Researchers developed a non-invasive technique to diagnose and classify chronic obstructive pulmonary disease (COPD) using electromyography (EMG) signals from the sternomastoid muscle during respiration. They analyzed EMG signals in the time, frequency, and time-frequency domains, and developed an onset detection algorithm and conduction velocity measure that improved COPD detection accuracy to 98.61%. Researchers also used continuous wavelet transform analysis at specific frequencies to extract features and classify COPD severity with 85.89% accuracy. This technique provides an easy-to-use alternative to spirometry for COPD diagnosis and assessment.

1. 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.