Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis.
Using gait analysis’ parameters to classify Parkinsonism: A data mining approach / Ricciardi, C.; Amboni, M.; De Santis, C.; Improta, G.; Volpe, G.; Iuppariello, L.; Ricciardelli, G.; D'Addio, G.; Vitale, C.; Barone, P.; Cesarelli, M.. - In: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. - ISSN 0169-2607. - 180:(2019), p. 105033. [10.1016/j.cmpb.2019.105033]
Using gait analysis’ parameters to classify Parkinsonism: A data mining approach
Ricciardi C.;Improta G.;Iuppariello L.;Cesarelli M.
2019
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
