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

• Main Authors: Yu-wei Juang, 莊育瑋
• Other Authors: Wei-Chih Hu
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/50820337143852474116

碩士 === 中原大學 === 生物醫學工程研究所 === 98 === Most existing ventilators are controlled by physical parameters such as the volume of air flow and air pressure. However, these parameters have their intrinsic limitations when applied to clinical applications such as the physiological adaptation control. Therefore, this thesis was aimed at identifying the differentiation between spontaneous breathing and compulsive breathing using electromyogram (EMG) signals to improve the schedule of lung ventilators. The respiratory EMG acquisition platform was integrated using a microprocessor (MSP430) to acquire, save and process the EMG signal. The processes of EMG signal were including the cancellation of ECG interference from diaphragmatic EMG and the calculation for physiological related information such as respiratory rate, heart rate and heart rate variability (HRV) etc. After all processes had been done on platform, the data could be conveyed to computer by USB for further analyses (such as the zero-cross rate and the spectrum of signal) and others application. There were significant difference (p<0.05) between spontaneous and compulsive breathing on the power spectrum. However, this thesis using zero-crossing rate for self pattern recognition. Furthermore, for breath detection using EMG signals, the accuracies were above 83% using semi-automatic method that was to set the threshold manually. A thermistor air flow of respiratory-detection was devised for the reference of the EMG respiratory-detection. The accuracies of described method was above 98.9%. Furthermore, the EMG-respiratory-detection method was checking against the downloaded data from HTUMIT-BIH Polysomnographic DatabaseUTH. Results showed that the method was sensitive to the muscular movement of respiration (particularly to the dyspnea data).