Classification of human breathing task based on electromyography signal of respiratory muscles

Breathing is one of the human physiological activities that catch the interest of researchers especially in the area of medical diagnosis and human physiological performance. Apart from conventional measurement using intake or outflow of air, breathing characteristics could also be assessed through human respiratory muscles with the analysis on Electromyography (EMG) signal. In this paper, EMG signal of human breathing is acquired from four respiratory muscles i.e. sternocleidomastoid, scalene, intercostal muscle and diaphragm while subjects perform four different breathing tasks. The aim is to classify EMG features from the muscles into the four breathing tasks. Classification is done using Feedforward Multi-layer Perceptron Artificial Neural Network (MLPANN). Four features are derived from the EMG data i.e. root-mean-square (RMS), zero crossing (ZC), mean frequency (MNF) and mean frequency power (MP). Classification is performed to compare the accuracy result of input vector from the four features of EMG and three combination set of these features using i) four data segmentation frame sizes and ii) six number of hidden neurons. The result of data classification shows highest accuracy when all feature sets is used as input to MLPANN with segmentation frame size of 1000 ms and number of hidden neurons of 60. Classification accuracy obtained is 59.52%.