title: Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition.
abstract: The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.
authors.affiliation: 1 Computer Science Department, University of Oviedo, EIMEM, c/Independencia 13, Oviedo, Asturias 33004, Spain.
authors.forename: José R
authors.initials: JR
authors.lastname: Villar
authors.affiliation: 1 Computer Science Department, University of Oviedo, EIMEM, c/Independencia 13, Oviedo, Asturias 33004, Spain.
authors.forename: Paula
authors.initials: P
authors.lastname: Vergara
authors.affiliation: 2 Cellular Morphology and Biology Department, University of Oviedo, School of Medicine, Avda. Julián Clavería 6, Oviedo, Asturias 33005, Spain.|3 Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Chile.
authors.forename: Manuel
authors.initials: M
authors.lastname: Menéndez
authors.affiliation: 1 Computer Science Department, University of Oviedo, EIMEM, c/Independencia 13, Oviedo, Asturias 33004, Spain.
authors.forename: Enrique
authors.initials: E
authors.lastname: de la Cal
authors.affiliation: 4 Control and Automation Department, University of Oviedo, Campus de Viesques, Edificio Departamental Oeste, Módulo 2, Gijón, Asturias 33204, Spain.
authors.forename: Víctor M
authors.initials: VM
authors.lastname: González
authors.affiliation: 5 Instituto Tecnológico de Castilla y León, Polígono Industrial Villalonquéjar. c/López Bravo, 70, Burgos, Burgos 09001, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Sedano
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000203:Activities of Daily Living / Q000145:classification*; D000328:Adult; D020820:Dyskinesias / Q000145:classification* / Q000175:diagnosis / Q000503:physiopathology; D004827:Epilepsy / Q000145:classification* / Q000175:diagnosis / Q000503:physiopathology; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D012640:Seizures / Q000145:classification / Q000175:diagnosis / Q000503:physiopathology; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D055815:Young Adult
keywords: Epilepsy convulsions recognition; daily living; fuzzy rule-based classifiers