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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

Gaussian process machine learning (GPML) and data synchronization applied during a migraine episode.
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f4-sensors-15-15419: Gaussian process machine learning (GPML) and data synchronization applied during a migraine episode.

Mentions: First of all, data from the four hemodynamic variables to work with are preprocessed in the preprocessing module (Figure 3). The result is shown in Figure 4. The green-colored signal sections are asymptomatic periods of time, while the red-colored sections represent the migraine itself (between black vertical bars). Some lost data have been recovered by the GPML subprocess. These data are represented with black points in the graphs. The grey zone represents the intervals of confidence of the GPML. From top to bottom, the signals have been recovered with the following fits: HR with 75.4%, TEMP 85.7%, EDA 93.2% and SpO2 73.4%. These fits are good enough; nevertheless, a finer tuning of the modeling parameters in the GPML could improve the results. This work will be tackled in the future.


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)

Gaussian process machine learning (GPML) and data synchronization applied during a migraine episode.
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-15-15419: Gaussian process machine learning (GPML) and data synchronization applied during a migraine episode.
Mentions: First of all, data from the four hemodynamic variables to work with are preprocessed in the preprocessing module (Figure 3). The result is shown in Figure 4. The green-colored signal sections are asymptomatic periods of time, while the red-colored sections represent the migraine itself (between black vertical bars). Some lost data have been recovered by the GPML subprocess. These data are represented with black points in the graphs. The grey zone represents the intervals of confidence of the GPML. From top to bottom, the signals have been recovered with the following fits: HR with 75.4%, TEMP 85.7%, EDA 93.2% and SpO2 73.4%. These fits are good enough; nevertheless, a finer tuning of the modeling parameters in the GPML could improve the results. This work will be tackled in the future.

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