Extracting the Critical Frequency Bands to Classify Vigilance States of Rats by Using a Novel Feature Selection Algorithm

[[abstract]]Identifying mammalian vigilance states has recently become an important topic in biological science research. The biological researchers concern not only to improve the accuracy rate for classifying the vigilance states, but also to extract the meaningful frequency bands. In this study, we propose a novel feature selection to extract the critical frequency bands of rat’s EEG signals. The proposed algorithm adopts the concept of neighborhood relation during adding and eliminating a candidate feature. In the experiments, the proposed method shows better accuracy rate, and find out the feature subset which locate on the critical frequency bands for recognizing rat’s vigilance states.[[conferencetype]]國際[[conferencedate]]20130224~20130225[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Rome, Ital

Feature Extraction and Selection in Automatic Sleep Stage Classification

Sleep stage classification is vital for diagnosing many sleep related disorders and Polysomnography (PSG) is an important tool in this regard. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. The automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. In this research work, we focused on feature extraction and selection steps. The main goal of this thesis was identifying a robust and reliable feature set that can lead to efficient classification of sleep stages. For achieving this goal, three types of contributions were introduced in feature selection, feature extraction and feature vector quality enhancement. Several feature ranking and rank aggregation methods were evaluated and compared for finding the best feature set. Evaluation results indicated that the decision on the precise feature selection method depends on the system design requirements such as low computational complexity, high stability or high classification accuracy. In addition to conventional feature ranking methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder (SSAE) was used for dimensionality reduction. In feature extration area, new and effective features such as distancebased features were utilized for the first time in sleep stage classification. The results showed that these features contribute positively to the classification performance. For signal quality enhancement, a loss-less EEG artefact removal algorithm was proposed. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy

A machine learning approach to classify vigilance states in rats

[[sponsorship]]資訊科學研究所,資訊科技創新研究中心[[note]]已出版;[SCI];有審查制度;具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=0957-4174&DestApp=JCR&RQ=IF_CAT_BOXPLO

A Machine Learning Approach to Classify Vigilance States in Rats

[[abstract]]Identifying mammalian vigilance states has recently become an important topic in biological science research. The vigilance states are usually categorized in at least three states, including slow wave sleep (SWS), rapid eye movement sleep (REM), and awakening. To identify different vigilance states, even a well-trained expert must spend a lot of time analyzing a mass of physiological recording data. This study proposes an automatic vigilance stages classification method for analyzing EEG signals in rats. The EEG signals were transferred by fast Fourier transform before extracting features. These extracted features were then used as training patterns to construct the proposed classification system. The proposed classification system contains two functional units. The first unit is principle component analysis (PCA) method, which is used to project the high dimensional features into the lower dimensional subspace. The second unit is the k-nearest neighbor (k-NN) method, which identifies the physiological state in each EEG signal epoch. Based on the results of analyzing 810 epochs of EEG signal, the proposed classification method achieves satisfactory classification accuracy for vigilance states. Based on machine-learning algorithms, the classifier learns to approach the configuration that best fits the categorization task. Therefore, additional training in searching best parameters and thresholds can be avoided. Moreover, the PCA algorithm projects data instances into a 3-D space, making it possible to visualize state-changing dynamics. Experimental results show that the proposed machine-learning based classifier performs better than conventional vigilance state classification algorithms. The results also suggest that it is possible to identify the vigilance states with only EEG signals using the proposed pattern recognition technique.[[incitationindex]]SCI[[booktype]]紙

[[alternative]]A Machine Learning Approach to Classify Vigilance States in Rats

計畫編號：NSC99-2221-E032-072研究期間：201008~201107研究經費：480,000[[sponsorship]]行政院國家科學委員