基於心電圖推導呼吸型態之睡眠呼吸異常偵測系統

心電圖的振幅會因為呼吸時胸腔體積的改變而造成心電圖的振幅變化。相關研究指出，調變的心電圖訊號與間接的呼吸量測技術，如氣動呼吸傳感器帶及呼吸感應性體積計，具有高度相關性。利用此特性從心電圖推導的呼吸訊號，簡稱EDR；基於此相關性，可以使用EDR監測睡眠呼吸狀態與診斷睡眠呼吸中止症。本研究將呈現一個自動化系統，能夠由心電圖訊號直接推導呼吸訊號以偵測睡眠呼吸中止症是否發生。本系統使用資料擷取裝置取得病人的心電圖資料，同時推導出EDR訊號；此外，本系統亦提供訊號處理的使用者介面，讓醫生可以用遠端連線的方式觀察到病患在睡眠呼吸中止症液生時的心電圖及EDR訊號。本研究使用兩種EDR推導方法，第一種為振幅調解，另一種為使用cubic spline插補法；訊號處理驗證採用Physionet中睡眠呼吸中止症的心電圖資料與正常心電圖資料以比較此兩種方法的正確性。由結果可以發現，雖然由 plethysmograph所推導之呼吸間隔與EDR訊號間的相關性不高，但仍然存在相似性。此外，無論是由Physionet的心電圖資料或是實測的心電圖中推導的呼吸訊號，皆利用「呼吸擺動信號」來監測呼吸中止的症狀，此方法對於呼吸中止的檢測有很高的敏感度。儘管仍有改進的空間，但是本系統可以提供睡眠專家及醫生判斷睡眠呼吸中止症的必要資訊，特別是在長期的心電圖監控之下，由此系統可以增加診斷出睡眠呼吸中止症的正確率。

關鍵字

心電圖；呼吸；睡眠呼吸暫停

並列摘要

Electrocardiograph (ECG) amplitude is modulated through respiration due to thoracic cavity measurement variance with respect to the heart. This causes axis shifts in the ECG reference lead as the signal is being modulated. This modulating signal was found to have a correlation with certain indirect respiration techniques, such as pneumatic respiratory transducer belts and inductance plethysmograms. Termed ECG-Derived Respiration (EDR), this technique paves the way in improving sleep apnea screening and diagnosis using ECG signals because of this correlation. In this paper, a system intended for patients and sleep experts/physicians is presented where EDR signals are derived and screened for apnea using an automated technique. This system provides ECG acquisition that makes use of the National Instruments USB DAQ-6008 data acquisition device and the derivation of the EDR for the patient side, which is provided with a GUI for signal processing. A physician GUI is provided that gives remote access to the EDR and ECG signals for analysis and screening for sleep apnea. Two EDR methods were used in this paper, one uses amplitude demodulation while the other one makes use of a cubic spline interpolation technique. These methods are tested using an ECG-apnea dataset and a normal ECG and respiration data set acquired from Physionet. It was found that although correlations of the EDR with inductance plethysmograph signal segments are low, there exist certain similarities in these segments with low correlation. EDR estimates from the Physionet records and also from our own gathered data are then screened for apnea using the derivation of a “breath swing signal”. It was found to be highly sensitive in detecting apneas when compared to reference annotations. Even though this is the case, the presented system as well as the presented EDR derivation methods is still subject to improvement, and later on will be able to provide sleep experts and physicians with necessary information in order to accurately score events as apnea, especially for long records of ECG data during sleep.

並列關鍵字

sleep apnea ； respiration ； electrocardiography

參考文獻

1) Arunachalam, S. P. (2009). Real-Time Estimation of the ECG-Derived Respiration (EDR) Signal using a New Algorithm for Baseline Wander Noise Removal. 31st Annual International Conference of the IEEE EMBS, Minnesota, USA, 5681-5684.

2) Babaeizadeh, S., Zhou, S. H., Pittman, S. D., White, D. P. (2011). Electrocardiogram-derived respiration in screening of sleep-disordered breathing. Journal of Electrocardiology 44, 700-706.

4) Boyle, J. Bidargaddi, N. (2009). Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG. IEEE Transactions on Information Technology in Biomedicine 13, 890-896.

5) Bragge, T., Tarvainen, M.P., Karjalainen, P.A. (2004). High-Resolution QRS Detection Algorithm for Sparsely Sampled ECG Recordings. Univ. of Kuopio, Dept. of Applied Physics Report.

7) Cysarz, D., Zerm, R., Betterman, H., Frühwirth, M., Moser, M., Kröz, M. (2008). Comparison of Respiratory Rates Derived from Heart Rate Variability, ECG Amplitude, and Nasal/Oral Airflow. Annals of Biomedical Engineering 36, 2085-2094

國際替代計量

基於心電圖推導呼吸型態之睡眠呼吸異常偵測系統

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