and thus improve the disease condition and health status. Traditionally, in self-management, patients and physicians manually note down vital signs, medication record, healthcare visits, and activity logs on paper or onto a computer. These methods of recording may yield data, but it is difficult to maintain or manage. This study is aimed for developing a system to build a personal health record (PHR) for diabetes care automatically, generate continuity of care document (CCD) files for sharing of health information and set up clinical guidelines (CGs). Important advantages of clinical guidelines for medical care including improvement in clinical practice, support for medical decision making, and reduced hospital delays. In addition, our approach follows the principle of American Association of Diabetes Educators (AADE) in designing a platform. The results reveal that the decrease rate of HbA1C of patients for intervention group (66.6%) is better than control group (40%) after three months. The platform not only assists patients in being actively involved in their own personal health records, but improves their health status as well.

In recent years, the rapid development of trade and traffic in our country has resulted in the increasing occurrence of various types of disasters and emergency injuries. According to the statistics from Fire Departments of Taiwan, medical emergency dispatch frequencies have exceeded 300,000, and the frequency of medical emergency dispatches has increased each year. These figures reflect the phenomenon that, despite the continual growth and progression of the community, various accidents and unforeseen situations are becoming increasingly common. Therefore, fire units have established a medical emergency care system with the objective of minimizing injuries, patient suffering, and the risk of mortality, reducing social medical costs, and alleviating the financial burden of medical emergencies on families. Many countries have special medical emergency dispatch systems, but these do not meet Taiwan's rules and emergency procedures. Thus, our research has focused on compiling a symptom-based medical emergency dispatch guide designed to match Taiwan's medical emergency dispatch system. We collate national medical emergency dispatch guides and present a medical emergency dispatch system to support medical staff and to educate these staff on the correct methodology in a short time. This system achieves the following objectives: (1) to enhance the quality of medical emergency services in Taiwan, (2) to reduce the response time of medical emergency dispatch, (3) to reduce the unnecessary waste of medical resources.