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Modeling and quality assessment of nystagmus eye movements recorded using an eye-tracker

Rosengren, William LU ; Nyström, Marcus LU orcid ; Hammar, Björn LU ; Rahne, Markus ; Sjödahl, Linnea and Stridh, Martin LU (2020) In Behavior Research Methods 52(4). p.1729-1743
Abstract

Mathematical modeling of nystagmus oscillations is a technique with applications in diagnostics, treatment evaluation, and acuity testing. Modeling is a powerful tool for the analysis of nystagmus oscillations but quality assessment of the input data is needed in order to avoid misinterpretation of the modeling results. In this work, we propose a signal quality metric for nystagmus waveforms, the normalized segment error (NSE). The NSE is based on the energy in the error signal between the observed oscillations and a reconstruction from a harmonic sinusoidal model called the normalized waveform model (NWM). A threshold for discrimination between nystagmus oscillations and disturbances is estimated using simulated signals and receiver... (More)

Mathematical modeling of nystagmus oscillations is a technique with applications in diagnostics, treatment evaluation, and acuity testing. Modeling is a powerful tool for the analysis of nystagmus oscillations but quality assessment of the input data is needed in order to avoid misinterpretation of the modeling results. In this work, we propose a signal quality metric for nystagmus waveforms, the normalized segment error (NSE). The NSE is based on the energy in the error signal between the observed oscillations and a reconstruction from a harmonic sinusoidal model called the normalized waveform model (NWM). A threshold for discrimination between nystagmus oscillations and disturbances is estimated using simulated signals and receiver operator characteristics (ROC). The ROC is optimized to find noisy segments and abrupt waveform and frequency changes in the simulated data that disturb the modeling. The discrimination threshold, 휖, obtained from the ROC analysis, is applied to real recordings of nystagmus data in order to determine whether a segment is of high quality or not. The NWM parameters from both the simulated dataset and the nystagmus recordings are analyzed for the two classes suggested by the threshold. The optimized 휖 yielded a true-positive rate and a false-positive rate of 0.97 and 0.07, respectively, for the simulated data. The results from the NWM parameter analysis show that they are consistent with the known values of the simulated signals, and that the method estimates similar model parameters when performing analysis of repeated recordings from one subject.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Eye tracking, Modelling, Nystagmus
in
Behavior Research Methods
volume
52
issue
4
pages
15 pages
publisher
Springer
external identifiers
  • pmid:32314183
  • scopus:85084038422
ISSN
1554-351X
DOI
10.3758/s13428-020-01346-y
language
English
LU publication?
yes
id
8c249cf4-47c7-4b8a-b90a-2349ae80895d
date added to LUP
2020-05-14 17:42:40
date last changed
2024-05-29 12:43:21
@article{8c249cf4-47c7-4b8a-b90a-2349ae80895d,
  abstract     = {{<p>Mathematical modeling of nystagmus oscillations is a technique with applications in diagnostics, treatment evaluation, and acuity testing. Modeling is a powerful tool for the analysis of nystagmus oscillations but quality assessment of the input data is needed in order to avoid misinterpretation of the modeling results. In this work, we propose a signal quality metric for nystagmus waveforms, the normalized segment error (NSE). The NSE is based on the energy in the error signal between the observed oscillations and a reconstruction from a harmonic sinusoidal model called the normalized waveform model (NWM). A threshold for discrimination between nystagmus oscillations and disturbances is estimated using simulated signals and receiver operator characteristics (ROC). The ROC is optimized to find noisy segments and abrupt waveform and frequency changes in the simulated data that disturb the modeling. The discrimination threshold, 휖, obtained from the ROC analysis, is applied to real recordings of nystagmus data in order to determine whether a segment is of high quality or not. The NWM parameters from both the simulated dataset and the nystagmus recordings are analyzed for the two classes suggested by the threshold. The optimized 휖 yielded a true-positive rate and a false-positive rate of 0.97 and 0.07, respectively, for the simulated data. The results from the NWM parameter analysis show that they are consistent with the known values of the simulated signals, and that the method estimates similar model parameters when performing analysis of repeated recordings from one subject.</p>}},
  author       = {{Rosengren, William and Nyström, Marcus and Hammar, Björn and Rahne, Markus and Sjödahl, Linnea and Stridh, Martin}},
  issn         = {{1554-351X}},
  keywords     = {{Eye tracking; Modelling; Nystagmus}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{1729--1743}},
  publisher    = {{Springer}},
  series       = {{Behavior Research Methods}},
  title        = {{Modeling and quality assessment of nystagmus eye movements recorded using an eye-tracker}},
  url          = {{http://dx.doi.org/10.3758/s13428-020-01346-y}},
  doi          = {{10.3758/s13428-020-01346-y}},
  volume       = {{52}},
  year         = {{2020}},
}