Analysis of the electromyogram of rapid eye movement sleep using wavelet techniques

Quantitative electromyographic (EMG) signal analysis in the frequency domain using classical power spectrum analysis techniques has been well documented over the past decade. Yet due to the nature of EMG, frequency analysis cannot be used to approximate a signal whose properties change over time. To address this problem a time varying feature representation has to be analyzed to extract useful information from the signal. In this paper, wavelet analysis technique has been used to extract features from EMG, and linear discriminant analysis have been used to classify the signal into two classes, normal or abnormal, which reflects the loss of rapid eye movement sleep atonia commonly seen in Parkinson disease (PD). An overall classification accuracy of 94.3% was achieved.