Fuzzy Approach to Saccades Detection in Optokinetic Nystagmus

Czabański, Robert; Pander, Tomasz; Przybyła, Tomasz

doi:10.1007/978-3-319-02309-0_24

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The application of fuzzy set theory resulted in methods of fuzzy clustering which assume the possibility of partial membership of an element to a given group. The well known Fuzzy c-Means (FCM) [3] algorithm can be used in the procedure of the adaptive threshold adjustment [4,5]. However, the resulting FCM cluster representatives (prototypes) are the linear statistics of data points which are known to be vulnerable to outliers [7].…”

Section: Estimation Of Amplitude Threshold With Median Fuzzy Clusteringmentioning

confidence: 99%

The Application of Median Fuzzy Clustering and Robust Weighted Averaging for Electronystagmography Signal Processing

Pander

Czabański

Przybyła

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. The purpose of this paper is to present a robust approach to processing of electronystagmography (ENG) signal which contain saccades. The accurate saccade detection and localization allows for recognition of the optokinetic nystagmus (OKN), which is regarded as one of the important phenomena to be evaluated by ophthalmologists. The robust ENG processing consists in robust filtering of the signal to suppress outliers, robust fuzzy clustering to determine the amplitude threshold level for automatic saccade detection and robust averaging of similar optokinetic nystagmus cycles to estimate the nystagmus parameters. The proposed method was tested using the artificial as well as the real signals. The results show the usefulness of the proposed solution in the process of the OKN modeling.

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Section: Estimation Of Amplitude Threshold With Median Fuzzy Clusteringmentioning

confidence: 99%

The Application of Median Fuzzy Clustering and Robust Weighted Averaging for Electronystagmography Signal Processing

Pander

Czabański

Przybyła

et al. 2015

Advances in Intelligent Systems and Computing

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Topology for gaze analyses - Raw data segmentation

Hein

Zangemeister²

2017

JEMR

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Recent years have witnessed a remarkable growth in the way mathematics, informatics, and computer science can process data. In disciplines such as machine learning, pattern recognition, computer vision, computational neurology, molecular biology, information retrieval, etc., many new methods have been developed to cope with the ever increasing amount and complexity of the data. These new methods offer interesting possibilities for processing, classifying and interpreting eye-tracking data. The present paper exemplifies the application of topological arguments to improve the evaluation of eye-tracking data. The task of classifying raw eye-tracking data into saccades and fixations, with a single, simple as well as intuitive argument, described as coherence of spacetime, is discussed, and the hierarchical ordering of the fixations into dwells is shown. The method, namely identification by topological characteristics (ITop), is parameter-free and needs no pre-processing and post-processing of the raw data. The general and robust topological argument is easy to expand into complexsettings of higher visual tasks, making it possible to identify visual strategies. As supplementary file an interactive demonstration of the method can be downloaded,

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