Augmenting Dementia Cognitive Assessment With Instruction-Less Eye-Tracking Tests

• Published in: IEEE journal of biomedical and health informatics 2020-11, Vol.24 (11), p.3066-3075
• Main Authors: Mengoudi, Kyriaki, Ravi, Daniele, Yong, Keir X. X., Primativo, Silvia, Pavisic, Ivanna M., Brotherhood, Emilie, Lu, Kirsty, Schott, Jonathan M., Crutch, Sebastian J., Alexander, Daniel C.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biomarkers; Biomedical materials; Cognition; Cognitive ability; Deep learning; Dementia; Dementia disorders; Eye movements; Eye-tracking; Feature extraction; Learning; Machine learning; Neural networks; Pupils; representation learning; Representations; Semantics; Task analysis; Tracking
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2020.3004686

Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representation learning where, by training initially a deep neural network to solve a pretext task using well-defined available labels (e.g. recognising distinct cognitive activities in healthy individuals), the network encodes high-level semantic information which is useful for solving other problems of interest (e.g. dementia classification). Inspired by previous work in explainable AI, we use the Layer-wise Relevance Propagation (LRP) technique to describe our network's decisions in differentiating between the distinct cognitive activities. The extent to which eye-tracking features of dementia patients deviate from healthy behaviour is then explored, followed by a comparison between self-supervised and handcrafted representations on discriminating between participants with and without dementia. Our findings not only reveal novel self-supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of tasks, but also validate that instruction-less eye-tracking tests can detect oculomotor biomarkers of dementia-related cognitive dysfunction. This work highlights the contribution of self-supervised representation learning techniques in biomedical applications where the small number of patients, the non-homogenous presentations of the disease and the complexity of the setting can be a challenge using state-of-the-art feature extraction methods.