Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition

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题名:	Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition
作者:	Yang,AC;Fuh,JL;Huang,NE;Shia,BC;Peng,CK;Wang,SJ
贡献者:	數據分析方法研究中心
关键词:	NONSTATIONARY TIME-SERIES;POPULATION-BASED SURVEY;BAROMETRIC-PRESSURE;MIGRAINE HEADACHE;CHINOOK WINDS
日期:	2011
上传时间:	2012-03-27 17:50:43 (UTC+8)
出版者:	國立中央大學
摘要:	Background: Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area. Methodology/Principal Findings: The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts. Conclusions/Significance: Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons.
關聯:	PLOS ONE
显示于类别:	[數據分析方法研究中心 ] 期刊論文

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