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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

SDMS2 design and usage in the real-time application. (a) Sensor-dependent model selection system (SDMS2); (b) Implementation of the system for real-time applications.
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f7-sensors-15-15419: SDMS2 design and usage in the real-time application. (a) Sensor-dependent model selection system (SDMS2); (b) Implementation of the system for real-time applications.

Mentions: At this point, we can introduce the sensor-dependent model selection system (SDMS2). This system, shown in Figure 7a, is able to detect saturated or lossy sensors. The SDMS2 senses the status of the sensors and chooses the best set of models according to their availability in real time. For each patient, at the validation stage, this system implements a hierarchy of sets of models, depending on the availability of sensors, after creating the whole set of models for every combination of features according to Table 3. The hierarchies of sets of models for Patient A and Patient B are shown in Figure 8. The ordination is represented from top to bottom; vector h represents the information of minimum, average (highlighted) and maximum horizons from Table 3.


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)

SDMS2 design and usage in the real-time application. (a) Sensor-dependent model selection system (SDMS2); (b) Implementation of the system for real-time applications.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-15-15419: SDMS2 design and usage in the real-time application. (a) Sensor-dependent model selection system (SDMS2); (b) Implementation of the system for real-time applications.
Mentions: At this point, we can introduce the sensor-dependent model selection system (SDMS2). This system, shown in Figure 7a, is able to detect saturated or lossy sensors. The SDMS2 senses the status of the sensors and chooses the best set of models according to their availability in real time. For each patient, at the validation stage, this system implements a hierarchy of sets of models, depending on the availability of sensors, after creating the whole set of models for every combination of features according to Table 3. The hierarchies of sets of models for Patient A and Patient B are shown in Figure 8. The ordination is represented from top to bottom; vector h represents the information of minimum, average (highlighted) and maximum horizons from Table 3.

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