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Article

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

1
Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain
2
Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain
3
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; https://doi.org/10.3390/s150715419
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)
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
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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. https://doi.org/10.3390/s150715419

AMA Style

Pagán J, De Orbe MI, Gago A, Sobrado M, Risco-Martín JL, Mora JV, Moya JM, Ayala JL. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data. Sensors. 2015; 15(7):15419-15442. https://doi.org/10.3390/s150715419

Chicago/Turabian Style

Pagán, Josué, M. I. De Orbe, Ana Gago, Mónica Sobrado, José L. Risco-Martín, J. V. Mora, José M. Moya, and José L. Ayala 2015. "Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data" Sensors 15, no. 7: 15419-15442. https://doi.org/10.3390/s150715419

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