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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (4): 349-354    DOI: 10.31083/j.jin.2018.04.0416
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Eye movement behavior identification for Alzheimer's disease diagnosis
Juan Biondi1, 2, *(), Gerardo Fernandez1, Silvia Castro1, 2, Osvaldo Agamennoni1, 3
1 Laboratorio de Desarrollo en Neurociencia Cognitiva, Instituto de Investigaciones en Ingeniería Eléctrica (IIIE), Departamento de Ingeniería Eléctrica y de Computadoras (DIEC), Universidad Nacional del Sur (UNS) - CONICET, San Andrés 800 – Bahía Blanca, 8000, Buenos Aires, Argentina
2 Laboratorio de Visualización y Computación Gráfica (VyGLab), Departamento de Ciencias e Ingeniería de la Computación (DCIC), Universidad Nacional del Sur (UNS), San Andrés 800 – Bahía Blanca, 8000, Buenos Aires, Argentina
3 Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC), San Andrés 800 – Bahía Blanca, 8000, Buenos Aires, Argentina
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Abstract  

We develop a deep-learning approach to differentiate between the eye movement behavior of people with neurodegenerative diseases during reading compared to healthy control subjects. The subjects with and without Alzheimer’s disease read well-defined and previously validated sentences including high- and low-predictable sentences, and proverbs. From these eye-tracking data trial-wise information is derived consisting of descriptors that capture the reading behavior of the subjects. With this information a set of denoising sparse-autoencoders are trained and a deep neural network is built using the trained autoencoders and a softmax classifier that identifies subjects with Alzheimer’s disease with 89.78% accuracy. The results are very encouraging and show that such models promise to be helpful for understanding the dynamics of eye movement behavior and its relation with underlying neuropsychological processes.

Key words:  Eye-tracking      Deep-learning      Alzheimer’s disease      neurodegenerative diseases      eye movement behavior      neuropsychological processes     
Submitted:  19 June 2017      Accepted:  09 January 2018      Published:  15 November 2018     
*Corresponding Author(s):  Juan Biondi     E-mail:  juan.biondi@uns.edu.ar

Cite this article: 

Juan Biondi, Gerardo Fernandez, Silvia Castro, Osvaldo Agamennoni. Eye movement behavior identification for Alzheimer's disease diagnosis. Journal of Integrative Neuroscience, 2018, 17(4): 349-354.

URL: 

https://jin.imrpress.com/EN/10.31083/j.jin.2018.04.0416     OR     https://jin.imrpress.com/EN/Y2018/V17/I4/349

Table 1  Variables employed for model construction
Name Description
nw Number of words in the sentence.
gaze Global (sentence) mean of the sum of fixation durations on the same word.
sd_gaze Standard deviation of gaze.
as Mean saccade amplitude in the sentence.
sd_as Standard deviation of as
ntf Count of the total number of fixations on the sentence.
ntm Count of the number of multifixations on the sentence.
dfp Mean duration of the first pass fixations on the sentence.
sd_dfp Standard deviation of dfp.
fpp Count of the number of first pass fixations on the sentence.
rf Count of refixations on the sentence.
nfu Count of unique fixations on the sentence.
dfu Mean duration of unique fixations on the sentence.
sd_dfu Standard deviation of dfu.
Fig. 1.  Classification result histogram giving the number of sentences split by “ground truth” values. Values below 0.5 are classified as Control, and higher values are classified as AD.

Fig. 2.  Classification results.Values below 0.5 are considered classified as Control (class 0), and higher values are considered classified as AD (class 1).

Fig. 3.  Number of misclassified sentences by type, split by “ground truth” label.

Fig. 4.  Parallel coordinates plot with two subsets of trials (one composed of AD subjects and the other of Control subjects) that have similar values for the input in each field and its codification within the different stages of the network. As expected, similar values encoded “together”. Control subjects encoded closer than AD subjects; this could be attributed to the high “within group” variability of the AD group.

Table 2  Comparison of mean diagnostic value given by the network and a “severity of disease” score given by psychiatrists.
ID Pat Mean SD Score Difference
58 0, 97 0, 17 0, 9 0, 07
57 0, 95 0, 17 0, 5 0, 45
66 0, 49 0, 32 0, 5 0, 01
60 0, 95 0, 16 0, 6 0, 35
56 0, 96 0, 16 0, 8 0, 16
55 0, 94 0, 17 0, 7 0, 24
63 0, 87 0, 25 0, 8 0, 07
64 0, 51 0, 34 0, 5 0, 01
70 0, 90 0, 24 0, 5 0, 40
69 0, 84 0, 25 0, 5 0, 34
65 0, 91 0, 22 0, 6 0, 31
59 0, 58 0, 36 0, 6 0, 02
71 0, 75 0, 33 0, 6 0, 15
62 0, 76 0, 32 0, 6 0, 16
67 0, 47 0, 35 0, 5 0, 03
68 0, 40 0, 31 0, 8 0, 40
53 0, 84 0, 31 0, 8 0, 04
Mean 0, 19
SD 0, 15
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