Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shikha Tripathi, Alejandro Acien, Ashley A Holmes, Teresa Arroyo-Gallego, Luca Giancardo
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引用次数: 0

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.

利用按键动态检测帕金森病:一种自我监督方法。
目的:被动监测触摸屏交互所产生的击键动态信号可用于检测和跟踪神经系统疾病,如帕金森病(PD)和精神运动障碍,而且对用户的负担最小。然而,这通常需要在标准化环境中收集具有临床确认标签的数据集,这具有挑战性,尤其是对于大量受试者而言。本研究验证了自我监督学习方法在减少对标签的依赖方面的功效,并评估了其可推广性:我们提出了一种新型的自我监督损失,它结合了巴洛双胞胎损失(Barlow Twins loss)和不相似性损失(Dissimilarity loss)。前者试图为来自同一研究对象的样本创建相似的特征表征,减少特征冗余;后者则为不同研究对象生成的样本提供不相关的特征。编码器首先在非控制环境下的无标记数据上使用该损失进行预训练,然后使用临床验证数据进行微调。我们的实验在两个独立的数据集上测试了模型在对照组和帕金森病患者中的泛化能力:结果:与之前的方法(包括特征工程策略、预训练帕金森病体征的深度学习模型和传统监督模型)相比,我们的方法显示出更好的泛化能力:由于缺乏标准化的数据采集协议,且注释数据集的可用性有限,监督模型的泛化能力大打折扣。在这种情况下,自监督模型具有从数据中学习更稳健模式的优势,而无需地面实况标签:这种方法有望加快触摸屏打字软件对神经退行性疾病的临床验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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