本文選題：熟聲 + 呼吸暫停�。� 參考：《大連理工大學(xué)》2010年碩士論文

【摘要】： 睡眠呼吸暫停低通氣綜合征,是一種具有嚴(yán)重危害及潛在危險(xiǎn)的高發(fā)性疾病。由于在睡眠時(shí)時(shí)常發(fā)生低氧及高碳酸血癥,最終引起全身多系統(tǒng)、多器官的漸進(jìn)性危害,患者的身體健康狀況受到很大的影響。 一直以來(lái),多導(dǎo)睡眠圖是公認(rèn)的診斷睡眠呼吸暫停低通氣綜合癥的“金標(biāo)準(zhǔn)”。但是多導(dǎo)睡眠圖監(jiān)測(cè)設(shè)備數(shù)量有限,絕大多數(shù)的患者得不到及時(shí)的診治。一種低費(fèi)用的,更加便捷的診斷方法得到眾多研究者的青睞。 本文研究基于鼾聲檢測(cè)的方法篩查睡眠呼吸暫停低通氣綜合癥。首先基于語(yǔ)音處理的方法對(duì)鼾聲信號(hào)預(yù)處理,其次在討論了傳統(tǒng)的短時(shí)能量、短時(shí)過(guò)門(mén)限率和雙門(mén)限端點(diǎn)檢測(cè)的基礎(chǔ)上,針對(duì)鼾聲信號(hào)的特性提出了自適應(yīng)的短時(shí)能量端點(diǎn)檢測(cè)法。與傳統(tǒng)的固定閾值端點(diǎn)檢測(cè)相比,新算法能夠動(dòng)態(tài)的調(diào)整能量閾值從而有效地檢測(cè)出有聲段。由于打鼾的不連續(xù)性,本文對(duì)鼾聲事件做了合理的修正：將間隔時(shí)間小于0.5s的相鄰兩段鼾聲合并為一段；將間隔時(shí)間大于60s排除不做考慮。 本文提出了三種方法對(duì)睡眠呼吸暫停低通氣綜合癥診斷： (1)定義鼾聲間隔時(shí)間大于10s為呼吸暫停,若單位時(shí)間內(nèi)呼吸暫停的次數(shù)大于5次則判斷為有病,反之,判斷為無(wú)病。 (2)根據(jù)男女不同性別之間的差異按照不同的性別分別診斷,男性病例以單位時(shí)間呼吸暫停次數(shù)等于8為判斷閾值,女性病例以單位時(shí)間呼吸暫停次數(shù)等于6為判斷閾值。 (3)為了進(jìn)一步減小個(gè)體差異帶來(lái)的影響,基于K均值對(duì)鼾聲間隔時(shí)間分類為正常的間隔時(shí)間和呼吸暫停的間隔時(shí)間,根據(jù)兩類中心的距離和呼吸暫停次數(shù)判斷是否患病。 實(shí)驗(yàn)結(jié)果表明,三種方法都能夠?qū)崿F(xiàn)對(duì)睡眠呼吸暫停低通氣綜合癥的篩查,靈敏度和特異度分別高達(dá)100%和85.7%。
[Abstract]:Sleep apnea hypopnea syndrome (SPAHS) is a high risk disease with serious hazards and potential dangers. Hypoxia and hypercapnia often occur during sleep, which eventually lead to systemic multi-system, multi-organ progressive harm, and the patient's health is greatly affected. Polysomnography has long been recognized as the gold standard for the diagnosis of sleep apnea hypopnea syndrome. However, the number of polysomnography monitoring equipment is limited, and the majority of patients can not be treated in time. A low-cost, more convenient diagnostic method has been favored by many researchers. The aim of this study was to screen for sleep apnea hypopnea syndrome based on snoring detection. Firstly, the snoring signal is preprocessed based on speech processing. Secondly, the traditional short time energy, short time threshold rate and double threshold endpoint detection are discussed. An adaptive short-time energy endpoint detection method is proposed for snoring signal. Compared with the traditional fixed threshold endpoint detection, the new algorithm can dynamically adjust the energy threshold to detect the acoustic segment effectively. Due to the discontinuity of snoring, this paper makes a reasonable correction to the snoring event: merging the two adjacent snoring segments with interval time less than 0.5 s into one segment, and excluding the interval time greater than 60 s. This paper presents three methods for diagnosis of sleep apnea hypopnea syndrome: (1) define snoring interval longer than 10 s as apnea, and if the number of apnea per unit time is more than 5 times, it can be judged as diseased. On the other hand, it was judged as disease-free. (2) according to the difference of sex between men and women, the male cases were diagnosed according to different gender, and the threshold value of apnea per unit time was equal to 8. The threshold of apnea per unit time was equal to 6. (3) in order to further reduce the impact of individual differences, The snoring interval time was classified as normal interval time and apnea interval time based on K-means, and the distance between the two types of centers and the number of apnea were used to judge whether the disease was ill or not. The results show that the three methods can be used to screen sleep apnea hypopnea syndrome with sensitivity of 100% and specificity of 85.7%.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2010
【分類號(hào)】：R766

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 趙玉霞;基于共振峰的OSAHS篩查[D];大連理工大學(xué);2011年

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本文編號(hào)：2083959