A review of automated sleep disorder detection
Article
Xu, Shuting, Faust, Oliver, Seoni, Silvia, Chakraborty, Subrata, Barua, Prabal Datta, Loh, Hui Wen, Elphick, Heather, Molinari, Filippo and Acharya, U. Rajendra. 2022. "A review of automated sleep disorder detection." Computers in Biology and Medicine. 150. https://doi.org/10.1016/j.compbiomed.2022.106100
| Article Title | A review of automated sleep disorder detection |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Xu, Shuting, Faust, Oliver, Seoni, Silvia, Chakraborty, Subrata, Barua, Prabal Datta, Loh, Hui Wen, Elphick, Heather, Molinari, Filippo and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 150 |
| Article Number | 106100 |
| Number of Pages | 20 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.106100 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482522008083 |
| Abstract | Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand. |
| Keywords | Automated detection; Biomedical signals processing; Deep learning; Insomnia: artificial intelligence; Machine learning; Sleep apnea; Sleep disorder detection; Sleep disorders |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Singapore University of Social Sciences (SUSS), Singapore |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| School of Business | |
| Cogninet Australia, Australia | |
| Anglia Ruskin University, United Kingdom | |
| Polytechnic University of Turin, Italy | |
| University of New England | |
| University of Technology Sydney | |
| Sheffield Children’s NHS Foundation Trust, United Kingdom |
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Tasci, Burak, Acharya, Madhav R., Barua, Prabal Datta, Yildiz, Arif Metehan, Gun, Mehmet Veysel, Keles, Tugce, Dogan, Sengul and Tuncer, Turker. 2022. "A new lateral geniculate nucleus pattern-based environmental sound classification using a new large sound dataset." Applied Acoustics. 196. https://doi.org/10.1016/j.apacoust.2022.108897TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
Barua, Prabal Datta, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Palmer, Elizabeth Emma, Ciaccio, Edward J. and Acharya, U. Rajendra. 2022. "TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals." Diagnostics. 12 (10). https://doi.org/10.3390/diagnostics12102544A set of quality metrics for the evaluation of voice termination services
Chan, Ka C., Zhou, Xujuan, Gururajan, Raj and Barua, Prabal. 2018. "A set of quality metrics for the evaluation of voice termination services." 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018). Kaohsiung, Taiwan 12 - 14 Nov 2018 Los Alamitos, CA, United States. https://doi.org/10.1109/BESC.2018.00036Application of deep learning models for automated identification of Parkinson’s disease: a review (2011–2021)
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Roui, Mohammad Beheshti, Zomorodi, Mariam, Sarvelayati, Masoomeh, Abdar, Moloud, Noori, Hamid, Plawiak, Pawel, Tadeusiewicz, Ryszard, Zhou, Xujuan, Khosravi, Abbas, Nahavandi, Saeid and Acharya, U. Rajendra. 2021. "A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs." Knowledge-Based Systems. 231. https://doi.org/10.1016/j.knosys.2021.107419Automated major depressive disorder detection using melamine pattern with EEG signals
Aydemir, Emrah, Tuncer, Tucker, Dogan, Sengul, Gururajan, Raj and Acharya, U. Rajendra. 2021. "Automated major depressive disorder detection using melamine pattern with EEG signals." Applied Intelligence. 51, pp. 6449-6466. https://doi.org/10.1007/s10489-021-02426-yAutomated interpretation of biopsy images for the detection of celiac disease using a machine learning approach
Koh, Joel En Wei, de Michele, Simona, Sudarshan, Vidya K., Jahmunah, V., Ciaccio, Edward J., Ooi, Chui Ping, Gururajan, Raj, Gururajan, Rashmi, Oh, Shu Lih, Lewis, Suzanne K., Green, Peter H., Bhagat, Govind and Acharya, U. Rajendra. 2021. "Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach." Computer Methods and Programs in Biomedicine. 203. https://doi.org/10.1016/j.cmpb.2021.106010Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science
Jahmunah, Vicnesh, Sudarshan, Vidya K., Oh, Shu Lih, Gururajan, Raj, Gururajan, Rashmi, Zhou, Xujuan, Tao, Xiaohui, Faust, Oliver, Ciaccio, Edward J., Ng, Kwan Hoong and Acharya, U. Rajendra. 2021. "Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science." International Journal of Imaging Systems and Technology. 31 (2), pp. 455-471. https://doi.org/10.1002/ima.22552A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection
Zhou, Xujuan, Li, Yuefeng, Gururajan, Raj, Bargshady, Ghazal, Tao, Xiaohui, Venkataraman, Revathi, Barua, Prabal D. and Kondalsamy-Chennakesavan, Srinivas. 2020. "A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection." 7th International Conference on Behavioural and Social Computing (BESC 2020). Bournemouth, United Kingdom 05 - 07 Nov 2020 United Kingdom. https://doi.org/10.1109/BESC51023.2020.9348322A survey on text classification and its applications
Zhou, Xujuan, Gururajan, Raj, Li, Yuefeng, Venkataraman, Revathi, Tao, Xiaohui, Bargshady, Ghazala, Barua, Prabal D. and Kondalsamy-Chennakesavan, Srinivas. 2020. "A survey on text classification and its applications." Web Intelligence. 18 (2), pp. 205-216. https://doi.org/10.3233/WEB-200442Automated prediction of sepsis using temporal convolutional network
Kok, Christopher, Jahmunah, V., Oh, Shu Lih, Zhou, Xujuan, Gururajan, Raj, Tao, Xiaohui, Cheong, Kang Hao, Gururajan, Rashmi, Molinari, Filippo and Acharya, U. Rajendra. 2020. "Automated prediction of sepsis using temporal convolutional network." Computers in Biology and Medicine. 127, pp. 1-10. https://doi.org/10.1016/j.compbiomed.2020.103957Determination of factors influencing student engagement using a learning management system in a tertiary setting
Barua, Prabal Datta. 2020. Determination of factors influencing student engagement using a learning management system in a tertiary setting. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/Z9AS-4P42Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach
Nasarian, Elham, Abdar, Moloud, Fahami, Mohammad Amin, Alizadehsani, Roohallah, Hussain, Sadiq, Basiri, Mohammad Ehsan, Zomorodi-Moghadam, Mariam, Zhou, Xujuan, Plawiak, Pawel, Acharya, U. Rajendra, Tan, Ru-San and Sarrafzadegan, Nizal. 2020. "Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach." Pattern Recognition Letters. 133, pp. 33-40. https://doi.org/10.1016/j.patrec.2020.02.010Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease
Zomorodi-Moghadam, Mariam, Abdar, Moloud, Davarzani, Zohreh, Zhou, Xujuan, Pławiak, Pawel and Acharya, U. Rajendra. 2021. "Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease." Expert Systems: the journal of knowledge engineering. 38 (1), pp. 1-17. https://doi.org/10.1111/exsy.12485A novel framework for distress detection through an automated speech processing system
Rana, Rajib, Gururajan, Raj, Mackenzie, Geraldine, Dunn, Jeff, Gray, Anthony, Zhou, Xujuan, Barua, Prabal Datta, Epps, Julien and Humphris, Gerald Michael. 2018. "A novel framework for distress detection through an automated speech processing system." 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2018). Santiago, Chile 03 - 06 Dec 2018 Los Alamitos, CA, United States. https://doi.org/10.1109/WI.2018.00-29Determination of factors influencing student engagement using a learning management system in a tertiary setting
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Abdar, Moloud, Zomorodi-Moghadam, Mariam, Zhou, Xujuan, Gururajan, Raj, Tao, Xiaohui, Barua, Prabal D. and Gururajan, Rashmi. 2020. "A new nested ensemble technique for automated diagnosis of breast cancer." Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2018.11.004Objective Analysis of Marker Bias in Higher Education
Chakraborty, Subrata, Zhou, Susan, Hafeez-Baig, Abdul, Gururajan, Raj, Paul, Manoranjan, Mandal, Anuradha, Chacko, Anila Elizabeth and Barua, Prabal D.. 2017. "Objective Analysis of Marker Bias in Higher Education." 5th IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE 2016). Bangkok, Thailand 07 - 09 Dec 2016 Piscataway, United States. https://doi.org/10.1109/TALE.2016.7851839This item comprises data from the following sources: Scopus