【摘要】：隨著現(xiàn)代生活節(jié)奏的加快，睡眠不足、睡眠不良，甚至徹夜失眠的情況時(shí)有發(fā)生。人們在追求高效生活和工作的同時(shí)，也受到疲勞和睡意的危害。在高危操作環(huán)境下，像駕駛、鋼鐵領(lǐng)域以及核工業(yè)等，如果操作人員在工作時(shí)有睡意，那人們的生命財(cái)產(chǎn)安全都會(huì)受到威脅。而在一些病患的狀態(tài)監(jiān)測方面，比如阻塞性睡眠呼吸暫停綜合征，實(shí)時(shí)的睡意監(jiān)測也是十分必要的。心電和脈搏信號(hào)蘊(yùn)含豐富的人體生理信息，測量也較為簡便。研究過程中設(shè)計(jì)了睡意檢測實(shí)驗(yàn)并且同步采集了30例健康無睡眠問題的實(shí)驗(yàn)對象在清醒和睡意狀態(tài)下的心電信號(hào)、脈搏信號(hào)和腦電信號(hào)。 本文首先對篩選出的26例實(shí)驗(yàn)對象的心電和脈搏同步信號(hào)進(jìn)行預(yù)處理，再通過特征提取與分析，選擇在清醒和睡意狀態(tài)下有顯著性差異的特征用于分類。與清醒狀態(tài)相比，在睡意狀態(tài)下心電T波峰值、重搏波高度和脈搏波傳輸時(shí)間均非常顯著地降低(p0.005)，脈搏波起點(diǎn)到主波峰點(diǎn)的時(shí)間非常顯著地增大(p0.005)，心率值和心率變異性中的VLF值顯著地降低(p0.05)。使用線性判別分析和支持向量機(jī)兩種方法對這些特征分別進(jìn)行單一特征和組合特征的分類。 本文除了提取到心電和脈搏信號(hào)在時(shí)域和頻域上的20多種特征，計(jì)算了心電信號(hào)的小波能譜熵，還對睡眠剝奪的實(shí)驗(yàn)對象連續(xù)時(shí)間段內(nèi)的心電RR間期最值和脈搏波特征K值進(jìn)行了分析。 研究結(jié)果表明：睡意對心電和脈搏信號(hào)的一些特征有影響；心電T波峰值、重搏波高度、脈搏波傳輸時(shí)間、脈搏波起點(diǎn)到主波峰點(diǎn)的時(shí)間、心率以及心率變異性中的VLF在清醒和睡意兩種狀態(tài)下有顯著性差異；特征組合有利于提高分類正確率；心電T波峰值的單一特征及其參與的組合特征的分類正確率高于其他。心電和脈搏特征能夠用于區(qū)分清醒和睡意狀態(tài)，而且心電和脈搏信號(hào)的測量方便，為睡意檢測提供一種新的識(shí)別方法。
[Abstract]:As the pace of modern life accelerates, sleep deprivation, poor sleep, and even sleeplessness happen all night. People in the pursuit of efficient life and work, but also by fatigue and sleep harm. In high-risk environments, such as driving, steel and the nuclear industry, people's lives and property are threatened if they are sleepy at work. In some patients, such as obstructive sleep apnea syndrome, real-time sleep monitoring is also necessary. ECG and pulse signals contain abundant physiological information of human body, and the measurement is also relatively simple. In the course of the study, a sleepiness detection experiment was designed and 30 healthy subjects without sleep problems were simultaneously collected ECG signals, pulse signals and EEG signals in awake and sleepy states. In this paper, the electrocardiogram and pulse synchronization signals of 26 selected subjects were preprocessed, and then, by feature extraction and analysis, the significant differences between awake and sleepy state were selected for classification. Compared with awake state, the peak value of ECG T wave in sleeping state, The height of repulse wave and the transmission time of pulse wave decreased significantly (p0.005), the time from the starting point of pulse wave to the peak point of main wave increased significantly (p0.005), and the VLF value of heart rate and heart rate variability decreased significantly (p0.05). Linear discriminant analysis (LDA) and support vector machine (SVM) are used to classify these features into single feature and combined feature respectively. In this paper, in addition to extracting more than 20 characteristics of ECG and pulse signals in time and frequency domain, the wavelet energy spectrum entropy of ECG signal is calculated. The maximum value of ECG RR interval and characteristic K value of pulse wave in the continuous period of sleep deprivation were also analyzed. The results show that sleep affects some characteristics of ECG and pulse signal, the peak value of ECG T wave, the height of repulse wave, the time of pulse wave transmission, the time of starting point of pulse wave to the peak point of main wave, VLF in heart rate and heart rate variability was significantly different between awake and sleepiness states; the combination of features was helpful to improve the classification accuracy; the single feature of ECG T wave peak value and the classification accuracy rate of its involved combination characteristics were higher than those of others. The characteristics of ECG and pulse can be used to distinguish awake and sleepy states, and the measurement of ECG and pulse signals is convenient, which provides a new recognition method for sleepiness detection.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.0;TN911.7

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