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Deep facial analysis: A new phase I epilepsy evaluation using computer vision by David Ahmedt-Aristizabal; Clinton Fookes; Kien Nguyen; Simon Denman; Sridha Sridharan; Sasha Dionisio is a Medicine article available to read on EtoBox.

What is Deep facial analysis: A new phase I epilepsy evaluation using computer vision about?

Semiology observation and characterization play a major role in the presurgical evaluation of epilepsy. However, the interpretation of patient movements has subjective and intrinsic challenges. In this paper, we develop approaches to attempt to automatically extract and classify semiological patterns from facial expressions. We address limitations of existing computer-based analytical approaches of epilepsy monitoring, where facial movements have largely been ignored. This is an area that has seen limited advances in the literature. Inspired by recent advances in deep learning, we propose two deep learning models, landmark-based and region-based, to quantitatively identify changes in facial semiology in patients with mesial temporal lobe epilepsy (MTLE) from spontaneous expressions during phase I monitoring. A dataset has been collected from the Mater Advanced Epilepsy Unit (Brisbane, Australia) and is used to evaluate our proposed approach. Our experiments show that a landmark-based approach achieves promising results in analyzing facial semiology, where movements can be effectively marked and tracked when there is a frontal face on visualization. However, the region-based counter

Who reads Deep facial analysis: A new phase I epilepsy evaluation using computer vision?

Author

David Ahmedt-Aristizabal; Clinton Fookes; Kien Nguyen; Simon Denman; Sridha Sridharan; Sasha Dionisio

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 1525-5050)

Published

2018

Language

EN

Field

Medicine (Health Sciences)