Early detection of silent hypoxia in COVID-19 pneumonia using deep learning and IoT - Bandopadhaya, Shuvabrata; Roy, Amarjit (Multimedia tools and applications, 2024)
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 (SpO 2 ) 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 SpO 2 readingsefficiently. The model is validated with the SpO 2 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.