Tremor is one of the most important symptom in Parkinson’s disease, which has been assessed clinically by neurologists as part of UPDRS scale. In this paper, we have implemented a supervised learning pattern recognition system to assess UPDRS of each Parkinson patient tremor to fill the absence of a reliable diagnosis and monitoring system for Parkinson patients. In our system a simple noninvasive method based on the recorded acceleration through the smartphone have been used for data acquisition. The results show high accuracy in the classifier block and neural network. A tight correlation between UPDRS scale and acceleration values reveals 91 percent accuracy by neural network with two hidden layers.
A neural network system for diagnosis and assessment of tremor in parkinson disease patients / Bazgir, O.; Frounchi, J.; Habibi, S. A. H.; Palma, L.; Pierleoni, P.. - ELETTRONICO. - (2015), pp. 1-5. (Intervento presentato al convegno 2015 22nd Iranian Conference on Biomedical Engineering (ICBME) tenutosi a Tehran, Iran nel 25-27 Nov. 2015) [10.1109/ICBME.2015.7404105].
A neural network system for diagnosis and assessment of tremor in parkinson disease patients
Palma, L.;Pierleoni, P.
2015-01-01
Abstract
Tremor is one of the most important symptom in Parkinson’s disease, which has been assessed clinically by neurologists as part of UPDRS scale. In this paper, we have implemented a supervised learning pattern recognition system to assess UPDRS of each Parkinson patient tremor to fill the absence of a reliable diagnosis and monitoring system for Parkinson patients. In our system a simple noninvasive method based on the recorded acceleration through the smartphone have been used for data acquisition. The results show high accuracy in the classifier block and neural network. A tight correlation between UPDRS scale and acceleration values reveals 91 percent accuracy by neural network with two hidden layers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
