Human Breathing Classification Using Electromyography Signal with Features Based on Mel-Frequency Cepstral Coefficients

Abstract

Typical method on assessing the human breathing characteristics is based on measurements of breathing air parameters. Another possible method for human breathing assessment is through the analysis of respiratory muscles electromyography (EMG) signal. The EMG signal from different breathing task will be analyzed in order to determine the characteristics of the EMG signal pattern. Thus, feature extraction need to be done on the EMG signals. This paper  will look into the use of Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG signal. Analysis is done using different data analysis frame sizes. EMG signal classification is done using K-Nearest Neighbour. Results shows that MFCC is a good feature extraction method for this purpose with classification accuracy exceeds more than 90% for data analysis frame size of 2000 ms, 4000 ms, 5000 ms and 10000 ms.