※ 引述《spaceuit (0000000)》之铭言： : 在肌电讯号的特徵值分析中，自主与非自主呼吸在三个频段的频谱功率皆呈现显着差异(p<0.05)， : 而过零率上则有明显的自体差异。 : 在特徵值的应用上，是以半自动的方式做呼吸判断，其正确率皆高於83%； : 本研究以呼吸气流温度比对平台演算法对於呼吸肌电讯号处理在位置与呼吸数判断的正 : 确性，分别为98.9%与95.4%。 : 此外本研究以MIT-BIH资料库的多重睡眠电图下巴肌电讯号验证本研究的特徵值方法， : 不但都能反应出受测者的呼吸动作(特别是在呼吸窒息的状况)，在难以观测的肌电讯号 : 中也能看出其呼吸动作的特徵值变化。 : 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. 老实说我不知道什麽是 "自体差异"。 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. --

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