Classification of Chronic Obstructive Pulmonary Disease (COPD) Using Electromyography

Chronic lung disease, in which the airway gets obstructed, is known as Chronic Obstructive Pulmonary Disease (COPD). According to WHO, COPD kills more than 3 million people every year. Spirometry is used to diagnose COPD; has many limitations. There is a need for physiologically accurate and easy to perform diagnosis technology. Researchers confirmed the activity of sternomastoid muscle in COPD with research limitations of; sample size, few time-domain features, lack of onset detection and the non-stationary nature of Electromyographical signals (EMG). In this, paper COPD diagnosis is made by analyzing Sternomastoid muscle of respiration in time, frequency and time-frequency domain. The slope-based onset detection algorithm and conduction velocity lead to an improvement in COPD detection accuracy to 98.61%. The feature selection algorithm is developed for the selection of the most significant features. A single frequency Continuous Wavelet Transform (CWT) analysis at 7, 8 and 10 Hz of frequency is used to extract features and to classify COPD in its grades, leading to the classification accuracy of 85.89%. Non-invasive, easy to use COPD diagnosis and classification technique is developed.