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      Patients with migraine are right about their perception of temperature as a trigger: time series analysis of headache diary data

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          Abstract

          Background

          Researches to date on the association between headache and weather have yielded inconsistent results. Only a limited number of studies have examined the clinical significance of self-reported weather sensitivity. This study aimed to identify the difference in the association of headache with temperature between migraine patients with and without temperature sensitivity.

          Methods

          66 migraine patients (75.8 % female; mean age 43.3 ± 12.9 years) provided their 1-year headache diaries from 2007 to a headache clinic in Taipei, Taiwan. 34 patients (51.5 %) reported sensitivity to temperature change but 32 (48.5 %) did not. Time series of daily headache incidence was modeled and stratified by temperature sensitivity. Empirical mode decomposition was used to identify temporal weather patterns that were correlated to headache incidence, and regression analysis was used to examine the amount of variance in headache incidence that could be explained by temperature in different seasons.

          Results

          Among all migraine patients, temperature change accounted for 16.5 % of variance in headache incidence in winter and 9.6 % in summer. In winter, the explained variance increased to 29.2 % among patients with temperature sensitivity, but was not significant among those without temperature sensitivity. Overall, temperature change explained 27.0 % of the variance of the mild headache incidence but only 4.8 % of the incidence of moderate to severe headache during winter.

          Conclusions

          This diary-based study provides evidence to link the perception of temperature sensitivity and headache incidence in migraine patients. Those who reported temperature sensitivity are more likely to have headache increase during the winter, particular for mild headaches.

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          Most cited references35

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          The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

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            The International Classification of Headache Disorders: 2nd edition.

            (2004)
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              • Article: not found

              On the trend, detrending, and variability of nonlinear and nonstationary time series.

              Determining trend and implementing detrending operations are important steps in data analysis. Yet there is no precise definition of "trend" nor any logical algorithm for extracting it. As a result, various ad hoc extrinsic methods have been used to determine trend and to facilitate a detrending operation. In this article, a simple and logical definition of trend is given for any nonlinear and nonstationary time series as an intrinsically determined monotonic function within a certain temporal span (most often that of the data span), or a function in which there can be at most one extremum within that temporal span. Being intrinsic, the method to derive the trend has to be adaptive. This definition of trend also presumes the existence of a natural time scale. All these requirements suggest the Empirical Mode Decomposition (EMD) method as the logical choice of algorithm for extracting various trends from a data set. Once the trend is determined, the corresponding detrending operation can be implemented. With this definition of trend, the variability of the data on various time scales also can be derived naturally. Climate data are used to illustrate the determination of the intrinsic trend and natural variability.
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                Author and article information

                Contributors
                +886-2-28762522 , sjwang@vghtpe.gov.tw
                Journal
                J Headache Pain
                J Headache Pain
                The Journal of Headache and Pain
                Springer Milan (Milan )
                1129-2369
                1129-2377
                26 May 2015
                26 May 2015
                2015
                : 16
                : 49
                Affiliations
                [ ]Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
                [ ]Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
                [ ]Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli, Taiwan
                [ ]Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA USA
                [ ]Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2, Shih-Pai Road, Taipei, 11217 Taiwan
                [ ]Division of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
                [ ]Preparatory office in Big Data Research Center & School of Management, Taipei Medical University, Taipei, Taiwan
                Article
                533
                10.1186/s10194-015-0533-5
                4446287
                26018293
                2e73f572-3dd5-44d7-96b9-20cc51a78011
                © Yang et al.; licensee Springer. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

                History
                : 10 April 2015
                : 19 May 2015
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Anesthesiology & Pain management
                empirical mode decomposition,migraine,time-dependent intrinsic correlation,weather,temperature

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