如今， 睡眠障礙正困擾著世界上約1/3的人們， 睡眠品質受到了高度地重視。其中， 睡眠呼吸中止癥 (SAS) 是睡眠問題中的一個重大議題， 而阻塞性睡眠呼吸中止癥 (OSAS) 佔了約90%。 睡眠呼吸中止癥患者通常伴有打鼾、 睡眠結構紊亂、 頻繁血氧飽和度下降、 白天嗜睡、 注意力不集中等病癥， 並可能導致高血壓、 冠心病、 II型糖尿病等疾病。
目前多導睡眠儀 (polysomnography, PSG) 仍為研究OSAHS的必要手段， 用PSG監測患者整夜的睡眠是診斷OSAHS的主要依據， 對評估患者病情， 選擇治療手段具有極高價值。 然而PSG費用不菲， 導聯訊號多而複雜， 患者可能因不習慣， 或導聯與身體接觸感到不適等原因， 監測時睡眠誤差導致難以入睡， 從而造成結果的偏差。 並且患者需要住院檢查， 受到了耗時且床位緊張等因素的限制， 不適宜普遍使用。
因此， 我們的研究旨在開發一套準確、 經濟、 簡單、 便於攜帶的OSAHS診斷系統， 對PSG應用局限之處如OSAHS大規模篩查中起到補充甚至替代作用。 前一階段的研究已經完成了系統中對心電圖 (ECG)、 血氧濃度 (SpO2) 的記錄及分析， 本文主要是基于鼾聲訊號的聲學特徵方面進行分析。 將收集的鼾聲訊號經由MATLAB程序進行特徵擷取， 對頻譜分佈， 800Hz功率比 (PR800)， 共振峰频率以及Mel頻率倒譜係數 (MFCCs ) 等參數進行分析， 從而為構建OSA監測系統提供基礎。

Currently, about one-third of the world population suffers from sleep disorders, and as such, the quality of sleep has been an active topic of research. Sleep apnea syndrome is one of these sleep disorders, and obstructive sleep apnea syndrome (OSAS) has dominated due to the 90 percent such a high percentage compared to other types among it. Sleep apnea patients often suffer from snoring, sleep disorganization, continual oxygen desaturation, daytime sleepiness, impaired concentration, and various other symptoms. Moreover, OSAS may be related to cardiovascular disease.
Currently, polysomnography (PSG) is the primary method to study OSAS. Data derived from monitoring the sleep of patients overnight with PSG is the main basis for diagnosing OSAS and allows patient assessment and the selection of appropriate therapeutic measures. However, PSG is very costly, and its leading signals have high multiplicity and complexity. During the procedure, patients may have difficulty sleeping due to being unaccustomed to the measurement apparatus or they may feel uncomfortable, which may lead to deviation. Additionally, patients need to stay in the hospital overnight. Due to the fact that the procedure is time consuming and since there is often a shortage of beds, PSG is not suitable for widespread use.
Therefore, the main aim of our study is to construct an accurate, economic, simple, and portable OSAS diagnosis system that can overcome the limitations of PSG such as mass screening of OSAS. Previous research has reported a system of recording and analyzing the electrocardiogram (ECG) and oxygen saturation (SpO2) levels. In this study, our major work is the acoustic analysis of snoring signals recorded by microphone. We used MATLAB to extract features of collected snoring signals, and then analyzed features containing frequency distribution, power ratio 800 (PR800), formant frequency, and Mel frequency cepstrum coefficients (MFCCs), which can provide the basis of construction of our OSAS detection system.

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