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Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

This paper presents a non-invasive method for Blood Pressure (BP) estimation based on extracted features from photoplethysmogram (PPG) and Electrocardiogram (ECG) signals. The proposed method depends on a machine learning technique, namely Artificial Neural Networks (ANN), to estimate blood pressure. The training is conducted on a real data set (more than 2000 BP, ECG and PPG signals) recorded by patients’ monitoring at various hospitals between 2001 and 2008. In addition to the ten features that are usually used in literature, the proposed method uses the cross validation technique between features to provide more robust estimation of the blood pressure. Furthermore, the proposed method provides accurate and reliable blood pressure estimation while it is calibration-free. Compared to previous works, we used half of the data and the results clarified that we achieved more accuracy in the systolic pressure measurements. These results are expected to improve more by increasing the training samples, which is planned in future work.

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Author information

Authors and Affiliations

  1. ASU Faculty of Engineering, Cairo, Egypt

    Nashat Maher & Wagdy R. Anis

  2. PHI Institute, Giza, Egypt

    G. A. Elsheikh

  3. ASU Faculty of Medicine, Cairo, Egypt

    Tamer Emara

Authors
  1. Nashat Maher
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  2. G. A. Elsheikh
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  3. Wagdy R. Anis
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  4. Tamer Emara
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Corresponding authors

Correspondence to Nashat Maher , G. A. Elsheikh , Wagdy R. Anis or Tamer Emara .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Maher, N., Elsheikh, G.A., Anis, W.R., Emara, T. (2020). Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_69

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