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Sensors (Basel). 2015 Jun 30;15(7):15419-42. doi: 10.3390/s150715419.

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

Author information

1
Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jpagan@ucm.es.
2
Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain. jpagan@ucm.es.
3
Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain. mirene@die.upm.es.
4
Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain. dra.anagago@gmail.com.
5
Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain. sobrado.m@med.ucm.es.
6
Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jlrisco@ucm.es.
7
Neurology Service, Sanitary Research Institute, University Hospital La Princesa, Madrid 28006, Spain. jvivancos@neurogps.com.es.
8
Electronic Engineering Department, Technical University of Madrid, Madrid 28040, Spain. josem@die.upm.es.
9
Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jayala@fdi.ucm.es.

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.

KEYWORDS:

N4SID; WBSN; migraine; modeling; prediction; robustness

PMID:
26134103
PMCID:
PMC4541837
DOI:
10.3390/s150715419
[Indexed for MEDLINE]
Free PMC Article

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