Using Keytyping as a Biomarker for Cognitive Decline Diagnostics: The Convolutional Neural Network Based Approach

Barnardo, Lucas Salvador; Damaševičius, Robertas; Maskeliūnas, Rytis

doi:10.1007/978-3-031-04112-9_28

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“…The motor symptoms characteristic of the disease affect simple daily activities performed by patients, such as typing on a computer’s keyboard [ 14 , 15 ]. In view of that, and aligned to the advances in AI-based solutions and current computational power, researchers [ 16 , 17 , 18 ] are aiming to find new approaches capable of detecting the motor impairments characteristic of PD [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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