KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Moeynoi, PimpornHas files: No

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Hybrid dimensionality reduction of multi-sets using nature inspired algorithms and Discriminant Canonical Correlation Analysis for automatic sleep stage classification
    (2019-01-01)
    Moeynoi, Pimporn
    ;
    Kitjaidure, Yuttana
    Automatic sleep stage classification using various signals is an important tool to assess sleep disorders and sleep quality. However, the numerous variables from multi sources lead to the classification problems. To improve the problem, this paper proposes a new hybrid dimensionality reduction by combining the feature selection based on the nature-inspired algorithms (NIAs) and multi-sets transformation technique by Canonical Correlation Analysis (CCA)/ Discriminant Canonical Correlation analysis (DCCA). The NIAs is first adopted to generate the updated population positions in each dataset, and then the CCA/DCCA is used to fuse the selected subsets. The proposed algorithm performance is demonstrated on sleep-wake detection and multi-class sleep stage classification. Furthermore, the proposed method called Dissimilarity Binary Grey Wolf Optimization - Discriminant Canonical Correlation Analysis (DisBGWO-DCCA), modified by using the differential evolution (DE) technique based on the similarity of Jaccard coefficient, provides the best classification accuracies with 97.75% of the sleep-wake detection and 95.85% of the multi-class sleep stage classification when comparing with other single dimensionality reduction approaches[1-2]. Moreover, the proposed algorithm achieves the computational cost better than the conventional NIAs as shown later in experiment. Our experiment is also operated on both healthy subjects and sleep disorder patients with efficient sleep stage classification.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Dimension reduction based on Canonical Correlation Analysis technique to classify sleep stages of sleep apnea disorder using EEG and ECG signals
    (2017-11-03)
    Moeynoi, Pimporn
    ;
    Kitjaidure, Yuttana
    Sleep stage scoring is the first step to diagnostic of sleep disorders and it is scored by the conventional method known as the visual sleep stage scoring based on human. To assist the sleep physician in evaluating of patients, a new automatic sleep stage classification system needs to be developed. So this is the aim of this work based on Electroencephalography (EEG) and Electrocardiography (ECG) for Sleep apnea patients. This article proposes two importance topics, the first is the new feature of EEG signal using a simple statistical technique and the results prove that the various sleep stages can be discriminated more clearly at significant levels (p<<0.05). Second, the dimension reduction is proposed based on the Canonical Correlation Analysis (CCA) technique that explores possible correlated multi-sources to improve the sleep stages classification at 95.42% accuracy by using random forest classification. The results show that our proposed method has ability to develop a new sleep stage classification assistance.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Canonical correlation analysis for dimensionality reduction of sleep apnea features based on ECG single lead
    (2017-02-21)
    Moeynoi, Pimporn
    ;
    Kitjaidure, Yuttana
    This paper presents the canonical correlation analysis (CCA) method for dimensional reduction of sleep apnea features extracted from the electrocardiogram single lead. The feature extraction belong to the linear and nonlinear techniques of variance of heart rhythm or heart rate variability and respiratory waveform from electrocardiography signal. These are benefit to evaluate the sleep apnea in noninvasive way. We introduce a feature reduction using the CCA method to determine relationship across the pair data sets and also compare with the common dimensionality reduction methods. As our review, this first demonstrate offers the CCA method to transformed apnea features. In experiment, 25 recording from physionet database are used and all features are extracted minute-by-minute based on apnea annotation. As expected, in processing for CCA, the classification results are better than the classical methods, and use the number of reduced features less than the classical ones.