EMG classification in obstructive sleep apnea syndrome and periodic limb movement syndrome patients by using wavelet packet transform and extreme learning machine

Authors : Necmettin SEZGİN, Necmettin SEZGİN

Pages : 873-884

Doi:10.3906/elk-1210-6

View : 10 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Electromyogram insert ignore into journalissuearticles values(EMG); signals, measured at the skin surface, provide crucial access to the muscle tones of a body. Some diseases, such as obstructive sleep apnea syndrome insert ignore into journalissuearticles values(OSAS); and periodic limb movement syndrome insert ignore into journalissuearticles values(PLMS);, are closely associated with the electrical activity of muscle tones. In this paper, a hybrid model containing wavelet packet transform insert ignore into journalissuearticles values(WPT); plus an extreme learning machine insert ignore into journalissuearticles values(ELM); was proposed to classify EMG signals in OSAS and PLMS patients. At first, the WPT was used to extract the features of the EMG signal, and then these features were fed to the ELM classifier. The mean classification accuracy of the ELM was 96.85%. The obtained overall results were significant enough for specialists to diagnose OSAS and PLMS diseases. Furthermore, a remarkable relationship between OSAS and PLMS has been revealed.
Keywords : Wavelet packet transform, extreme learning machine, obstructive sleep apnea syndrome, periodic limb movement syndrome