Alejandro Hernández-García’s scientific contributions

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Publications (1)

Figure 2: Examples of time series filtered using the TFCE algorithm with different combinations of parameters. A larger E weighs more the components of the original signal with longer temporal extent (e), defined as the time interval where the signal is consistently above a predefined minimum. Larger H, conversely, weighs more the highest components of the signal in terms of intensity (h). Note that the TFCE values here are normalized for visualization purposes only.
Figure 3: Schematic representation of the architecture of the deep recursive neural network.
Computational methods for continuous eye-tracking perimetry based on spatio-temporal integration and a deep recurrent neural network
  • Preprint
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May 2020

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67 Reads

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Alejandro Hernández-García

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Remco J. Renken

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Perimetry, the mapping of the sensitivity of different visual field locations, is an essential procedure in ophthalmology. Unfortunately, standard automated perimetry (SAP), suffers from some practical issues: it can be tedious, requires manual feedback and a high level of patient compliance. These factors limit the effectiveness of perimetry in some clinical populations. In an attempt to remove some of these limitations, alternatives to SAP have been tried based on tracking eye movements. These new approaches have attempted to mimic SAP, thus presenting stimuli on a fixed grid, and replacing manual by ocular responses. While this solves some issues of SAP, these approaches hardly exploit the high spatial and temporal resolution facilitated by eye-tracking. In this study, we present two novel computational methods that do tap into this potential: (1) an analytic method based on the spatio-temporal integration of positional deviations by means of Threshold Free Cluster Enhancement (TFCE) and (2) a method based on training a recursive deep artificial neural network (RNN). We demonstrate that it is possible to reconstruct visual field maps based on continuous gaze-tracking data acquired in a relatively short amount of time.

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