Title
Detection Of Circaseptan Rhythm And The "Monday Effect" From Long-Term Pulse Rate Dynamics
Abstract
This study proposes a methodology to detect circaseptan (CS) rhythm in pulse rate (PR) data and to investigate the "Monday effect" in CS rhythm. Daily PR was collected from a middle-aged healthy working woman over one year. PR, SDNN index and sample entropy (SampEn) were chosen as the indexes of PR dynamics. In order to avoid interference from other biorhythms, ensemble empirical mode decomposition (EEMD) method was used to decompose the original PR series into multiple components. And the single cosinor method was applied to fit the detrended component signal. An optimal 7-day period was found in all indexes (P = 0.0103, P = 0.0133, P = 0.0122 for PR, SDNN index and SampEn, separately) that demonstrated an underlying CS rhythm. In the following study, a statistical Monday decrease in PR dynamics was observed especially significant in the detrended signal. The results suggested a direct relationship between the "Monday effect" and the CS variation, and also indicated a cardiac susceptibility to the social activities. The findings in CS periodicity and the "Monday effect" may help understand the human's biorhythm, provide evidence for preventive and optimized timing treatment, and also serve to daily health management.
Year
DOI
Venue
2011
10.1109/IEMBS.2011.6090646
2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
time series analysis,empirical mode decomposition,blood pressure measurement,stress,indexation,cardiology,health management,heart rate variability,entropy,rhythm,sleep,indexes
Time series,Biorhythm,Circadian rhythm,Sample entropy,Pulse (signal processing),Artificial intelligence,Medicine,Circaseptan,Computer vision,Heart rate variability,Speech recognition,Statistics,Rhythm
Conference
Volume
ISSN
Citations 
2011
1557-170X
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Ying Chen111516.65
Wenxi Chen22211.15