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

Pagán J, De Orbe MI, Gago A, Sobrado M, Risco-Martín JL, Mora JV, Moya JM, Ayala JL - Sensors (Basel) (2015)

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jpagan@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.

No MeSH data available.


Related in: MedlinePlus

Modeling of subjective pain evolution curve.
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f2-sensors-15-15419: Modeling of subjective pain evolution curve.

Mentions: To predict the symptomatic crisis, the first step is to generate a model of the migraine pain. To do this, an adjustment process of the registered subjective pain curve was carried out. The symptomatic curve has been modeled as two semi-Gaussian curves, as they fit the patient's subjective response. In addition to the punctual point of the pain evolution, patients also indicate three timestamps during the migraine attack. The first timestamp indicates the beginning of aura; the second one indicates the pain when detected; and the third one indicates the end of pain. With these three points and the punctual points of the pain evolution, two semi-Gaussian curves can be generated. {(μ1, σ1), (μ2, σ2)} are the two semi-Gaussian's parameters necessary to define a symptomatic curve. The symptomatic curve includes the aura and the pain period, as the latter reflects some changes in the migraine process. An example of the resulting function of is shown in Figure 2.


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

Pagán J, De Orbe MI, Gago A, Sobrado M, Risco-Martín JL, Mora JV, Moya JM, Ayala JL - Sensors (Basel) (2015)

Modeling of subjective pain evolution curve.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541837&req=5

f2-sensors-15-15419: Modeling of subjective pain evolution curve.
Mentions: To predict the symptomatic crisis, the first step is to generate a model of the migraine pain. To do this, an adjustment process of the registered subjective pain curve was carried out. The symptomatic curve has been modeled as two semi-Gaussian curves, as they fit the patient's subjective response. In addition to the punctual point of the pain evolution, patients also indicate three timestamps during the migraine attack. The first timestamp indicates the beginning of aura; the second one indicates the pain when detected; and the third one indicates the end of pain. With these three points and the punctual points of the pain evolution, two semi-Gaussian curves can be generated. {(μ1, σ1), (μ2, σ2)} are the two semi-Gaussian's parameters necessary to define a symptomatic curve. The symptomatic curve includes the aura and the pain period, as the latter reflects some changes in the migraine process. An example of the resulting function of is shown in Figure 2.

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: Computer Architecture and Automation Department, Complutense University of Madrid, Madrid 28040, Spain. jpagan@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.

No MeSH data available.


Related in: MedlinePlus