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Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

Authors :
Lucas Salvador Bernardo
Robertas Damaševičius
Sai Ho Ling
Victor Hugo C. de Albuquerque
João Manuel R. S. Tavares
MDPI AG (Basel, Switzerland)
Faculdade de Engenharia
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.

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