Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches

Mohd Nasir, Haslinah and Nor Razman, Nur Fatin Shazwani and Zainuddin, Suraya and Brahin, Noor Mohd Ariff and Ibrahim, Idnin Pasya and Mispan, Mohd Syafiq (2024) Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches. International Journal Of Electrical And Computer Engineering (IJECE), 14 (6). pp. 6929-6943. ISSN 2088-8708

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Classification of respiration, Deep learning, Machine learning, Radar, Respiration
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 Aug 2025 04:50
Last Modified:	11 Aug 2025 04:50
URI:	http://eprints.utem.edu.my/id/eprint/28890
Statistic Details:	View Download Statistic

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