利用心率變異度分析進行睡眠呼吸中止症偵測

Time-Domain Heart Rate Variability Analysis as a Tool for Sleep Apnea Detection

鄭嘉輝(Jarwin-Jim Ang Tee)

指導教授 ： 蔡育秀

中原大學/工學院/生物醫學工程研究所/碩士(2013年)

https://doi.org/10.6840/cycu201300130

摘要

睡眠阻塞性呼吸中止症（Obstructive Sleep Apnea, OSA），是一種在睡眠中重複發生的症狀。且由於呼吸道的阻塞，導致呼吸停止間歇性缺氧的情況，時常影響人們的睡眠情形。從過去的研究中了解到透過心率週期變化（cyclic variation of heart rate, CVHR）可以找出睡眠阻塞性呼吸中止症變化特徵－CVHR指數（每小時CVHR）與睡眠呼吸暫停低通氣（通氣不足）指數（AHI）。偵測心電圖中的R波，分析RR間期，再利用時域心律變異性針對睡眠阻塞性呼吸中止症進行特徵分析，已經被證明能夠成為分析OSA的重要工具。
這項研究旨在建構心電圖的擷取裝置以取得心律變異度（Heart rate variability, HRV），利用So and Chan演算法進行時域心律變異度分析。最後從Physionet資料庫中睡眠阻塞性呼吸中止症的心電圖資料進行演算法驗證，比對診斷結果。以此演算法分析得到的量測結果之精確度達到84%、靈敏度為90%、專一性為80%。實際應用上，利用NI DAQ-6008做擷取轉換，經由So and Chan演算法得出與Physionet資料庫相同的分析結果。

關鍵字

睡眠阻塞性呼吸中止症 ； 心率週期變化 ； 心律變異度

並列摘要

Obstructive Sleep Apnea (OSA) is a syndrome in which there is a repeated event of a partial or complete obstruction of the upper airway during sleep, resulting in intermittent hypoxia and transient repetitive arousals from sleep. The characteristic heart rate pattern, known as the cyclic variation of heart rate (CVHR), that is known to accompany OSA episodes had been demonstrated in earlier studies to be an effective tool in the detection of OSA due to the high correlation between the CVHR index (CVHR per hour) and the apnea-hypopnea index. Moreover, Time- domain HRV analysis has been proven as powerful tool in definitive diagnosis and classification of OSAS by using R-wave detection to extract and analyze the RR intervals of ECG readings.
In this study, the So and Chan algorithm for QRS detection was implemented along with time-domain HRV analysis in order to develop a system capable of deriving the required HRV characteristics for reliable diagnosis from ECG signals. The system was tested by using ECG recordings from Physionet’s Apnea-ECG database and also from ECG recorded using through the system. The results of the diagnosis from the Physionet data were then compared to the minute by minute classifications found in the Physionet database in order to test the reliability of the algorithm. The findings in the tests conducted have shown high accuracy, as high as 84% for recordings with severe apneas, and high sensitivity and specificity, around 90% and around 80% respectively. Real ECG data that was recorded using the National Instruments USB DAQ-6008 data acquisition device gave us similarly good results as with the analysis of the Physionet database.

並列關鍵字

Obstructive Sleep Apnea ； Cyclic variation heart rate ； heart rate variability

參考文獻

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