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  • 1.
    Benouar, Sara
    et al.
    Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria; Department of Textile Technology, University of Borås, Borås, Sweden.
    Hafid, Abdelakram
    Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria; Department of Textile Technology, University of Borås, Borås, Sweden.
    Kedir-Talha, Malika
    Laboratory of Instrumentation, University of Sciences and Technology Houari Boumediene, Algiers, Algeria.
    Seoane, Fernando
    Department for Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden; The Department of Medical Technology, Karolinska University Hospital, Stockholm , Sweden; The Swedish School of Textiles, University of Borås, Borås, Sweden.
    Classification of impedance cardiography dZ/dt complex subtypes using pattern recognition artificial neural networks2021In: Biomedizinische Technik (Berlin. Zeitschrift), ISSN 1862-278X, E-ISSN 0013-5585, Vol. 66, no 5, p. 515-527Article in journal (Refereed)
    Abstract [en]

    In impedance cardiography (ICG), the detection of dZ/dt signal (ICG) characteristic points, especially the X point, is a crucial step for the calculation of hemodynamic parameters such as stroke volume (SV) and cardiac output (CO). Unfortunately, for beat-to-beat calculations, the accuracy of the detection is affected by the variability of the ICG complex subtypes. Thus, in this work, automated classification of ICG complexes is proposed to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. A novel pattern recognition artificial neural network (PRANN) approach was implemented, and a divide-and-conquer strategy was used to identify the five different waveforms of the ICG complex waveform with output nodes no greater than 3. The PRANN was trained, tested and validated using a dataset from four volunteers from a measurement of eight electrodes. Once the training was satisfactory, the deployed network was validated on two other datasets that were completely different from the training dataset. As an additional performance validation of the PRANN, each dataset included four volunteers for a total of eight volunteers. The results show an average accuracy of 96% in classifying ICG complex subtypes with only a decrease in the accuracy to 83 and 80% on the validation datasets. This work indicates that the PRANN is a promising method for automated classification of ICG subtypes, facilitating the investigation of the extraction of hemodynamic parameters from beat-to-beat dZ/dt complexes.

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