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

• Type: Article in International Scientific Journal
• Date: 2022-11

Authors

Lucas Salvador Bernardo (Author) Other; Robertas Dama¨evičius (Author) Other; Sai Ho Ling (Author) Other; Victor Hugo C. de Albuquerque (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Biomedicines

Vol. 10

Pages: 2746-2746

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus

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Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00X-GN5

DOI: 10.3390/biomedicines10112746

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15