睡眠期間呼吸流量極限值訊號之分析識別

Analysis and Recognizing of Inspiratory Flow Limitation Signals During Sleep

施博鐘(Po-Chung Shih)

指導教授 ： 林康平

中原大學/電機資訊學院/通訊工程碩士學位學程/碩士(2013年)

https://doi.org/10.6840/cycu201300989

摘要

摘要
阻塞性呼吸中止症是一種睡眠期間因呼吸道阻塞，導致間歇性呼吸停止的疾病。流量限制(Flow Limitation)為阻塞性呼吸的一種重要徵狀，但是目前檢測流量限制的方法並不明確。
本論文所使用之訊號來源，為多重睡眠生理記錄儀(PSG)紀錄之臨床受測者，擷取呼吸流量認定為呼吸氣流受限(RERA)部分的47種不同類別呼吸流量訊號，其中標記正常的有8筆資料，標記為流量限制有39筆資料，其中還可細分為輕度阻塞及重度阻塞。
本論文先評估三種專利方法，當作本研究檢測之基準。此方法首先檢測為平坦指數，只需取樣整個吸氣中間25%~75%部分做平均差，再除以其平均值做誤差的總和。此外，再以加權平坦指數將平坦指數為基礎之加權改良。分別以數值加權平坦指數加權平均以上的資料。以及時間加權平坦指數加權吸氣波形後段的資料。
另外，本研究提出四種檢測方法分別為。(1)線性曲線檢測 (2)二階曲線檢測 (3)三階曲線檢測，以上三種方法皆為計算資料之擬合曲線，並求出其平均絕對誤差。最後，再增加(4)加權曲線檢測，此方法與三階曲線相似，但在開頭、結尾、中間對曲線做加權，達到最佳曲線擬合。
本研究共將47筆呼吸流量資料與其振幅、時間、加入不同程度雜訊，其可產生約1400多種吸氣波型之變形，並使用上述檢測方法分析，進行效果比較。呼吸道通暢部分七種檢測法結果都相當一致。但在較難以辨別的部分，時間加權平坦指數，一階曲線，二階曲線是不太有參考價值，各種結果與醫生研判之結果差異較大。平坦指數、數值加權平坦指數、三階曲線此三種檢測法為較接近現實結果，加權三階曲線則可達約99%準確率。.

關鍵字

阻塞型睡眠呼吸暫停 ； 流量限制 ； 峰值吸氣流量 ； 平坦指數 ； 睡眠呼吸中止症

並列摘要

Abstract
Obstructive Sleep Apnea (OSA) is an intermittent respiratory arrest disease due to airway obstructed during sleep. Flow limitation is an important symptom of OSA, but the method of detecting flow limitation was not clear now.
The signal sources were Polysomnography (PSG) records of clinical patients. 47 different categories of respiratory flow signals identified as RERA respiratory flow were captured. Among those, 8 cases were marked normal, and 39 were marked as flow limitation which can be further categorized into mild and severe obstruction.
3 kinds of methods were used as detection standards: (1) Flattening index (F.I) feature: an accepted standard for the majority of studies. Sample the middle portion of the inspiratory wave to calculate mean deviations, which were divided by its mean to acquire sum of errors. Weighted F.I was F.I after weighted improvements. (2) Value Weight: Weighted data above average of inspiratory waveform. (3)Time Weight: Weighted data from the later part of inspiratory waveform.
Four alternative detection methods were proposed in this study: (1) First Order Detection, (2) Second Order Detection, and (3) Third Order Detection. The three methods were calculating the fitting curve of data and obtained the mean absolute error. The forth method was Weighted Curve Detection. It was similar to the Third Order Curve but the curve was weighted at the beginning, the middle and the end to achieve optimal fitting curves.
Various levels of noises were added in the amplitude and time of 47 cases of respiratory flow. More than 1400 acquired inspiratory waveforms were analyzed using the proposed 7 detection methods and the performances were compared. 7 methods obtained consistent results in normal cases. However, in the parts that were difficult to recognize, the results from Time Weight, First Order Detection and Second Order Detection were insignificant due to the larger differences with the clinical diagnosis. F.I, Value Weight and Third Order Detection were more consistent with the actual results. Third Order Detection could achieve 99% of accuracy.

並列關鍵字

Flow Limitation ； Sleep Apnea ； Flattening index ； Obstructive Sleep Apnea ； Peak Respiratory Flow

參考文獻

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