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Double Channel Neural Non Invasive Blood Pressure Prediction

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Book cover Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12463))

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Abstract

Cardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG and PPG for estimating both systolic and diastolic blood pressures. The double channel configuration is the best performing one by means of the mean absolute error w.r.t the corresponding invasive blood pressure signal (IBP); indeed, it is also proven to be compliant with the ANSI/AAMI/ISO 81060-2:2013 regulation for non invasive ABP techniques. Both ECG and PPG correlations to IBP signal are further analyzed using Spearman’s correlation coefficient. Despite it suggests PPG is more closely related to ABP, its regression performance is worse than ECG input configuration one. However, this behavior can be explained looking to human biology and ABP computation, which is based on peaks (systoles) and valleys (diastoles) extraction.

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Authors and Affiliations

  1. DET - Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

    Annunziata Paviglianiti, Vincenzo Randazzo & Eros Pasero

  2. Lab. LTI, Université de Picardie Jules Verne, Amiens, France

    Giansalvo Cirrincione

  3. University of South Pacific, Suva, Fiji

    Giansalvo Cirrincione

Authors
  1. Annunziata Paviglianiti
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  2. Vincenzo Randazzo
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  3. Giansalvo Cirrincione
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  4. Eros Pasero
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Corresponding author

Correspondence to Annunziata Paviglianiti .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Paviglianiti, A., Randazzo, V., Cirrincione, G., Pasero, E. (2020). Double Channel Neural Non Invasive Blood Pressure Prediction. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-60799-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60798-2

  • Online ISBN: 978-3-030-60799-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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