Algorithms and Tools for the Automatic Analysis of Embedded Systems.

Algorithms and Tools for the Automatic Analysis of Embedded Systems

10.21236/ada350960 ◽

1998 ◽

Author(s):

Thomas A. Henzinger

Keyword(s):

Embedded Systems ◽

Automatic Analysis

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Automatic analysis of embedded systems specified in Astral

Proceedings of the Thirty-First Hawaii International Conference on System Sciences ◽

10.1109/hicss.1998.656139 ◽

2002 ◽

Cited By ~ 2

Author(s):

K. Brink ◽

L.J.G. Bun ◽

J. van Katwijk ◽

R.F. Lutje Spelberg ◽

W.J. Toetenel

Keyword(s):

Embedded Systems ◽

Automatic Analysis

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Automatic analysis of segmented-EEG during long-term monitoring

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88507-4 ◽

1997 ◽

Vol 103 (1) ◽

pp. 116

Author(s):

R Agarwal

Keyword(s):

Automatic Analysis ◽

Long Term Monitoring ◽

Term Monitoring

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Neural Network for Automatic Analysis of Motility Data

Methods of Information in Medicine ◽

10.1055/s-0038-1634978 ◽

1994 ◽

Vol 33 (01) ◽

pp. 157-160 ◽

Cited By ~ 2

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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