Circadian Rhythms in Muscular Activity

2015 ◽  
pp. 218-238 ◽  
Author(s):  
Thomas Reilly
Keyword(s):  
Circadian Rhythms ◽  
Muscular Activity

The Biological Clock and Sleep in the Elderly

2006 ◽  
Vol 19 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Myriam Juda ◽  
Mirjam Münch ◽  
Anna Wirz-Justice ◽  
Martha Merrow ◽  
Till Roenneberg
Keyword(s):  
Circadian Rhythms ◽  
Biological Clock ◽  
The Elderly ◽  
Early Morning ◽  
Behavioral Changes ◽  
Older Age ◽  
Age Related ◽  
Theoretical Considerations ◽  
Sleep Difficulties ◽  
Circadian Biology

Abstract: Among many other changes, older age is characterized by advanced sleep-wake cycles, changes in the amplitude of various circadian rhythms, as well as reduced entrainment to zeitgebers. These features reveal themselves through early morning awakenings, sleep difficulties at night, and a re-emergence of daytime napping. This review summarizes the observations concerning the biological clock and sleep in the elderly and discusses the documented and theoretical considerations behind these age-related behavioral changes, especially with respect to circadian biology.

Circadian Rhythms: A Tutorial

1982 ◽  
Vol 27 (12) ◽  
pp. 931-932
Author(s):  
Gerald S. Wasserman
Keyword(s):  
Circadian Rhythms

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

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