Title :
Automated Levodopa-induced dyskinesia assessment
Author :
Tsipouras, Markos G. ; Tzallas, Alexandros T. ; Rigas, Georgios ; Bougia, Panagiota ; Fotiadis, Dimitrios I. ; Konitsiotis, Spyridon
Author_Institution :
Dept. of Mater. Sci. & Eng., Univ. of Ioannina, Ioannina, Greece
fDate :
Aug. 31 2010-Sept. 4 2010
Abstract :
An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject´s body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).
Keywords :
accelerometers; biomechanics; diseases; drugs; feature extraction; gyroscopes; medical signal processing; signal classification; Parkinson´s disease; accelerometers; automated Levodopa-induced dyskinesia assessment; classification technique; feature extraction; gyroscopes; signal analysis; Accelerometers; Accuracy; Classification tree analysis; Diseases; Feature extraction; Gyroscopes; Acceleration; Algorithms; Antiparkinson Agents; Automation; Biosensing Techniques; Dyskinesia, Drug-Induced; Equipment Design; Humans; Levodopa; Models, Statistical; Monitoring, Ambulatory; Parkinson Disease; Programming Languages; Reproducibility of Results; Signal Processing, Computer-Assisted;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
Print_ISBN :
978-1-4244-4123-5
DOI :
10.1109/IEMBS.2010.5626130