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基因演算法應用在以ECG信號為基礎之睡眠辨識特徵值選取 = Applic...

吳宜衡

基因演算法應用在以ECG信號為基礎之睡眠辨識特徵值選取 = Application of genetic algorithm on ECG-based features selection for sleep staging

Record Type:	Language materials, printed : monographic
Paralel Title:	Application of genetic algorithm on ECG-based features selection for sleep staging
Author:	吳宜衡,
Secondary Intellectual Responsibility:	國立高雄大學
Place of Publication:	[高雄市]
Published:	撰者;
Year of Publication:	2014[民103]
Description:	70面圖,表 : 30公分;
Subject:	基因演算法
Subject:	Genetic Algorithm (GA)
Online resource:	http://handle.ncl.edu.tw/11296/ndltd/48500899068496205268
Notes:	104年10月31日公開
Notes:	參考書目：面67-70
Summary:	隨著科技日新月異生活步調也變得相當的緊湊，擁有良好的睡眠品質更顯得相當的重要。然而並非每個人都擁有良好的睡眠品質，也很難去感覺自己的睡眠品質是否良好。在臨床上通常使用多重睡眠生理記錄儀（PSG）來記錄病患的睡眠生理訊號，並藉此來觀察病患的睡眠狀況和品質。目前判讀都是依賴專家的人工判讀，使判讀的結果可能會含有專家的主觀想法而且也十分的耗力費時。所以自動判讀系統成為相當重要的課題，可以讓我們隨時擁有省力且客觀的分析並減輕專家的負擔。一般的睡眠分析大多使用腦電圖、眼電圖和肌電圖的訊號組合但是這種方式對於使用者的睡眠品質有著一定程度干擾，使的判讀結果無法正確的反映出該使用者的真正睡眠品質。因此本研究決定採用相對干擾較低的心率訊號來開發自動判讀系統，並結合基因演算法挑選合適的特徵值做分析使得判讀時間可以大幅縮短。再利用隱藏式馬爾可夫模型(HMM)進行睡眠判讀，由於該模型可以對一段連續時間序列的狀態轉移機率及觀察狀態機率來分析，而HMM在狀態估測的過程中前一個狀態的結果會影響到下一個狀態，因此HMM適合具有連續階段轉移特性的睡眠階段辨識。　　Sleep is very important to everyone. However, not everyone can acquire good sleep quality. For the diagnosis, all night polysomnographic (PSG) recordings are usually taken from the patients. The doctor needs to realize the sleep quality and quantity of them. Nevertheless, visual sleep scoring is a time consuming and subjective process. Therefore, developing an automatic sleep scoring method is a very important issue. Due to the disturbance from typical biomedical signals: EEG, EOG, and EMG recording are too huge, the sleep quality scored from those signals is not accurate enough. So our objective of this study is developing an automatic sleep scoring method which only uses the heart rate as the input signal. Although the method using HRV as the input signal is not good enough, the benefits like less disturbance, easy to use and capability of detecting sleep cycle, make it has unlimited potential.　　We used Genetic Algorithm(GA) to select some suitable features calculated ECG for sleep staging. Combine DHMM which is trained by using the codebook for all testing features. The trained DHMM model is used for sleep staging. Through theevolution of GA and DHMM, better chromosomes or more suitablefeatures are obtained.

基因演算法應用在以ECG信號為基礎之睡眠辨識特徵值選取 = Application of genetic algorithm on ECG-based features selection for sleep staging
吳, 宜衡

基因演算法應用在以ECG信號為基礎之睡眠辨識特徵值選取 = Application of genetic algorithm on ECG-based features selection for sleep staging / 吳宜衡撰 - [高雄市] : 撰者, 2014[民103]. - 70面 ; 圖,表 ; 30公分.
104年10月31日公開參考書目：面67-70.
基因演算法Genetic Algorithm (GA)

基因演算法應用在以ECG信號為基礎之睡眠辨識特徵值選取 = Application of genetic algorithm on ECG-based features selection for sleep staging
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330 $a 隨著科技日新月異生活步調也變得相當的緊湊，擁有良好的睡眠品質更顯得相當的重要。然而並非每個人都擁有良好的睡眠品質，也很難去感覺自己的睡眠品質是否良好。在臨床上通常使用多重睡眠生理記錄儀（PSG）來記錄病患的睡眠生理訊號，並藉此來觀察病患的睡眠狀況和品質。目前判讀都是依賴專家的人工判讀，使判讀的結果可能會含有專家的主觀想法而且也十分的耗力費時。所以自動判讀系統成為相當重要的課題，可以讓我們隨時擁有省力且客觀的分析並減輕專家的負擔。一般的睡眠分析大多使用腦電圖、眼電圖和肌電圖的訊號組合但是這種方式對於使用者的睡眠品質有著一定程度干擾，使的判讀結果無法正確的反映出該使用者的真正睡眠品質。因此本研究決定採用相對干擾較低的心率訊號來開發自動判讀系統，並結合基因演算法挑選合適的特徵值做分析使得判讀時間可以大幅縮短。再利用隱藏式馬爾可夫模型(HMM)進行睡眠判讀，由於該模型可以對一段連續時間序列的狀態轉移機率及觀察狀態機率來分析，而HMM在狀態估測的過程中前一個狀態的結果會影響到下一個狀態，因此HMM適合具有連續階段轉移特性的睡眠階段辨識。　　Sleep is very important to everyone. However, not everyone can acquire good sleep quality. For the diagnosis, all night polysomnographic (PSG) recordings are usually taken from the patients. The doctor needs to realize the sleep quality and quantity of them. Nevertheless, visual sleep scoring is a time consuming and subjective process. Therefore, developing an automatic sleep scoring method is a very important issue. Due to the disturbance from typical biomedical signals: EEG, EOG, and EMG recording are too huge, the sleep quality scored from those signals is not accurate enough. So our objective of this study is developing an automatic sleep scoring method which only uses the heart rate as the input signal. Although the method using HRV as the input signal is not good enough, the benefits like less disturbance, easy to use and capability of detecting sleep cycle, make it has unlimited potential.　　We used Genetic Algorithm(GA) to select some suitable features calculated ECG for sleep staging. Combine DHMM which is trained by using the codebook for all testing features. The trained DHMM model is used for sleep staging. Through theevolution of GA and DHMM, better chromosomes or more suitablefeatures are obtained.
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