| Title |
Modified SqueezeNet architecture for Parkinson’s disease detection based on keypress data / |
| Authors |
Salvador Bernardo, Lucas ; Damaševičius, Robertas ; Ling, Sai Ho ; de Albuquerque, Victor Hugo C ; Tavares, João Manuel R. S |
| DOI |
10.3390/biomedicines10112746 |
| Full Text |
|
| Is Part of |
Biomedicines.. Basel : MDPI. 2022, vol. 10, iss. 11, art. no. 2746, p. 1-15.. ISSN 2227-9059 |
| Keywords [eng] |
Parkinson’s disease ; neurodegeneration ; early diagnosis ; key typing ; deep learning ; convolutional network |
| Abstract [eng] |
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. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
