title: Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data.
abstract: Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.
authors.affiliation: Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jpagan@ucm.es.|Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain. jpagan@ucm.es.
authors.forename: Josué
authors.initials: J
authors.lastname: Pagán
authors.affiliation: Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain. mirene@die.upm.es.
authors.forename: M Irene
authors.initials: MI
authors.lastname: De Orbe
authors.affiliation: Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain. dra.anagago@gmail.com.
authors.forename: Ana
authors.initials: A
authors.lastname: Gago
authors.affiliation: Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain. sobrado.m@med.ucm.es.
authors.forename: Mónica
authors.initials: M
authors.lastname: Sobrado
authors.affiliation: Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jlrisco@ucm.es.
authors.forename: José L
authors.initials: JL
authors.lastname: Risco-Martín
authors.affiliation: Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain. jvivancos@neurogps.com.es.
authors.forename: J Vivancos
authors.initials: JV
authors.lastname: Mora
authors.affiliation: Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain. josem@die.upm.es.
authors.forename: José M
authors.initials: JM
authors.lastname: Moya
authors.affiliation: Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jayala@fdi.ucm.es.
authors.forename: José L
authors.initials: JL
authors.lastname: Ayala
mesh_terms: D000465:Algorithms; D015716:Electrocardiography, Ambulatory; D004867:Equipment Design; D005260:Female; D006439:Hemodynamics; D006801:Humans; D008881:Migraine Disorders / Q000175:diagnosis* / Q000503:physiopathology; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000379:methods*; D058998:Remote Sensing Technology / Q000379:methods*; D015203:Reproducibility of Results; D012881:Skin Temperature
keywords: N4SID; WBSN; migraine; modeling; prediction; robustness