Abstract
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%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Benouar, S., Hafid, A., Attari, M., Kedir-Talha, M., Seoane, F.: Systematic variability in ICG recordings results in ICG complex subtypes–steps towards the enhancement of ICG characterization. J. Electrical Bioimpedance 9(1), 72–82 (2018)
Meijer, J.H., Boesveldt, S., Elbertse, E., Berendse, H.: Method to measure autonomic control of cardiac function using time interval parameters from impedance cardiography. Phys. Measure. 29(6), S383 (2008)
Cybulski, G.: Ambulatory impedance cardiography. In: Ambulatory Impedance Cardiography. pp. 39–56. Springer (2011)
Kim, T.-H.: Pattern recognition using artificial neural network: a review. In: International Conference on Information Security and Assurance. pp. 138–148: Springer (2010)
Hosseini, H.G., Luo, D., Reynolds, K.J.: The comparison of different feed forward neural network architectures for ECG signal diagnosis. Med. Eng. Phys. 28(4), 372–378 (2006)
He, L., Hou, W., Zhen, X., Peng, C.: Recognition of ECG patterns using artificial neural network. In: Sixth International Conference on Intelligent Systems Design And Applications. vol. 2, pp. 477–481: IEEE (2006)
Hafid, A., Benouar, S., Kedir-Talha, M., Abtahi, F., Attari, M., Seoane, F.: Full impedance cardiography measurement device using raspberry pi3 and system-on-chip biomedical instrumentation solutions. IEEE J. Biomed. Health Inf. 22(6), 1883–1894 (2018)
Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Aarhus University, Computer Science Department (1990)
Demuth, H.B., Beale, M.H., De Jess, O., Hagan, M.T.: Neural network design. Martin Hagan, Boston (2014)
Yao, Y., Rosasco, L., Caponnetto, A.: On early stopping in gradient descent learning. Const. Approx. 26(2), 289–315 (2007)
Stathakis, D.: How many hidden layers and nodes? Int. J. Remote Sens. 30(8), 2133–2147 (2009)
Acknowledgment
The authors would like to thank the Ministére de l’Enseignement Supérieur et de la Recherche Scientifique, Algerian Government and the research group Textile and Wearable Sensing for p-Health Solutions, University of Borås.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflicts of interest.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Benouar, S., Hafid, A., Kedir-Talha, M., Seoane, F. (2021). First Steps Toward Automated Classification of Impedance Cardiography dZ/dt Complex Subtypes. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_64
Download citation
DOI: https://doi.org/10.1007/978-3-030-64610-3_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64609-7
Online ISBN: 978-3-030-64610-3
eBook Packages: EngineeringEngineering (R0)