%0 Conference Proceedings
%T Discovering the Discriminating Power in Patient Test Features Using Visual Analytics: A Case Study in Parkinson’s Disease
%+ Centre for Research and Technology Hellas [Athènes] (CERTH)
%+ Department of Neurology (Aristotle University of Thessaloniki)
%+ UFR Sciences du Sport (STAPS) (Université de Bourgogne)
%+ Cognition, Action, et Plasticité Sensorimotrice [Dijon - U1093] (CAPS)
%+ Department of Physical Education and Sports Sciences (Aristotle University of Thessaloniki)
%A Moschonas, Panagiotis
%A Kalamaras, Elias
%A Papadopoulos, Stavros
%A Drosou, Anastasios
%A Votis, Konstantinos
%A Bostantjopoulou, Sevasti
%A Katsarou, Zoe
%A Papaxanthis, Charalambos
%A Hatzitaki, Vassilia
%A Tzovaras, Dimitrios
%Z EU
%Z NoTremor (EC FP7) 610391
%Z Part 11: New Methods and Tools for Big Data Wokshop (MT4BD)
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Thessaloniki, Greece
%Y Lazaros Iliadis
%Y Ilias Maglogiannis
%I Springer
%3 Artificial Intelligence Applications and Innovations
%V AICT-475
%P 600-610
%8 2016-09-16
%D 2016
%R 10.1007/978-3-319-44944-9_53
%K Parkinson’s disease
%K Visual analytics
%K Multi-objective optimisation
%K Feature discrimination power
%Z Computer Science [cs]
%Z Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Conference papers
%X This paper presents a novel methodology for selecting the most representative features for identifying the presence of the Parkinson’s Disease (PD). The proposed methodology is based on interactive visual analytic based on multi-objective optimisation. The implemented tool processes and visualises the information extracted via performing a typical line-tracking test using a tablet device. Such output information includes several modalities, such as position, velocity, dynamics, etc. Preliminary results depict that the implemented visual analytics technique has a very high potential in discriminating the PD patients from healthy individuals and thus, it can be used for the identification of the best feature type which is representative of the disease presence.
%G English
%Z TC 12
%Z WG 12.5
%2 https://u-bourgogne.hal.science/hal-01478299/document
%2 https://u-bourgogne.hal.science/hal-01478299/file/430537_1_En_53_Chapter.pdf
%L hal-01478299
%U https://u-bourgogne.hal.science/hal-01478299
%~ INSERM
%~ UNIV-BOURGOGNE
%~ U1093
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-475