ArticleIEEE journal of translational engineering in health and medicine2022
Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.
Article in IEEE journal of translational engineering in health and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Towards generalizable contactless oximetry from multispectral video via deep domain-adaptive learning strategy.Biomedical optics express · 2026Article
- AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction.JMIR medical informatics · 2025Article
- Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.Sensors (Basel, Switzerland) · 2024Review
- COVID-19 Detection Using Photoplethysmography and Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection.IEEE journal of translational engineering in health and medicine · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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Abstract
Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.
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