Alonso Joan Francesc, Mañanas Miguel A, Hoyer Dirk, Topor Zbigniew L, Bruce Eugene N
Department of Automatic Control, Biomedical Engineering Research Center, Technical University of Catalonia (UPC), 5th Pau Gargalo St., E-08028 Barcelona, Spain.
IEEE Trans Biomed Eng. 2007 Sep;54(9):1573-82. doi: 10.1109/TBME.2007.893494.
Analysis of respiratory muscles activity is an effective technique for the study of pulmonary diseases such as obstructive sleep apnea syndrome (OSAS). Respiratory diseases, especially those associated with changes in the mechanical properties of the respiratory apparatus, are often associated with disruptions of the normally highly coordinated contractions of respiratory muscles. Due to the complexity of the respiratory control, the assessment of OSAS related dysfunctions by linear methods are not sufficient. Therefore, the objective of this study was the detection of diagnostically relevant nonlinear complex respiratory mechanisms. Two aims of this work were: (1) to assess coordination of respiratory muscles contractions through evaluation of interactions between respiratory signals and myographic signals through nonlinear analysis by means of cross mutual information function (CMIF); (2) to differentiate between functioning of respiratory muscles in patients with OSAS and in normal subjects. Electromyographic (EMG) and mechanomyographic (MMG) signals were recorded from three respiratory muscles: genioglossus, sternomastoid and diaphragm. Inspiratory pressure and flow were also acquired. All signals were measured in eight patients with OSAS and eight healthy subjects during an increased respiratory effort while awake. Several variables were defined and calculated from CMIF in order to describe correlation between signals. The results indicate different nonlinear couplings of respiratory muscles in both populations. This effect is progressively more evident at higher levels of respiratory effort.
呼吸肌活动分析是研究诸如阻塞性睡眠呼吸暂停综合征（OSAS）等肺部疾病的有效技术。呼吸系统疾病，尤其是那些与呼吸器官机械性能变化相关的疾病，常常伴随着呼吸肌正常高度协调收缩的紊乱。由于呼吸控制的复杂性，用线性方法评估与OSAS相关的功能障碍是不够的。因此，本研究的目的是检测具有诊断意义的非线性复杂呼吸机制。这项工作有两个目标：（1）通过交叉互信息函数（CMIF）进行非线性分析，评估呼吸信号与肌电图信号之间的相互作用，从而评估呼吸肌收缩的协调性；（2）区分OSAS患者和正常受试者呼吸肌的功能。从颏舌肌、胸锁乳突肌和膈肌这三块呼吸肌记录肌电图（EMG）和机械肌电图（MMG）信号。还采集了吸气压力和流量。在八名OSAS患者和八名健康受试者清醒时呼吸负荷增加期间测量所有信号。从CMIF定义并计算了几个变量，以描述信号之间的相关性。结果表明两组人群中呼吸肌的非线性耦合不同。在更高的呼吸负荷水平下，这种效应越来越明显。
