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

Electromyogram (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 (OSAS) and periodic limb movement syndrome (PLMS), are closely associated with the electrical activity of muscle tones. In this paper, a hybrid model containing wavelet packet transform (WPT) plus an extreme learning machine (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.

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