【摘要】：阻塞性睡眠呼吸暫停低通氣綜合癥是一種發(fā)病率很高的睡眠呼吸紊亂性疾病,睡眠期間的頻繁呼吸暫停和低通氣使得患者更容易引發(fā)心血管疾病、高血壓、腎臟疾病等其他生命器官并發(fā)癥,甚至發(fā)生猝死。 多導(dǎo)睡眠監(jiān)測被公認為是診斷睡眠呼吸障礙疾病的“金標準”,但是由于多導(dǎo)睡眠監(jiān)測設(shè)備有限、檢測費用昂貴、監(jiān)測過程不舒適等缺點導(dǎo)致大部分打鼾者得不到及時的診斷。目前迫切需要找到一種便攜的、舒適的、低費用的、可用于大量人群的篩查方法來減輕多導(dǎo)睡眠監(jiān)測的負荷。 本文研究方法是利用鼾聲信號的共振峰參數(shù)來實現(xiàn)阻塞性睡眠呼吸暫停低通氣綜合癥的篩查。首先利用數(shù)字語音信號處理的方法對鼾聲信號進行預(yù)處理,利用一種改進的基于短時能量的方法檢測出所有的鼾聲段語音；利用線性預(yù)測技術(shù)估計產(chǎn)生鼾聲的上氣道模型參數(shù),并利用求根方法計算出鼾聲段的第一共振峰頻率。目前已有方法使用統(tǒng)一固定的共振峰閾值來區(qū)分正常鼾聲段和不正常鼾聲段,但是每個人的上氣道生理結(jié)構(gòu)是不同的,即存在個體差異,現(xiàn)有的固定共振峰門限值篩查方法受個體差異的影響存在篩查率不高的缺陷；本文方法提出了一種不受個體差異影響的個體化閾值,利用K均值聚類算法將打鼾者一整晚鼾聲段的第一共振峰頻率分為兩類,并將較小的聚類中心(正常鼾聲對應(yīng)的第一共振峰頻率)視為該打鼾者的基準頻率,根據(jù)基準頻率與不正常鼾聲第一共振峰頻率的關(guān)系得到個體化閾值。 本文提出阻塞性睡眠呼吸暫停低通氣綜合癥篩查方法的依據(jù)為：首先,如果第一共振峰頻率值高于個體化閾值,就認為是不正常鼾聲段的第一共振峰頻率；如果不正常鼾聲段的持續(xù)時間大于0.3s,則認為鼾聲段是不正常鼾聲段；其次,模擬多導(dǎo)睡眠監(jiān)測的呼吸暫停—低通氣指數(shù)(AHI),即統(tǒng)計一小時內(nèi)不正常鼾聲段的個數(shù),根據(jù)多導(dǎo)睡眠監(jiān)測的標準,如果AHI高于5次/時,則就初步認為該打鼾者是阻塞性睡眠呼吸暫停低通氣綜合癥患者,否則認為是單純的打鼾者。本文篩查方法的靈敏度和特異度分別是93.3%和91.67%,滿足臨床醫(yī)學(xué)上篩查疾病的要求。
[Abstract]:Obstructive sleep apnea hypopnea syndrome (OSAS) is a high incidence of sleep apnea disorder disease. Frequent apnea and hypopnea during sleep make patients more prone to cardiovascular disease and hypertension. Kidney disease and other life organ complications, and even sudden death. Polysomnography is recognized as the "golden standard" for the diagnosis of sleep apnea disorder. However, due to the limitation of polysomnography monitoring equipment, the high cost of detection and the discomfort of monitoring process, most snorers can not be diagnosed in time. There is an urgent need to find a portable, comfortable, low-cost screening method that can be used in large populations to reduce the load of polysomnography. In this paper, the resonant peak parameters of snoring signal are used to screen obstructive sleep apnea hypopnea syndrome (OSAS). Firstly, the snoring signal is preprocessed by digital speech signal processing, and all snoring segment speech is detected by an improved method based on short time energy, and the parameters of upper airway model which produce snoring are estimated by linear prediction technique. The first resonance peak frequency of snoring is calculated by root seeking method. At present, there are methods to distinguish normal snoring segment from abnormal snoring segment by using a fixed resonance peak threshold, but the physiological structure of upper airway is different, that is, individual differences exist. The existing screening methods with fixed resonance peak threshold have the defect that the screening rate is not high due to individual differences. In this paper, an individual threshold is proposed, which is not affected by individual differences. K-means clustering algorithm is used to divide the first resonance peak frequency of snoring all night into two categories, and the smaller cluster center (the first resonance peak frequency corresponding to normal snoring) is regarded as the reference frequency. According to the relation between the reference frequency and the frequency of the first resonance peak of abnormal snoring, the individualized threshold is obtained. In this paper, a screening method for obstructive sleep apnea hypopnea syndrome (OSAS) is proposed. Firstly, if the frequency of the first resonance peak is higher than the individual threshold, it is considered to be the first resonance peak frequency of the abnormal snoring segment. If the duration of abnormal snoring segment is longer than 0.3 s, the snoring segment is considered to be abnormal snoring segment. Secondly, the apnea hypopnea index (AHI), which simulates polysomnotic monitoring, counts the number of abnormal snoring segments within an hour. According to the standard of polysomnography, if AHI is more than 5 times / time, the snoring person is considered as obstructive sleep apnea hypopnea syndrome, otherwise it is considered as a simple snoring person. The sensitivity and specificity of this screening method are 93.3% and 91.67% respectively, which meet the requirements of clinical medical screening.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：R766

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