TY - GEN
T1 - A neural network approach to distinguish Parkinsonian tremor from advanced essential tremor
AU - Hossen, A.
AU - Muthuraman, M.
AU - Raethjen, J.
AU - Deuschl, G.
AU - Heute, U.
PY - 2012
Y1 - 2012
N2 - A new technique for discrimination between Parkinsonian tremor and essential tremor is investigated in this paper. The method is based on spectral analysis of both accelerometer and surface EMG signals with neural networks. The discrimination system consists of two parts: feature extraction part and classification (distinguishing) part. The feature extraction part uses the method of approximate spectral density estimation of the data by implementing the wavelet-based soft decision technique. In the classification part, a machine learning approach is implemented using back-propagation supervised neural network. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the important features used for distinguishing between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance.
AB - A new technique for discrimination between Parkinsonian tremor and essential tremor is investigated in this paper. The method is based on spectral analysis of both accelerometer and surface EMG signals with neural networks. The discrimination system consists of two parts: feature extraction part and classification (distinguishing) part. The feature extraction part uses the method of approximate spectral density estimation of the data by implementing the wavelet-based soft decision technique. In the classification part, a machine learning approach is implemented using back-propagation supervised neural network. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the important features used for distinguishing between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance.
KW - Accelerometer
KW - Artificial Neural Networks
KW - EMG
KW - Essential Tremor
KW - Parkinsonian Tremor
KW - Soft-Decision Technique
KW - Wavelet-Decomposition
UR - http://www.scopus.com/inward/record.url?scp=84861148353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861148353&partnerID=8YFLogxK
U2 - 10.1007/978-81-322-0487-9_96
DO - 10.1007/978-81-322-0487-9_96
M3 - Conference contribution
AN - SCOPUS:84861148353
SN - 9788132204862
T3 - Advances in Intelligent and Soft Computing
SP - 1051
EP - 1060
BT - Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011
T2 - International Conference on Soft Computing for Problem Solving, SocProS 2011
Y2 - 20 December 2011 through 22 December 2011
ER -