DocumentCode :
3662514
Title :
Detecting Parkinson´s diseases via the characteristics of the intrinsic mode functions of filtered electromyograms
Author :
Yizhong Dai; Weichao Kuang;Bingo W. K. Ling; Zhijing Yang; Kim-Fung Tsang; Haoran Chi; Chung-Kit Wu;Henry Shu-Hung Chung;Gerhard P. Hancke
Author_Institution :
Sch. of Inf. Eng., G.D.U.T., Guangzhou, China
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1484
Lastpage :
1487
Abstract :
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.
Keywords :
"Band-pass filters","Finite impulse response filters","Feature extraction","Entropy","Approximation methods","Parkinson´s disease"
Publisher :
ieee
Conference_Titel :
Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on
ISSN :
1935-4576
Electronic_ISBN :
2378-363X
Type :
conf
DOI :
10.1109/INDIN.2015.7281952
Filename :
7281952
Link To Document :
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