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Effective IoT Based Analysis of Photoplethysmography Waveforms for Investigating Arterial Stiffness and Pulse Rate Variability

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Abstract

In order to detect variations in blood volume in the peripheral arterial pulse, photoplethysmography (PPG) can be used as an electro-optical method. Recognized as a straightforward, non-invasive, and reasonably priced method for diagnosing cardiovascular issues, PPG pulse characterization has attracted a lot of attention in recent years. IoT based analysis of PPG contours can shed light on cardiac characteristics at various points in the cardiac cycle. Loss of pulsatility associated with age and Cardio Vascular Disease (CVD) is the primary limitation of contour analysis The e waves are classified through optimistic discrete wavelet and CNN based approach. As a result, IoT based accurate delineation of the PPG pulse is necessary for accurate detection of heart illness. In addition, resampling the SDPPG signal ensures the occurrence of particular facts when it comes to the process of building slabs of attention, in which the undesired slabs are primarily removed by means of onset criteria. Research provides a comparison of the performance of the proposed method to that of other machine learning based techniques. In terms of classification accuracy (Normal, P1, and P2 pulses: 95.9%, 93.4%, and 90.08% respectively), wavelet-based CNN with PPG signal outperforms CNN with PPG and ABP signals. Correct classification is aided by CNN's ability to extract many features associated with premature pulses. To ensure that no pulses are missed, our method estimates the wavelet transform at one-second intervals over the whole signal's duration. CNN's incremental fine tuning also aids in improving both sensitivity and specificity. The wavelet-based CNN outperforms other state-of-the-art approaches in terms of accuracy, sensitivity, and specificity when classifying waves.

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Authors and Affiliations

  1. Department of EECE, GITAM Deemed to be University, Rushikonda, Visakhapatnam, AP, India

    Srinivasa Rao Sankranti

  2. Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, TN, India

    S. Mahaboob Basha

  3. Department of IT, St. Martin’s Engineering College, Secunderabad, Telangana, India

    B. Laxmi Kantha

  4. Department of ECE, Rajalakshmi Engineering College, Chennai, TN, India

    L. Bhagyalakshmi

  5. Department of CSE, Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, TN, India

    N. Gomathi

  6. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India

    Kuchipudi Prasanth Kumar

  7. Department of ECE, and Dean R&D, St. Martin’s Engineering College, Secunderabad, Telangana, India

    Sanjay Kumar Suman

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  1. Srinivasa Rao Sankranti
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Correspondence to L. Bhagyalakshmi.

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Sankranti, S.R., Basha, S.M., Kantha, B.L. et al. Effective IoT Based Analysis of Photoplethysmography Waveforms for Investigating Arterial Stiffness and Pulse Rate Variability. SN COMPUT. SCI. 5, 474 (2024). https://doi.org/10.1007/s42979-024-02777-6

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