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



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Pagán, Josué and De Orbe, M. and Gago, Ana and Sobrado, Mónica and Risco Martín, José Luis and Vivancos Mora, J. and Moya, José M. and Ayala Rodrigo, José Luis (2015) Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data. Sensors, 15 (7). pp. 15419-15442. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s150715419


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.

Item Type:Article
Uncontrolled Keywords:migraine; WBSN; modeling; N4SID; prediction; robustness
Subjects:Sciences > Computer science
Medical sciences > Medicine > Medical telematics
ID Code:67808
Deposited On:15 Sep 2021 14:26
Last Modified:15 Sep 2021 14:31

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