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Early detection of silent hypoxia in COVID-19 pneumonia using deep learning and IoT

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Abstract

Unlike normal pneumonia, in COVID-19 pneumonia, the shortfall of oxygen occurs without any noticeable breathing difficulties leads to multiple organ failure and death. The early detection of silent hypoxia in COVID-19 pneumonia is the key to save many lives from this deadly disease. This paper has proposed an e-health solution for early detection of the hypoxia condition of COVID-19 patients using internet of things (IoT) and deep learning techniques. The proposed solution has implemented an IoT framework to collect the percentage of oxygen saturation level in the blood (SpO2) of the patient on real-time basis. It has proposed a time-series forecasting model with deep learning, that being trained with the collected data, forecasts two upcoming SpO2readingsefficiently. The model is validated with the SpO2 level data of 261 hospitalized COVID-19 infected patients with varying level of criticality updated in each 2 hours, and the absolute percentage of errors (APE) in the prediction process has been observed around ~1.56%. The proposed methodology has great potential to control fatality rate in COVID-19 as the early detection of hypoxia helps to initiate the necessary course of action at appropriate time.

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Data Availability

The dataset is available on Github via given link. https://github.com/Shuvabrata1/Time-series-forecasting-for-SpO2-reading.

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Authors and Affiliations

  1. Banasthali Vidyapith, Radha Kishnpura, Rajasthan, India

    Shuvabrata Bandopadhaya

  2. Ghani Khan Choudhury Institute of Engineering and Technology, Malda, India

    Amarjit Roy

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  1. Shuvabrata Bandopadhaya
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  2. Amarjit Roy
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Correspondence to Amarjit Roy.

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Bandopadhaya, S., Roy, A. Early detection of silent hypoxia in COVID-19 pneumonia using deep learning and IoT. Multimed Tools Appl 83, 24527–24539 (2024). https://doi.org/10.1007/s11042-023-16473-9

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