A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration

The alarming rate of mortality and disability due to Chronic Obstructive Pulmonary Disease (COPD) has become a serious health concern worldwide. The progressive nature of this disease makes it inevitable to detect this disease in its early stages, leads to a greater demand for developing non-obstructive and reliable technology for COPD detection. The use of highly patient-effort dependent, time-consuming, and expensive methods are some major inherent limitations of previous techniques. Lack of knowledge about the disease and inadequacy of proper diagnostic tool for early detection of COPD is another reason behind the 3rd leading cause of death worldwide. For this reason, this study aims to explore the utility of ECG Derived Respiration (EDR) for classification between COPD patients and normal healthy subjects as EDR can be easily extracted from ECG. ECG and respiration signals collected from 30 normal and 30 COPD subjects were analysed. Error calculation and statistical analysis were performed to observe the similarity between original respiration and EDR signal. The morphological pattern changes of respiration and EDR signals were analysed and three different features were extracted from those. Classification was performed by different classifiers employing Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Apart from obtaining comparable classification performance it was seen that EDR has better potential than the original respiration signal for classification of COPD from normal population.