Detailed Analysis of the Luria's Alternating SeriesTests for Parkinson's Disease Diagnostics

• Published in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 1347 - 1352
• Main Authors: Nomm, Sven, Bardos, Konstantin, Toomela, Aaro, Medijainen, Kadri, Taba, Pille
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2018
• Subjects: Acceleration; Data processing; Diseases; Feature extraction; healthy control subjects; Image edge detection; Kinematics; learning (artificial intelligence); Luria alternating series test; machine learning; medical computing; medical disorders; motor motions; neurology; neurophysiology; Object segmentation; Parkinson disease patients; Parkinson's disease; psychology; reference shape; severe neurologic disorder; stylus; tablet computer; Testing
• DOI: 10.1109/ICMLA.2018.00219

Patterns drawn by the patients during digital Luria's alternating series tests are analysed in this paper to support diagnostics of the Parkinson's disease. There are two main components that distinguish the approach proposed in this paper. The first one is the application of digital Luria's alternating series tests. In spite of its simplicity battery of the Luria's alternating series, tests allow not only to diagnose the disease but also may help to uncover on which level motor functions are affected. The second component is that kinematic and geometric features are computed not on the basis of the entire pattern but its "logical" constituents. Such an approach is justified by the fact that there is no formal description of the errors possible during the testing. Finally, it is demonstrated that classification decisions may be traced and interpreted.