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Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden; Laboratory of Instrumentation, Department of Instrumentation and Automatics, Institute of Electrical Engineering, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria.
Laboratory of Instrumentation, Department of Instrumentation and Automatics, Institute of Electrical Engineering, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria.
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden; Department of Medical Technology, Karolinska University Hospital, Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden 5Department of Textile Technology, University of Borås, Borås, Sweden.
2023 (Engelska)Ingår i: Frontiers in Physiology, E-ISSN 1664-042X, Vol. 14, artikel-id 1181745Artikel i tidskrift (Övrigt vetenskapligt) Published
Abstract [en]

One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is required the left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time-series non-linear autoregressive model with exogenous inputs (NARX) feedback neural network approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%–30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets.

Ort, förlag, år, upplaga, sidor
Frontiers Media S.A., 2023. Vol. 14, artikel-id 1181745
Nyckelord [en]
artificial neural networks, NARX, impedance cardiography, machine learning, time-series predictive model, characteristic point detection
Nationell ämneskategori
Datavetenskap (datalogi) Annan teknik
Identifikatorer
URN: urn:nbn:se:umu:diva-213012DOI: 10.3389/fphys.2023.1181745ISI: 001015238500001PubMedID: 37346485Scopus ID: 2-s2.0-85162206317OAI: oai:DiVA.org:umu-213012DiVA, id: diva2:1789335
Tillgänglig från: 2023-08-18 Skapad: 2023-08-18 Senast uppdaterad: 2024-01-17Bibliografiskt granskad

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