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Categorization of fetal heart rate patterns using neural networks
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
2001 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 25, no 4, 269-276 p.Article in journal (Refereed) Published
Abstract [en]

Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.

Place, publisher, year, edition, pages
2001. Vol. 25, no 4, 269-276 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-29553DOI: 10.1023/A:1010779205000Local ID: 14924OAI: oai:DiVA.org:liu-29553DiVA: diva2:250369
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-09-14Bibliographically approved
In thesis
1. Medical data time series analyses using AI techniques
Open this publication in new window or tab >>Medical data time series analyses using AI techniques
2002 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In many areas of medicine, we collect serial measurements of different variables as they change over time. Changes in a time series is often reflective of a change in a patient's status and may be helpful in guiding the appropriate therapy. As we tend to collect data more data as well as more frequently and for longer periods of time, the amount of information available to the clinician grows. Even though there is and has been a siguificant development in medical technology, the management of these growing anaounts of data still requires manual interpretation in many cases.

Typically, the information collected from serial measurements of data is used to establish a trend, which may be related to a patient's progress or deterioration. Isolated measurements of a variable also contiibute to a better understanding of a patient's status. The physician ahnost always manually interprets this type of infonnation. Time series may contain other types of information that may not be as easily be interpreted this way.

This thesis describes a number of cases where other types of information have been extracted from medical data time series. Time series may contain information that can be used for prediction as well as categorization (diagnosis). Except from the potential of improving patient care, this information is also helpful in expanding the understanding of the processes involved in each example.

The methods used in this thesis include principal component analysis, neural networks, cross-correlation analysis and the wavelet transform. By applying these methods to analyse the information in the various time series, adaptable and effective tools for an increased understanding of these problems may be created. This may contribute to better patient management by providing the clinician with improved decision support.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2002. 49 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 726
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-29437 (URN)14783 (Local ID)91-7373-164-1 (ISBN)14783 (Archive number)14783 (OAI)
Public defence
2002-04-15, Högdahlsalen, Universitetssjukhuset, Linköping, 13:00 (Swedish)
Opponent
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-09-14Bibliographically approved

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