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Related Concept Videos

Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

671
Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Parkinson's Disease: Treatment01:24

Parkinson's Disease: Treatment

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Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...
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Related Experiment Video

Updated: Aug 22, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data.

Lucas Salvador Bernardo1, Robertas Damaševičius1, Sai Ho Ling2

  • 1Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania.

Biomedicines
|November 11, 2022
PubMed
Summary

This study introduces a novel method for detecting Parkinson's disease (PD) using a modified SqueezeNet convolutional neural network (CNN) that analyzes key-typing patterns. The AI model achieved 90% accuracy, offering a promising advancement in early PD detection.

Keywords:
Parkinson’s diseaseconvolutional networkdeep learningearly diagnosiskey typingneurodegeneration

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting over 6 million individuals globally.
  • PD is characterized by motor and non-motor symptoms, significantly impacting daily activities like typing.
  • Current diagnostic methods may not fully capture the subtle, early-stage motor changes.

Purpose of the Study:

  • To develop and evaluate a novel AI-based approach for detecting Parkinson's disease.
  • To investigate the efficacy of a modified SqueezeNet convolutional neural network (CNN) in identifying PD through typing patterns.
  • To improve the accuracy and accessibility of early Parkinson's disease detection.

Main Methods:

  • Utilized key-typing data from subjects.
  • Pre-processed data using standardization and Synthetic Minority Oversampling Technique (SMOTE).
  • Applied Continuous Wavelet Transformation to generate spectrograms for training a modified SqueezeNet CNN model.

Main Results:

  • The modified SqueezeNet model achieved a diagnostic accuracy of 90% for Parkinson's disease.
  • This accuracy represents a significant improvement over existing detection methods.
  • Typing patterns proved to be a viable biomarker for PD detection using AI.

Conclusions:

  • A modified SqueezeNet CNN model effectively detects Parkinson's disease using typing patterns with high accuracy.
  • This AI-driven approach offers a non-invasive and potentially earlier method for PD diagnosis.
  • Further research can explore integrating this method into routine health monitoring.