Parkinson's disease diagnosis using modular systems
Paper in proceedings, 2013

In this paper, we present two modular systems for Parkinson's disease diagnosis. Also, we compare the frequency and chaotic behavior of rest tremor velocity in the index finger of some parkinsonian and healthy subjects. The proposed methods consist of two different modules, first, high-dimensional features are compressed by local linear and nonlinear principal component analysis (PCA) techniques and then, the features are classified by neural classifiers. The results indicate the efficiency of modular systems in Parkinson's disease diagnosis.

Author

Mona Noori-Hosseini

Chalmers, Signals and Systems, Systems and control, Automation

Behrooz Makki

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Proceedings - 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013

79-83

Subject Categories

Medical Engineering

DOI

10.1109/TAAI.2013.28

More information

Created

10/7/2017