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Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain
Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain
Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Sensors 2015, 15(7), 15419-15442;
Received: 3 June 2015 / Revised: 23 June 2015 / Accepted: 26 June 2015 / Published: 30 June 2015
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
PDF [2180 KB, uploaded 1 July 2015]


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. View Full-Text
Keywords: migraine; WBSN; modeling; N4SID; prediction; robustness migraine; WBSN; modeling; N4SID; prediction; robustness
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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MDPI and ACS Style

Pagán, J.; De Orbe, M.I.; Gago, A.; Sobrado, M.; Risco-Martín, J.L.; Mora, J.V.; Moya, J.M.; Ayala, J.L. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data. Sensors 2015, 15, 15419-15442.

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