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Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
- Source :
- Biomedicines; Volume 10; Issue 11; Pages: 2746
- Publication Year :
- 2022
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2022.
-
Abstract
- Parkinson’s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject’s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.
- Subjects :
- key typing
Technological sciences, Medical and Health sciences
Ciências médicas e da saúde
Medical and Health sciences
Parkinson’s disease
neurodegeneration
deep learning
Medicine (miscellaneous)
early diagnosis
convolutional network
General Biochemistry, Genetics and Molecular Biology
Ciências Tecnológicas, Ciências médicas e da saúde
Subjects
Details
- Language :
- English
- ISSN :
- 22279059
- Database :
- OpenAIRE
- Journal :
- Biomedicines; Volume 10; Issue 11; Pages: 2746
- Accession number :
- edsair.doi.dedup.....e8b9c4b68d67e5c8a8fb798aad4c4d9d
- Full Text :
- https://doi.org/10.3390/biomedicines10112746