Time-Frequency Analysis of Electronystagmogram Signals in Patients with Congenital Nystagmus

Hosokawa, Mio; Hasebe, Shinji; Ohtsuki, Hiroshi; Tsuchida, Yozo

doi:10.1007/s10384-003-0052-9

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…During the urgent evaluation by neurology, an episode of nystagmus was also reported and pathology was ruled out by brain magnetic resonance imaging with thin slices in the axial plane in T1, FLAIR, and T2 and coronal images in T2 that were normal with and without contrast. In the deferred exploration by otolaryngology, audiometry and vestibular tests were performed, which were normal ( Hosokawa et al, 2004 ). In the case of an epidemic, it is critical to guarantee early detection of the infection in patients with acute neurological symptoms in order to prevent the disease from spreading.…”

Section: Introductionmentioning

confidence: 99%

Electrophysiological analysis of ENG signals in patients with Covid-19

Niazi,

Shankayi,

Asadi

et al. 2023

IBRO Neuroscience Reports

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Section: Introductionmentioning

confidence: 99%

Electrophysiological analysis of ENG signals in patients with Covid-19

Niazi,

Shankayi,

Asadi

et al. 2023

IBRO Neuroscience Reports

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“…Automatic processing of nystagmus signals is an established and alternative analysis method compared to manual inspection of nystagmus oscillations. The analysis techniques originate from a wide range of different disciplines such as control theory (Broomhead et al, 2000 ), dynamic systems modeling (Akman et al, 2005 ), time series analysis (Theodorou & Clement, 2016 ) and time-frequency analysis (Hosokawa et al, 2004 ). One may consider two different approaches to model nystagmus: system-based and signal-based modeling.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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, 𝜖 , 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 𝜖 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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“…Several detection methods have been published for nystagmus signal analysis since the 1970s [1][2][3][4][5][6][7][8][9][10][11][12]. One of the research details designed has been to detect nystagmus beats as accurately as possible.…”

Section: Introductionmentioning

confidence: 99%

“…A study of congenital nystagmus in infants was performed in which waveforms of different nystagmus beats were analysed [8], but the procedure for the computer analysis was not described. Congenital nystagmus containing waveform analysis was also studied in [9] for both children and adults by applying time-frequency analysis with the fast Fourier transform. The influence of blinking artefacts was identified along with their narrow frequency band in the spectrum.…”

Section: Introductionmentioning

confidence: 99%

The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods

Juhola

Aalto

Joutsijoki

et al. 2013

Advances in Artificial Neural Systems

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Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. We measured the spontaneous nystagmus of 107 otoneurological patients to form a training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record threedimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained.

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Time-Frequency Analysis of Electronystagmogram Signals in Patients with Congenital Nystagmus

Abstract: Some patients with congenital nystagmus showed clear periodicity in the nystagmus waveform without alternation of beat direction. The STFT is a useful tool to evaluate the temporal characteristics of congenital nystagmus in clinics.

Cited by 9 publications

References 19 publications

Electrophysiological analysis of ENG signals in patients with Covid-19

Electrophysiological analysis of ENG signals in patients with Covid-19

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

The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods

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