A neural network approach to distinguish Parkinsonian tremor from advanced essential tremor
- 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 ofA 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.…
| Author: | A. Hossen, Muthuraman MuthuramanORCiDGND, Jan Raethjen, Günther Deuschl, U. Heute |
|---|---|
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/110354 |
| ISBN: | 9788132204862OPAC |
| ISBN: | 9788132204879OPAC |
| ISSN: | 1867-5662OPAC |
| ISSN: | 1867-5670OPAC |
| Parent Title (English): | Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), December 20-22, 2011, volume 1 |
| Publisher: | Springer India |
| Place of publication: | New Delhi |
| Editor: | Kusum Deep, Atulya Nagar, Millie Pant, Jagdish Chand Bansal |
| Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2012 |
| Release Date: | 2023/12/20 |
| First Page: | 1051 |
| Last Page: | 1060 |
| Series: | Advances in Intelligent and Soft Computing ; 130 |
| DOI: | https://doi.org/10.1007/978-81-322-0487-9_96 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Informatik in der Medizintechnik | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
