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Measuring Saccade Latency Using Smartphone Cameras.

Hsin-Yu Lai, Gladynel Saavedra-Pena, Charles G Sodini

    IEEE Journal of Biomedical and Health Informatics
    |May 7, 2019
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    Smartphone cameras can now reliably measure saccade latency, a key indicator of neurodegenerative disease progression. This technology enables objective, frequent tracking of disease in large patient groups outside clinical settings.

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    Area of Science:

    • Ophthalmology and Neuroscience
    • Biomedical Engineering
    • Digital Health

    Background:

    • Quantifying neurodegenerative disease progression is challenging.
    • Eye movement features show promise as objective biomarkers for diagnosis and tracking.
    • Saccade latency, a measure of reaction time, is a potential biomarker.

    Purpose of the Study:

    • To demonstrate robust measurement of saccade latency using a smartphone camera outside clinical settings.
    • To develop a framework for tracking saccade latency in large cohorts.
    • To enable objective, frequent monitoring of neurodegenerative disease progression.

    Main Methods:

    • Combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination.
    • Implemented automated signal-quality quantification and artifact rejection.
    • Validated smartphone recordings against high-speed cameras and assessed test-retest reliability.

    Main Results:

    • Smartphone saccade latency measurements showed negligible differences compared to high-speed cameras.
    • The approach demonstrated good to excellent test-retest reliability (ICC=0.76).
    • Over 19,000 measurements revealed significant intra- and inter-subject variability, emphasizing individualized tracking.
    • Mean saccade latency can be estimated to within 10 ms precision using approximately 65 measurements in under 4 minutes.

    Conclusions:

    • Smartphone-based saccade latency measurement is a reliable and feasible method for objective biomarker tracking.
    • This technology facilitates large-scale, frequent, and individualized monitoring of neurodegenerative disease progression.
    • The framework expands the possibilities for quantifying patient states on a finer timescale in broader populations.