Abstract
This article describes a method for detection of cognitive defects based on eye tracking during reading. The aim of this research is to pursue and extend the experiments conducted in Sweden by introducing Conventional signal theory. The dataset used for experiment was acquired by the authors of the research article Screening for Dyslexia Using Eye Tracking during Reading. The provided data consist of 185 subjects divided into two groups. The first group comprises of 88 low risk (LR) subjects and the second group comprises of 97 high risk (HR) subjects. Our measurements achieved a classification accuracy score 86.164% by classifying the subjects into the correct groups.
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Acknowledgment
I would like to thank the authors for providing the dataset and information regarding the previous research Screening for Dyslexia Using Eye Tracking during Reading. Mattias Nilsson Benfatto, Gustaf Orqvist Seimyr, Jan Ygge, Ten Pansell, Agneta Rydberg, Christer Jacobson.
This research was supported by the VEGA 1/0440/19 research grant.
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Nerusil, B., Polec, J., Skunda, J. (2022). Fast Algorithm for Dyslexia Detection. In: Rozinaj, G., Vargic, R. (eds) Systems, Signals and Image Processing. IWSSIP 2021. Communications in Computer and Information Science, vol 1527. Springer, Cham. https://doi.org/10.1007/978-3-030-96878-6_14
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DOI: https://doi.org/10.1007/978-3-030-96878-6_14
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