※ 引述《spaceuit (0000000)》之銘言： : 在肌電訊號的特徵值分析中，自主與非自主呼吸在三個頻段的頻譜功率皆呈現顯著差異(p<0.05)， : 而過零率上則有明顯的自體差異。 : 在特徵值的應用上，是以半自動的方式做呼吸判斷，其正確率皆高於83%； : 本研究以呼吸氣流溫度比對平台演算法對於呼吸肌電訊號處理在位置與呼吸數判斷的正 : 確性，分別為98.9%與95.4%。 : 此外本研究以MIT-BIH資料庫的多重睡眠電圖下巴肌電訊號驗證本研究的特徵值方法， : 不但都能反應出受測者的呼吸動作(特別是在呼吸窒息的狀況)，在難以觀測的肌電訊號 : 中也能看出其呼吸動作的特徵值變化。 : The characteristics of EMG signal between spontaneous and compulsive breathing : were analyzed, and the results of the each of three bands showed significant : differences (p<0.05) on power spectrum analyses. By analyzing the characteristic shapes of EMG signals, we found significant difference (p<0.05) in the power spectra obtained from three freuqency bands, respectively, between spontaneous and compulsive breathing signals. : However, there were only self-difference on zero-cross rate. 老實說我不知道什麼是 "自體差異"。 We also found evident self-difference on the zero-crossing rate. : Additionally, the characteristic-methods were applied to detect the breath : semi-automatically in EMG, and the accuracies of characteristic-methods were : all above 83%; The respiratory-detection method performed on an : EMG-acquisition platform was estimated by comparing the position of : breathing peak and the count of respiration with the air temperature signal, : and the accuracies were 98.9% and 95.4% respectively. Furthermore, an accuracy of 83% was obtained by applying the new method to semi-automated breathing detection using EMG signals. Measured by the positions of breathing peaks and the counts of respiration, our respiratory-detection method shows accuracies of 98.9% and 95.4%, respectively, based on an EMG-acquisition platform. : Furthermore, the chin EMG signals from MIT-BIH Polysomnographic Database were : carried out by characteristics-method; : as a result, the responses of all characteristics of EMG were agreement with : not only the average signal (especially at the status of dyspnea) : but also the hard-to-detect signal. To perform further validation, chin signals downloaded from MIT-BIH's Polysomnographic Database were analyzed by our respiratory-detection method. Results show that the new method successfully recovers the breathing movement (particularly in dyspnea) of the participants. We have also demonstrated that the method is able to detect the minor differences in breathing movement using the characteristic shape analysis of EMG signals. --

※ 發信站: 批踢踢實業坊(ptt.cc) ◆ From: 120.126.36.193 ※ 編輯: astar 來自: 120.126.36.193 (05/05 16:48) ※ 編輯: astar 來自: 120.126.36.193 (05/05 16:53)