本文關(guān)鍵詞： 睡眠監(jiān)測 經(jīng)驗?zāi)B(tài)分解 心沖擊信號 邊界效應(yīng) 心率變異性　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：夜間的睡眠質(zhì)量影響著人們的日常生活和身體健康。良好的睡眠是維持身心健康的重要保證。在睡眠過程中,正常平穩(wěn)的呼吸和心跳能夠促進(jìn)血液循環(huán),為身體提供充足的氧氣支持新陳代謝,提高睡眠質(zhì)量。高質(zhì)量的睡眠能夠增強(qiáng)自身免疫力,延長細(xì)胞的活性,是青少年人身心健康成長必不可少的條件。同時,中老年人保持充足健康的睡眠,可以使身體和頭腦在夜間得到充分的休息,為日常的生活和工作提供健康保障。如今,社會的快速發(fā)展使人們的生活方式發(fā)生轉(zhuǎn)變,為了有充沛的精力應(yīng)對每天的生活和工作,人們對于睡眠質(zhì)量也有了更多的關(guān)注和更高的要求。另外,由于科技不斷發(fā)展,生活成本顯著增高,人們的日常需求不斷增加和升級,這也為自己和家人帶來了更多的壓力,容易引發(fā)失眠、睡眠呼吸暫停等睡眠障礙問題。課題組針對中老年人的睡眠健康問題,設(shè)計出睡眠監(jiān)測床墊實時監(jiān)控睡眠狀態(tài),綜合人體體征指標(biāo)分析身體狀況。本課題利用硬件系統(tǒng)采集到的人體體征信號進(jìn)行信號處理相關(guān)技術(shù)研究,選取經(jīng)驗?zāi)B(tài)分解信號處理方法提取分析人體心沖擊信號。經(jīng)驗?zāi)B(tài)分解是一種新型的信號處理技術(shù),其良好的自適應(yīng)性非常適用于分析非線性、非平穩(wěn)信號。基于傳統(tǒng)的經(jīng)驗?zāi)B(tài)分解信號處理技術(shù),本論文為了更好地適應(yīng)所研究的信號特征對算法進(jìn)行了改進(jìn),以期望達(dá)到最佳分解效果。應(yīng)用改進(jìn)后的經(jīng)驗?zāi)B(tài)分解算法實現(xiàn)對信號的分解提取,針對處理后的心沖擊信號進(jìn)行了心率變異性分析。心率變異性表示人體正常心跳周期間的微小漲落,蘊(yùn)含著大量人體心血管疾病的信息,利用心沖擊信號分析得出心率變異性的相關(guān)指標(biāo),能夠及早判斷和發(fā)現(xiàn)心血管疾病的發(fā)生,實現(xiàn)日常睡眠的監(jiān)護(hù)。本論文闡述了經(jīng)驗?zāi)B(tài)分解算法的基本原理,介紹了瞬時頻率和特征模態(tài)分量的基本概念,給出了詳細(xì)的分解步驟以及希爾伯特變換的定義式,并且應(yīng)用添加高斯白噪聲的方法解決了在分解過程中的模態(tài)混疊問題。在算法改進(jìn)方面,一方面對包絡(luò)擬合方法進(jìn)行了優(yōu)化。另一方面,分析了最相關(guān)波形擬合法等邊界效應(yīng)處理方法,最終采用上下包絡(luò)邊界極值點(diǎn)延拓法解決了邊界飛翼問題。而后,本文采集了8組正常人的試驗數(shù)據(jù),利用改進(jìn)后的經(jīng)驗?zāi)B(tài)分解算法對其進(jìn)行分解提取出心沖擊信號。最后,利用處理得到的信號綜合時域、頻域、非線性三種分析方法進(jìn)行心率變異性分析,對比指標(biāo)參數(shù),驗證性能。
[Abstract]:The quality of sleep at night affects people's daily life and health. Good sleep is an important guarantee of maintaining physical and mental health. During sleep, normal and steady breathing and heartbeat can promote blood circulation. Provide adequate oxygen to the body to support metabolism, improve sleep quality. High quality sleep can enhance their own immunity, prolong the activity of cells, is an essential condition for the physical and mental growth of teenagers. Adequate and healthy sleep for middle-aged and old people allows the body and mind to rest at night and provide health protection for daily life and work. Nowadays, the rapid development of society has transformed people's way of life. In order to have a lot of energy to cope with daily life and work, people also have more attention to sleep quality and higher requirements. In addition, because of the continuous development of technology, the cost of living has increased significantly. People's daily needs are increasing and upgrading, which also brings more pressure to themselves and their families, which can easily lead to insomnia, sleep apnea and other sleep disorders. The sleep monitoring mattress is designed to monitor the sleep state in real time, and the body condition is analyzed by synthesizing the physical sign index. The signal processing related technology is studied by using the human sign signal collected by the hardware system. The empirical mode decomposition (EMD) signal processing method is selected to extract and analyze the human heart shock signal. EMD is a new signal processing technology, and its good adaptability is very suitable for nonlinear analysis. Non-stationary signal. Based on the traditional empirical mode decomposition signal processing technology, this paper improves the algorithm to better adapt to the studied signal characteristics. In order to achieve the best decomposition effect, the improved empirical mode decomposition algorithm is used to extract the signal. Heart rate variability (HRV) is used to analyze the heart rate variability (HRV), which represents the small fluctuation during the normal heartbeat, and contains a large amount of information about cardiovascular diseases. The related indexes of heart rate variability can be obtained by the analysis of cardiac shock signal, and the occurrence of cardiovascular disease can be judged and discovered early, and the monitoring of daily sleep can be realized. In this paper, the basic principle of empirical mode decomposition algorithm is described. The basic concepts of instantaneous frequency and characteristic modal component are introduced, and the detailed decomposition steps and the definition of Hilbert transform are given. And the method of adding Gao Si white noise is used to solve the problem of modal aliasing in the process of decomposition. In the aspect of algorithm improvement, the envelope fitting method is optimized on the one hand, and on the other hand, The boundary effect processing methods such as the most correlated waveform fitting method are analyzed, and the upper and lower envelope boundary extremum continuation method is used to solve the boundary flight wing problem. Then, the test data of 8 groups of normal people are collected. The improved empirical mode decomposition algorithm is used to extract the heart-impact signal. Finally, the heart rate variability is analyzed by the three methods of integrated time-domain, frequency-domain and nonlinear analysis, and the index parameters are compared. Verify performance.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R740;TN911.7

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