A novel method for chronic obstructive pulmonary disease detection using comprehensive respiratory impedance and electrocardiogram-derived respiration

Currently, pulmonary function test is the only standard method for diagnosing COPD, but it often involves high testing costs, poor compliance, and a high dependence on patient cooperation. Other new detection methods, such as CT and FOT, also have disadvantages such as radiation exposure and high usage costs. Existing non-invasive COPD detection methods consider the impact of COPD on the lungs or heart, thus they can only roughly detect COPD in individuals, without the ability to grade COPD. Therefore, this study proposed a method for COPD detection using comprehensive respiratory impedance (CRI) and electrocardio-derived respiration (EDR) signals. The method measured both respiratory impedance and ECG synchronously, and the CRI and EDR signals were obtained through an algorithm. The effects of COPD on respiratory pattern were studied by taking CRI and EDR signal morphology as objects, from which characteristic parameters were extracted and input into the supervised classifier to fuse the information of COPD on the changes of respiratory signal morphology pattern in the heart and lungs. The final result is accurate detection of normal, mild COPD and moderate COPD. The experimental results of 46 subjects showed that the accuracy, sensitivity and specificity of CRI and EDR signal characteristics for COPD detection by different classifiers were up to 93.5 %, 90.5 % and 96.5 %, and the AUC values of different classifiers were all > 0.88. Therefore, the method can not only effectively distinguish COPD patients from healthy people, but also provide differentiated detection capabilities for different COPD clinical grades, providing strong support for the early diagnosis and treatment of COPD, and can be applied to the development of portable medical devices for the detection of COPD.