Analyzing Accelerometer Data for Epilepsy Episode Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

José R. Villar , Manuel Menéndez , Javier Sedano , Enrique de la Cal , Víctor M. González

ABSTRACT

Epilepsy is one of the main neurological disorders with high impact in the patient’s everyday life. An incorrect treatment or a lack in monitoring might produce cognitive damage and depression. Therefore, developing a wearable device for epilepsy monitoring would eventually complete the anamnesis, enhancing the medical staff diagnosing and treatment setting. This study shows the preliminary results in epilepsy onset recognition based on wearable tri-axial accelerometers and simple fuzzy set learnt using genetic algorithms. A complete experimentation for learning the fuzzy set is detailed. According to the obtained results, some generalized feasible solutions are discussed. Results show a very interesting researching area that might be easily transferred to embedded devices and online health care systems. More... »

PAGES

39-48

References to SciGraph publications

Book

TITLE

10th International Conference on Soft Computing Models in Industrial and Environmental Applications

ISBN

978-3-319-19718-0
978-3-319-19719-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-19719-7_4

DOI

http://dx.doi.org/10.1007/978-3-319-19719-7_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041245722


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