Can Gait Features Help in Differentiating Parkinson’s Disease Medication States and Severity Levels? A Machine Learning Approach
Chariklia Chatzaki,
Vasileios Skaramagkas,
Zinovia Kefalopoulou,
Nikolaos Tachos,
Nicholas Kostikis,
Foivos Kanellos,
Eleftherios Triantafyllou,
Elisabeth Chroni,
Dimitrios I. Fotiadis,
Manolis Tsiknakis
Affiliations
Chariklia Chatzaki
Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
Vasileios Skaramagkas
Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
Zinovia Kefalopoulou
Department of Neurology, Patras University Hospital, 26404 Patra, Greece
Nikolaos Tachos
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Nicholas Kostikis
PD Neurotechnology Ltd., 45500 Ioannina, Greece
Foivos Kanellos
PD Neurotechnology Ltd., 45500 Ioannina, Greece
Eleftherios Triantafyllou
Department of Neurology, Patras University Hospital, 26404 Patra, Greece
Elisabeth Chroni
Department of Neurology, Patras University Hospital, 26404 Patra, Greece
Dimitrios I. Fotiadis
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Manolis Tsiknakis
Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.
