Generalizing Parkinson’s Disease Detection Using Keystroke Dynamics: A Self-Supervised Approach

Authors

Shikha Tripathi
Alejandro Acien
Ashley A Holmes
Teresa Arroyo-Gallego
Luca Giancardo

Publication Date

5-20-2024

Journal

Journal of the American Medical Informatics Association

Abstract

Objective: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on labels and evaluates its generalizability.

Materials and methods: We propose a new type of self-supervised loss combining Barlow Twins loss, which attempts to create similar feature representations with reduced feature redundancy for samples coming from the same subject, and a Dissimilarity loss, which promotes uncorrelated features for samples generated by different subjects. An encoder is first pre-trained using this loss on unlabeled data from an uncontrolled setting, then fine-tuned with clinically validated data. Our experiments test the model generalizability with controls and subjects with PD on 2 independent datasets.

Results: Our approach showed better generalization compared to previous methods, including a feature engineering strategy, a deep learning model pre-trained on Parkinsonian signs, and a traditional supervised model.

Discussion: The absence of standardized data acquisition protocols and the limited availability of annotated datasets compromise the generalizability of supervised models. In these contexts, self-supervised models offer the advantage of learning more robust patterns from the data, bypassing the need for ground truth labels.

Conclusion: This approach has the potential to accelerate the clinical validation of touchscreen typing software for neurodegenerative diseases.

Keywords

Humans, Parkinson Disease, Supervised Machine Learning, Male, Female, Aged, Algorithms, Middle Aged, Parkinson’s disease, neurodegenerative diseases, self-supervised learning, machine learning, user-device interaction

DOI

10.1093/jamia/ocae050

PMID

38497957

PMCID

PMC11105137

PubMedCentral® Posted Date

3-18-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes