V K BAIRAGI Research Paper
                     Title:  Classification of Chronic Obstructive Pulmonary Disease (COPD) Using 
Electromyography
Authors : V K BAIRAGI & ARCHANA B KANWADE
Abstract :
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 analysing 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.