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First Steps Toward ...
First Steps Toward Automated Classification of Impedance Cardiography dZ/dt Complex Subtypes
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- Benouar, Sara (author)
- Högskolan i Borås,Akademin för textil, teknik och ekonomi,Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria; Department of Textile Technology, University of Borås, Borås, Sweden,Department of Textile Technology
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- Hafid, Abdelakram (author)
- Högskolan i Borås,Akademin för textil, teknik och ekonomi,University of Sciences and Technology Houari Boumediene,Department of Textile Technology
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- Kedir-Talha, M. (author)
- University of Sciences and Technology Houari Boumediene
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- Seoane, Fernando, 1976- (author)
- Högskolan i Borås,Akademin för textil, teknik och ekonomi,Karolinska University Hospital,Department of Textile Technology
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(creator_code:org_t)
- 2020-11-30
- 2021
- English.
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In: 8th European Medical and Biological Engineering Conference. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030646097 - 9783030646103 ; , s. 563-573
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The detection of the characteristic points of the complex of the impedance cardiography (ICG) is a crucial step for the calculation of hemodynamical parameters such as left ventricular ejection time, stroke volume and cardiac output. Extracting the characteristic points from the dZ/dt ICG signal is usually affected by the variability of the ICG complex and assembling average is the method of choice to smooth out such variability. To avoid the use of assembling average that might filter out information relevant for the hemodynamic assessment requires extracting the characteristics points from the different subtypes of the ICG complex. Thus, as a first step to automatize the extraction parameters, the aim of this work is to detect automatically the kind of dZ/dt complex present in the ICG signal. To do so artificial neural networks have been designed with two different configurations for pattern matching (PRANN) and tested to identify the 6 different ICG complex subtypes. One of the configurations implements a 6-classes classifier and the other implemented the divide and conquer approach classifying in two stages. The data sets used in the training, validation and testing process of the PRANNs includes a matrix of 1 s windows of the ICG complexes from the 60 s long recordings of dZ/dt signal for each of the 4 healthy male volunteers. A total of 240 s. As a result, the divide and conquer approach improve the overall classification obtained with the one stage approach on +26% reaching and average classification ration of 82%.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- ABEXYOZ complex
- Artificial neural networks
- Bioimpedance
- Classification
- dZ/dt signal
- Feed-forward backpropagation
- Impedance cardiography
- Pattern recognition
- Biochemical engineering
- Complex networks
- Electrocardiography
- Neural networks
- Pattern matching
- Automated classification
- Characteristic point
- Characteristics points
- Divide-and-conquer approach
- Extraction parameters
- Left ventricular
- Testing process
- Biomedical signal processing
Publication and Content Type
- ref (subject category)
- kon (subject category)
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