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Open AccessArticle

Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification

1
Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Spain
2
MSC Lab, ENSA, Cadi Ayyad University, Marrakech 40000, Morocco
3
Center for Computational Simulation, Universidad Politécnica de Madrid, 28040 Madrid, Spain
4
Department of Neurology, Hospital Universitario Fundación de Alcorcón, 28922 Alcorcón, Spain
*
Author to whom correspondence should be addressed.
Information 2020, 11(8), 393; https://doi.org/10.3390/info11080393
Received: 1 July 2020 / Revised: 30 July 2020 / Accepted: 6 August 2020 / Published: 10 August 2020
(This article belongs to the Special Issue Signal Processing and Machine Learning)
Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation occurs in the neonatal period and depends on the sympathetic tone in each eye. We hypothesized that the presence of visible or subtle color iris changes in both eyes could be used as a quantitative biomarker for screening and early detection of CH. This work scrutinizes the scope of an automatic diagnosis-support system for early detection of CH, by using as indicator the error rate provided by a statistical classifier designed to identify the eye (left vs. right) from iris pixels in color images. Systematic tests were performed on a database with images of 11 subjects (four with CH, four with other ophthalmic diseases affecting the iris pigmentation, and three control subjects). Several aspects were addressed to design the classifier, including: (a) the most convenient color space for the statistical classifier; (b) whether the use of features associated to several color spaces is convenient; (c) the robustness of the classifier to iris spatial subregions; (d) the contribution of the pixels neighborhood. Our results showed that a reduced value for the error rate (lower than 0.25) can be used as CH marker, whereas structural regions of the iris image need to be taken into account. The iris color feature analysis using statistical classification is a potentially useful technique to investigate disorders affecting the autonomous nervous system in CH. View Full-Text
Keywords: cluster headache; early diagnosis; quantitative analysis; iris color; color spaces; statistical classification cluster headache; early diagnosis; quantitative analysis; iris color; color spaces; statistical classification
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MDPI and ACS Style

El-Yaagoubi, M.; Mora-Jiménez, I.; Jabrane, Y.; Muñoz-Romero, S.; Rojo-Álvarez, J.L.; Pareja-Grande, J.A. Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification. Information 2020, 11, 393.

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