Detecting Parkinson's diseases via the characteristics of the intrinsic mode functions of filtered electromyograms

This paper proposes a novel method for detecting the Parkinson's diseases via applying the empirical mode decomposition to filtered electromyograms. First, the electromyograms are processed by different linear phase finite impulse response bandpass filters with different pairs of cutoff frequencies. Second, each filtered electromyogram is decomposed into several intrinsic mode functions. Third, both the entropies and the total numbers of the extrema of the intrinsic mode functions of each filtered electromyogram are computed and they are used as the features for detecting the Parkinson's diseases. Computer numerical simulation results show that the features are linearly separable. Hence, a simple perceptron can be employed for the detection of the Parkinson's diseases. Finally, the algorithm is implemented via a mobile application. Compared to conventional empirical mode decomposition approaches in which a predefined number of features is employed for detecting the Parkinson's diseases, our proposed method allows to use a flexible number of features for detecting the Parkinson's diseases. This is because the total number of filters to be employed is very flexible. As a result, our proposed method is more flexible than the existing methods.