本文選題：睡眠分期 + 心率變異性�。� 參考：《電子科技大學》2017年碩士論文

【摘要】：越來越多的人開始遭遇睡眠問題,評價睡眠質(zhì)量進而改善睡眠狀況已經(jīng)成為一大課題。準確的睡眠分期是客觀評估睡眠質(zhì)量和診斷睡眠相關(guān)疾病的基礎(chǔ),經(jīng)典自動分期方法基本上是對腦電信號進行分析的。但是,腦電信號的記錄操作復(fù)雜,成本高,電極的放置也會干擾人的正常睡眠,無法滿足家庭睡眠監(jiān)測場合的需要。人們提出用較為方便檢測的生理參數(shù)去自動分析睡眠過程,相較傳統(tǒng)腦電更具實用價值,利用壓電感知床墊能實現(xiàn)人在睡眠時多個生理參數(shù)的長時間同步檢測,對睡眠幾乎沒有任何干擾,可以在家庭中監(jiān)測人們的真實睡眠狀況,擁有良好的應(yīng)用前景。床墊采集的是心沖擊圖(BCG)信號,包含心率、呼吸率和體動信息,但目前對BCG信號進行自動睡眠分期的準確率不高。本文的研究目的是基于BCG信號建立一種更加準確可靠的多參數(shù)自動分期算法,然后應(yīng)用于實現(xiàn)的床墊式實時睡眠監(jiān)護系統(tǒng)中,進行睡眠的實時監(jiān)測。本文采用從BCG信號中計算出的心率、呼吸和體動序列進行自動睡眠分期,分為四個階段,即覺醒期、淺度睡眠期、深度睡眠期和快速眼動睡眠期。由于不同睡眠階段的心率波形形態(tài)較難區(qū)分,利用時變自回歸模型進行心率變異性(HRV)的特征提取,進一步基于隱馬爾可夫模型進行特征的自動分類識別。發(fā)現(xiàn)高頻段極點相位與總功率的特征組合能夠較好地區(qū)分各睡眠分期,并結(jié)合呼吸率和體動信息校正分期結(jié)果,能夠有效地提高基于HRV的分期準確率。使用MIT-BIH數(shù)據(jù)庫中的數(shù)據(jù)測試,比較文中算法和專家的分期結(jié)果,驗證了建立的多參數(shù)分期算法的精度,識別率達到70.13%,且計算快速,可用于實時監(jiān)測。本文設(shè)計并實現(xiàn)了床墊式實時睡眠監(jiān)護系統(tǒng),將本文建立的自動睡眠分期算法應(yīng)用到該系統(tǒng)中,能在家庭環(huán)境中使用,實時監(jiān)測人們的睡眠。系統(tǒng)基于壓電感知床墊,將從BCG信號分離出的實時的心率、呼吸率和體動數(shù)據(jù)上傳至服務(wù)器進行存儲,完成自動睡眠分期,最終通過智能手機應(yīng)用為用戶提供實時睡眠信息展示、睡眠質(zhì)量分析和改善建議等服務(wù),以幫助人們提高睡眠質(zhì)量。
[Abstract]:More and more people are experiencing sleep problems. It has become a major topic to evaluate sleep quality and improve sleep quality. Accurate sleep staging is the basis of objective evaluation of sleep quality and diagnosis of sleep related diseases. However, the recording of EEG signals is complicated and costly, and the placement of electrodes will interfere with normal sleep, which can not meet the needs of family sleep monitoring. More convenient physiological parameters are proposed to automatically analyze sleep process, which is more practical than traditional EEG. Using piezoelectric sensing mattress can realize long time synchronous detection of multiple physiological parameters during sleep. It can monitor people's real sleep condition in the family, and has good application prospect. The mattresses collected the BCG) signals of cardiogram, which included heart rate, respiration rate and body movement information, but the accuracy of automatic sleep staging of BCG signals was not high at present. The purpose of this paper is to establish a more accurate and reliable multi-parameter automatic staging algorithm based on BCG signal, and then apply it to the realization of the mattress real-time sleep monitoring system to monitor sleep in real time. In this paper, the heart rate, respiration and body motion sequences calculated from BCG signal are used to carry out the automatic sleep stages, which are divided into four stages, namely, wakefulness, shallow sleep, deep sleep and rapid eye movement sleep. Because it is difficult to distinguish the shape of heart rate waveform in different sleep stages, the time-varying autoregressive model is used to extract the feature of HRV, and then the hidden Markov model is used for automatic classification and recognition. It is found that the feature combination of pole phase and total power in high frequency band can distinguish the sleep stages well, and can effectively improve the accuracy of staging based on HRV by combining the results of respiration rate and volume motility information correction. By using the data test in MIT-BIH database and comparing the results of the algorithm and the expert, the accuracy of the multi-parameter staging algorithm is verified. The recognition rate is 70.133.And the calculation is fast and can be used for real-time monitoring. In this paper, a mattress type real-time sleep monitoring system is designed and implemented. The automatic sleep staging algorithm established in this paper is applied to the system, which can be used in the home environment and monitor people's sleep in real time. Based on piezoelectric sensing mattress, the system uploads the real-time heart rate, respiration rate and body motion data separated from BCG signal to the server for storage, and completes the automatic sleep stage. Finally, the smartphone application provides users with real-time sleep information display, sleep quality analysis and improvement advice to help people improve the quality of sleep.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.52;R740

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本文編號：1840012