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Gaiduk M, Serrano Alarcón Á, Seepold R, Martínez Madrid N. Current status and prospects of automatic sleep stages scoring: Review. Biomed Eng Lett 2023; 13:247-272. [PMID: 37519865 PMCID: PMC10382458 DOI: 10.1007/s13534-023-00299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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Affiliation(s)
- Maksym Gaiduk
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
| | | | - Ralf Seepold
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
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Karabiber Cura O, Kocaaslan Atli S, Akan A. Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6343. [PMID: 34640663 PMCID: PMC8512967 DOI: 10.3390/s21196343] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022]
Abstract
As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
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Affiliation(s)
- Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Jain R, Ganesan RA. Reliable sleep staging of unseen subjects with fusion of multiple EEG features and RUSBoost. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Huang H, Zhang J, Zhu L, Tang J, Lin G, Kong W, Lei X, Zhu L. EEG-Based Sleep Staging Analysis with Functional Connectivity. SENSORS (BASEL, SWITZERLAND) 2021; 21:1988. [PMID: 33799850 PMCID: PMC7999974 DOI: 10.3390/s21061988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/20/2022]
Abstract
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.
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Affiliation(s)
- Hui Huang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Li Zhu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jiajia Tang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Guang Lin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Wanzeng Kong
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China;
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing 400715, China
| | - Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
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Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2020.08.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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