Hanskunatai, Anantaporn

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Hanskunatai, Anantaporn
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Hanskunatai, Anantapom
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anantaporn.ha@kmitl.ac.th

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    Item type:Publication,
    A comparative investigation of PSG signal patterns to classify sleep disorders using machine learning techniques
    (2015-01-01)
    Wongsirichot, Thakerng
    ;
    Hanskunatai, Anantaporn
    Patients with Non-Communicable Diseases (NCDs) are increasing around the globe. Possible causes of the NCDs are continuously being investigated. One of them is a sleep disorder. In order to detect specific sleep disorders, the Polysomnography (PSG), is necessary. However, due to the lack of the PSG in many hospitals, researchers attempt to discover alternative approaches. This article demonstrates comparisons of sleep disorder classifications using machine learning techniques. Three main machine learning techniques have been compared including Classification And Regression Tree (CART), k-Mean Clustering (KMC) and Support Vector Machine (SVM). The SVM achieves the best classification results in NREM-1 and NREM-2. The CART performs superior in NREM-3 and REM. Implications in terms of medical diagnosis, there are two main selected features, SaO2 and Pulse, based on the CART in all of the sleep stages. The features may be pieces of evidences to predict various types of sleep disorders.
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    Item type:Publication,
    A MULTI-LAYER HYBRID MACHINE LEARNING MODEL for AUTOMATIC SLEEP STAGE CLASSIFICATION
    (2018-12-01)
    Wongsirichot, Thakerng
    ;
    Hanskunatai, Anantaporn
    Sleep Stage Classification (SSC) is a standard process in the Polysomnography (PSG) for studying sleep patterns and events. The SSC provides sleep stage information of a patient throughout an entire sleep test. A physician uses results from SSCs to diagnose sleep disorder symptoms. However, the SSC data processing is time-consuming and requires trained sleep technicians to complete the task. Over the years, researchers attempted to find alternative methods, which are known as Automatic Sleep Stage Classification (ASSC), to perform the task faster and more efficiently. Proposed ASSC techniques usually derived from existing statistical methods and machine learning (ML) techniques. The objective of this study is to develop a new hybrid ASSC technique, Multi-Layer Hybrid Machine Learning Model (MLHM), for classifying sleep stages. The MLHM blends two baseline ML techniques, Decision Tree (DT) and Support Vector Machine (SVM). It operates on a newly developed multi-layer architecture. The multi-layer architecture consists of three layers for classifying W, R and N1, N2, N3 in different epoch lengths. Our experiment design compares MLHM and baseline ML techniques and other research works. The dataset used in this study was derived from the ISRUC-Sleep database comprising of 100 subjects. The classification performances were thoroughly reviewed using the hold-out and the 10-fold cross-validation method in both subject-specific and subject-independent classifications. The MLHM achieved a certain satisfactory classification results. It gained 0.694±0.22 of accuracy (AUC=0.822±0.31) in subject-specific classification and 0.942±0.02 of accuracy (AUC=0.920±0.17) in subject-independent classification. The pros and cons of the MLHM with the multi-layer architecture were thoroughly discussed. The effect of class imbalance was rationally discussed towards the classification results.