2026-06-28T20:33:51Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/238872023-08-28T08:32:48Zcom_20.500.14352_14col_20.500.14352_15

00925njm 22002777a 4500 dc Pagán, Josué author De Orbe, M. author Gago, Ana author Sobrado, Mónica author Risco Martín, José Luis author Vivancos Mora, J. author Moya, José M. author Ayala Rodrigo, José Luis author 2015-06-30 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. 1424-8220 10.3390/s150715419 https://hdl.handle.net/20.500.14352/23887 https://doi.org/10.3390/s150715419 https://www.mdpi.com/1424-8220/15/7/15419 Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data