A Systematic Review of Literature on Automated Sleep Scoring

Alsolai, Hadeel; Qureshi, Shahnawaz; Iqbal, Syed Muhammad Zeeshan; Vanichayobon, Sirirut; Henesey, Lawrence; Lindley, Craig A.; Karrila, Seppo

doi:10.1109/access.2022.3194145

Cited by 10 publications

(5 citation statements)

References 146 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automated EEG decision-support tools are also becoming available (e.g. seizure surveillance) [10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Changes in the Term Neonatal Electroencephalogram with General Anesthesia: A Systematic Review with Narrative Synthesis

Corlette,

Walker,

Cornelissen

et al. 2024

Anesthesiology

View full text Add to dashboard Cite

While effects of general anesthesia on neuronal activity in the human neonatal brain are incompletely understood, electroencephalography (EEG) provides some insight and may identify age-dependent differences. A systematic search (MEDLINE, Embase, PUBMED, Cochrane Library to November 2023) retrieved English language publications reporting EEG during general anesthesia for cardiac or non-cardiac surgery in term neonates (37 to 44 weeks post-menstrual age). Data were extracted and risk of bias (ROBINS-I Cochrane tool) and quality of evidence (GRADE checklist) assessed. From 1155 abstracts, nine publications (157 neonates; 55.7% male) fulfilled eligibility criteria. Data were limited and study quality was very low. The occurrence of discontinuity, a characteristic pattern of alternating higher and lower amplitude EEG segments, was reported with general anesthesia (94 of 119 neonates, six publications) and with hypothermia (23 of 23 neonates, two publications). Decreased power in the delta (0.5-4Hz) frequency range was also reported with increasing anesthetic dose (39 neonates; three publications). While evidence gaps were identified, both increasing sevoflurane concentration and decreasing temperature are associated with increasing discontinuity.

show abstract

“…Automated EEG decision-support tools are also becoming available (e.g. seizure surveillance) [10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Changes in the Term Neonatal Electroencephalogram with General Anesthesia: A Systematic Review with Narrative Synthesis

Corlette,

Walker,

Cornelissen

et al. 2024

Anesthesiology

View full text Add to dashboard Cite

show abstract

“…However, studies in recent years have focused on specific aspects of the problem. For example, in [ 17 ], only the algorithms that work with the EEG signal were selected. In [ 18 ], consumer sleep technologies (CSTs) were investigated to analyse sleep in combination with artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Current status and prospects of automatic sleep stages scoring: Review

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

“…Spectral linear features and an RF classifier led to the best classification performance, while ensuring real-time online processing. An extensive review of the current literature on automated sleep scoring can be found in [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

Cheng

Lech

Wilkinson

2023

Sensors

View full text Add to dashboard Cite

Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches.

show abstract

A Systematic Review of Literature on Automated Sleep Scoring

Cited by 10 publications

References 146 publications

Changes in the Term Neonatal Electroencephalogram with General Anesthesia: A Systematic Review with Narrative Synthesis

Changes in the Term Neonatal Electroencephalogram with General Anesthesia: A Systematic Review with Narrative Synthesis

Current status and prospects of automatic sleep stages scoring: Review

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

Contact Info

Product

Resources

About