Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches
Respiration-related disease refers to a wide range of conditions, including influenza, pneumonia, asthma, sudden infant death syndrome (SIDS) and the latest outbreak, coronavirus disease 2019 (COVID-19), and many other respiration issues. However, real-time monitoring for the detection of respirato...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Institute Of Advanced Engineering And Science (IAES)
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28890/3/32240-75594-1-PB.pdf http://eprints.utem.edu.my/id/eprint/28890/ https://ijece.iaescore.com/index.php/IJECE/article/view/32240 http://doi.org/10.11591/ijece.v14i6.pp6929-6943 |
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| Summary: | Respiration-related disease refers to a wide range of conditions, including influenza, pneumonia, asthma, sudden infant death syndrome (SIDS) and the latest outbreak, coronavirus disease 2019 (COVID-19), and many other
respiration issues. However, real-time monitoring for the detection of respiratory disorders is currently lacking and needs to be improved. Realtime respiratory measures are necessary since unsupervised treatment of respiratory problems is the main contributor to the rising death rate. Thus, this paper reviewed the classification of the respiratory signal using two different approaches for real-time monitoring applications. This research explores machine learning and deep learning approaches to forecasting
respiration conditions. Every consumption of these approaches has been discussed and reviewed. In addition, the current study is reviewed to identify critical directions for developing respiration real-time applications. |
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