Robust and accurate modeling approaches for migraine Per-Patient prediction from ambulatory data

Pagán Ortiz, Josué and Orbe Izquierdo, Irene de and Gago, Ana and Sobrado, Mónica and Risco Martín, José Luis and Vivancos Mora, J. and Moya Fernández, José Manuel and Ayala Rodrigo, José Luis (2015). Robust and accurate modeling approaches for migraine Per-Patient prediction from ambulatory data. "Sensors", v. 15 ; pp. 15419-15442. ISSN 1424-8220. https://doi.org/10.3390/s150715419.

Description

Title: Robust and accurate modeling approaches for migraine Per-Patient prediction from ambulatory data
Author/s:
  • Pagán Ortiz, Josué
  • Orbe Izquierdo, Irene de
  • Gago, Ana
  • Sobrado, Mónica
  • Risco Martín, José Luis
  • Vivancos Mora, J.
  • Moya Fernández, José Manuel
  • Ayala Rodrigo, José Luis
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 2015
ISSN: 1424-8220
Volume: 15
Subjects:
Freetext Keywords: Migraine; WBSN; modeling; N4SID; prediction; robustness
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2012-33892UnspecifiedMinisterio de Economía y CompetitividadUnspecified

More information

Item ID: 41443
DC Identifier: http://oa.upm.es/41443/
OAI Identifier: oai:oa.upm.es:41443
DOI: 10.3390/s150715419
Official URL: http://www.mdpi.com/1424-8220/15/7/15419
Deposited by: Memoria Investigacion
Deposited on: 20 Jul 2016 19:14
Last Modified: 20 Jul 2016 19:14
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM