Sleep spindle detection using rolling ball sifting
博士 === 國立中央大學 === 系統生物與生物資訊研究所 === 105 === Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests b...
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ndltd-TW-105NCU051120042019-05-15T23:39:52Z http://ndltd.ncl.edu.tw/handle/37t9vp Sleep spindle detection using rolling ball sifting 使用滾球篩選睡眠紡錘波檢測 Min-Yin Liu 劉明音 博士 國立中央大學 系統生物與生物資訊研究所 105 Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737. Norden E. Huang Hui-Yang Huang 黃鍔 黃輝揚 2017 學位論文 ; thesis 82 en_US |
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博士 === 國立中央大學 === 系統生物與生物資訊研究所 === 105 === Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.
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Norden E. Huang |
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Norden E. Huang Min-Yin Liu 劉明音 |
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Min-Yin Liu 劉明音 |
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Min-Yin Liu 劉明音 Sleep spindle detection using rolling ball sifting |
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Min-Yin Liu |
title |
Sleep spindle detection using rolling ball sifting |
title_short |
Sleep spindle detection using rolling ball sifting |
title_full |
Sleep spindle detection using rolling ball sifting |
title_fullStr |
Sleep spindle detection using rolling ball sifting |
title_full_unstemmed |
Sleep spindle detection using rolling ball sifting |
title_sort |
sleep spindle detection using rolling ball sifting |
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2017 |
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http://ndltd.ncl.edu.tw/handle/37t9vp |
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