U-Healthcare System for Pre-Diagnosis of Parkinson's Disease from Voice Signal
| Autor: | Sylvio Barbon Junior, Rodrigo Capobianco Guido, Shi-Huang Chen, Victor G. Turrisi da Costa |
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| Přispěvatelé: | Barbon Junior, S, Turrisi da Costa, V. G., Chen, S. -H., Guido, R. C. |
| Rok vydání: | 2018 |
| Předmět: |
Brain modeling
Decision support system Telemedicine Support vector machine Ubiquitous computing 020205 medical informatics Computer science Speech recognition Population Feature extraction 02 engineering and technology Machine learning algorithm [Parkinson's disease] 03 medical and health sciences 0302 clinical medicine Radio frequency 0202 electrical engineering electronic engineering information engineering Context awareness education Parkinson's disease: Machine learning algorithms education.field_of_study Support vector machines SIGNAL (programming language) 030217 neurology & neurosurgery |
| Zdroj: | ISM |
| Popis: | With the ageing and growth of the population, some chronic diseases, such as Parkinson's disease (PD), urge the society to a health-conscious looking for better health system designs. Some recent research endeavour has been supported by solutions grounded in ubiquitous healthcare (u-Health) coupling telemedicine, context awareness and decision support capabilities. In this work, we propose a u-healthcare system to pre-diagnose PD based on the speech signal of people under voice call. The speech stream is sampled as well as processed to support the pre-diagnose using machine learning (ML). Experiments were conducted over a PD voice dataset composed of 40 individuals by using five different ML algorithms. Based on a linear Support Vector Machine (SVM) model, a false negative rate of 10% was obtained when classifying the locution of number "three". |
| Databáze: | OpenAIRE |
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