建構呼吸肌電圖擷取平台及其在自主與非自主呼吸判斷之應用

Implementation of a Respiratory EMG Acquisition Platform for Differentiation between Spontaneous Breathing and Compulsive Breathing

10.29948/JAE.201104.0009

陳奕瑋(Yi-Wei Chen)；莊育瑋(Yu-Wei Chuang)；胡威志(Wei-Chih Hu)

呼吸機 ； 過零率 ； 頻譜功率 ； 多重睡眠電圖 ； ventilator ； zero-crossing rate ； power spectrum ； polysomnographic

先進工程學刊

6卷2期（2011 / 04 / 01）

131 - 139

繁體中文

The diaphragm EMG acquisition platform was integrated using a microprocessor (MSP430) to acquire, save and process the EMG signals. The processes of the EMG signals included the cancellation of ECG interference from diaphragmatic EMG and the calculation for physiology-related information such as the respiratory rate, heart rate and heart rate variability (HRV), and so forth. After all the processes had been completed on the platform, the data could be conveyed to a computer via USB for further analyses (such as the zero-cross rate and the spectrum of signals) and for other applications. There was significant difference in the power spectrum (p＜0.05) between spontaneous and compulsive breathing on the power spectrum. However, this article used the zero-crossing rate specifically for self pattern recognition. Accuracy in differentiating the breathing patterns using the EMG signals was found to be about 83% as a threshold was chosen manually. Furthermore, the accuracy became greater than 98.9%, when a thermistor for sensing the respiratory air flow was devised as the reference of the EMG respiration-detection.

The diaphragm EMG acquisition platform was integrated using a microprocessor (MSP430) to acquire, save and process the EMG signals. The processes of the EMG signals included the cancellation of ECG interference from diaphragmatic EMG and the calculation for physiology-related information such as the respiratory rate, heart rate and heart rate variability (HRV), and so forth. After all the processes had been completed on the platform, the data could be conveyed to a computer via USB for further analyses (such as the zero-cross rate and the spectrum of signals) and for other applications. There was significant difference in the power spectrum (p＜0.05) between spontaneous and compulsive breathing on the power spectrum. However, this article used the zero-crossing rate specifically for self pattern recognition. Accuracy in differentiating the breathing patterns using the EMG signals was found to be about 83% as a threshold was chosen manually. Furthermore, the accuracy became greater than 98.9%, when a thermistor for sensing the respiratory air flow was devised as the reference of the EMG respiration-detection.