SVM with bounds of confidence and PLS for quantifying the effects of acupuncture on migraine patients

Authors:
M. López;J. M. Górriz;J. Ramírez;D. Salas-Gonzalez;R. Chaves;M. Gómez-Río
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Nuclear Medicine, Hospital Universitario Virgen de las Nieves, Granada, Spain
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

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Abstract

In this work, SPECT brain images are analyzed automatically in order to determine whether acupuncture, applied under real conditions of clinical practice, is effective for fighting migraine. To this purpose two different groups of patients are randomly collected and received verum and sham acupuncture, respectively. Acupuncture effects on brain perfusion patterns can be measured quantitatively by dealing with the images in a classification context. Partial Least Squares are used as feature extraction technique, and Support Vector Machines with bounds of confidence are used to quantify the acupuncture effects on the brain activation pattern. Conclusions of this work prove that acupuncture produces new brain activation patterns when applied to migraine patients.