scholarly journals Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 393
Author(s):  
Mohammed El-Yaagoubi ◽  
Inmaculada Mora-Jiménez ◽  
Younes Jabrane ◽  
Sergio Muñoz-Romero ◽  
José Luis Rojo-Álvarez ◽  
...  
Keyword(s):  
Early Detection ◽  
Cluster Headache ◽  
Error Rate ◽  
Color Space ◽  
Autonomous Nervous System ◽  
Statistical Classification ◽  
Group Iii ◽  
Statistical Classifier ◽  
Iris Pigmentation

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.

Die Wirksamkeit eines Frühinterventionsprogramms für Jugendliche mit Computerspiel- und Internetabhängigkeit: Mittelfristige Effekte der PROTECT+ Studie

2020 ◽  
Vol 48 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Carolin Szász-Janocha ◽  
Eva Vonderlin ◽  
Katajun Lindenberg
Keyword(s):  
Internet Use ◽  
Health Problems ◽  
Reliable Change Index ◽  
Gaming Disorder ◽  
Statistical Classification ◽  
Reliable Change ◽  
Classification Of Diseases ◽  
International Statistical Classification

Zusammenfassung. Fragestellung: Das junge Störungsbild der Computerspiel- und Internetabhängigkeit hat in den vergangenen Jahren in der Forschung zunehmend an Aufmerksamkeit gewonnen. Durch die Aufnahme der „Gaming Disorder“ in die ICD-11 (International Statistical Classification of Diseases and Related Health Problems) wurde die Notwendigkeit von evidenzbasierten und wirksamen Interventionen avanciert. PROTECT+ ist ein kognitiv-verhaltenstherapeutisches Gruppentherapieprogramm für Jugendliche mit Symptomen der Computerspiel- und Internetabhängigkeit. Die vorliegende Studie zielt auf die Evaluation der mittelfristigen Effekte nach 4 Monaten ab. Methodik: N = 54 Patientinnen und Patienten im Alter von 9 bis 19 Jahren (M = 13.48; SD = 1.72) nahmen an der Frühinterventionsstudie zwischen April 2016 und Dezember 2017 in Heidelberg teil. Die Symptomschwere wurde zu Beginn, zum Abschluss der Gruppentherapie sowie nach 4 Monaten anhand von standardisierten Diagnostikinstrumenten erfasst. Ergebnisse: Mehrebenenanalysen zeigten eine signifikante Reduktion der Symptomschwere anhand der Computerspielabhängigkeitsskala (CSAS) nach 4 Monaten. Im Selbstbeurteilungsbogen zeigte sich ein kleiner Effekt (d = 0.35), im Elternurteil ein mittlerer Effekt (d = 0.77). Der Reliable Change Index, der anhand der Compulsive Internet Use Scale (CIUS) berechnet wurde, deutete auf eine starke Heterogenität im individuellen Symptomverlauf hin. Die Patientinnen und Patienten bewerteten das Programm zu beiden Follow-Up-Messzeitpunkten mit einer hohen Zufriedenheit. Schlussfolgerungen: Die vorliegende Arbeit stellt international eine der wenigen Studien dar, die eine Reduktion der Symptome von Computerspiel- und Internetabhängigkeit im Jugendalter über 4 Monate belegen konnte.

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

scholarly journals A study of statistical classification of aphasia: multivariate analysis of SLTA.

1984 ◽  
Vol 4 (2) ◽  
pp. 620-628 ◽  
Author(s):  
Hiroichi Tasaki ◽  
Shunzo Watanabe ◽  
Kei Hojo ◽  
Kazue Chishima ◽  
Hirobumi Metoki
Keyword(s):  
Multivariate Analysis ◽  
Statistical Classification

Robust model selection and the statistical classification of languages

2012 ◽  
Author(s):  
J. E. García ◽  
V. A. González-López ◽  
M. L. L. Viola ◽  
Julio Michael Stern ◽  
Marcelo De Souza Lauretto ◽  
...  
Keyword(s):  
Model Selection ◽  
Statistical Classification ◽  
Robust Model ◽  
Robust Model Selection
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