Relational local electroencephalography representations for sleep scoring

Brandmayr, Georg; Hartmann, M.; Fürbass, Franz; Matz, Gerald; Samwald, Matthias; Kluge, Tilmann; Dorffner, Georg

doi:10.1016/j.neunet.2022.07.020

Cited by 7 publications

(9 citation statements)

References 25 publications

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“…Table 2 summarizes the kappa values for the 20 validation studies of the 6 AI-based autoscoring algorithms outputting the hypnodensity graph (Stephansen et al, 2018;Cesari et al, 2021Cesari et al, , 2022Vallat and Walker, 2021;Anderer et al, 2022b;Brandmayr et al, 2022;Bakker et al, 2023;Fiorillo et al, 2023b). As can be seen in Table 2, Cohen's kappa for the 5-stage comparison was comparable between the six algorithms.…”

Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 93%

“…Based on these probabilities, it is possible to create a hypnodensity chart from autoscoring which can be directly compared to the hypnodensity chart based on multiple expert scorings. In Table 2 we summarize publications using AI-algorithms for sleep staging and reporting autoscored hypnodensity graphs (Stephansen et al, 2018;Cesari et al, 2021Cesari et al, , 2022Vallat and Walker, 2021;Anderer et al, 2022b;Brandmayr et al, 2022;Bakker et al, 2023;Fiorillo et al, 2023b). In addition to information regarding the training datasets, the epoch encoder including feature extraction, the sequence encoder and classifier, and the test datasets, the Cohen's kappa values obtained in each study are given.…”

Section: Hypnodensity Based On Autoscoring Using Neurological Signalsmentioning

confidence: 99%

“…Note the large difference in the size of the training data (between 10 and more than 15,000 PSGs) as well as in the size of the test data (between 8 and close to 3000). Table 3 summarizes validation results of AI-algorithms that applied a hold-out or crossvalidation; i.e., an internal validation based on data from the same dataset that has been used for training (Supratak et al, 2017;Sors et al, 2018;Phan et al, 2019;Zhang et al, 2019;Abou Jaoude et al, 2020;Guillot et al, 2020;Korkalainen et al, 2020;Sun et al, 2020a;Alvarez-Estevez and Rijsman, 2021;Fiorillo et al, 2021Fiorillo et al, , 2023bJia et al, 2021;Nasiri and Clifford, 2021;Olesen et al, 2021;Pathak et al, 2021;Vallat and Walker, 2021;Brandmayr et al, 2022;Cho et al, 2022;Ji et al, 2022;Li C. et al, 2022;Sharma et al, 2022;Yubo et al, 2022). Table 4 summarizes the validation results of AI-algorithms which have been validated in datasets completely unseen by the model (Anderer et al, 2018(Anderer et al, , 2022bBiswal et al, 2018;Patanaik et al, 2018;Stephansen et al, 2018;Zhang et al, 2019;Abou Jaoude et al, 2020;Alvarez-Estevez and Rijsman, 2021;Cesari et al, 2021Cesari et al, , 2022Vallat and Walker, 2021;Bakker et al, 2023).…”

Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 99%

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Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Anderer¹,

Ross²,

Cerny³

et al. 2023

Front. Sleep

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Human experts scoring sleep according to the American Academy of Sleep Medicine (AASM) rules are forced to select, for every 30-second epoch, one out of five stages, even if the characteristics of the neurological signals are ambiguous, a very common occurrence in clinical studies. Moreover, experts cannot score sleep in studies where these signals have not been recorded, such as in home sleep apnea testing (HSAT). In this topic review we describe how artificial intelligence can provide consistent and reliable scoring of sleep stages based on neurological signals recorded in polysomnography (PSG) and on cardiorespiratory signals recorded in HSAT. We also show how estimates of sleep stage probabilities, usually displayed as hypnodensity graph, can be used to quantify sleep stage ambiguity and stability. As an example of the application of hypnodensity in the characterization of sleep disordered breathing (SDB), we compared 49 patients with sleep apnea to healthy controls and revealed a severity-depending increase in ambiguity and decrease in stability during non-rapid eye movement (NREM) sleep. Moreover, using autoscoring of cardiorespiratory signals, we show how HSAT-derived apnea-hypopnea index and hypoxic burden are well correlated with the PSG indices in 80 patients, showing how using this technology can truly enable HSATs as alternatives to PSG to diagnose SDB.

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Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 93%

Section: Hypnodensity Based On Autoscoring Using Neurological Signalsmentioning

confidence: 99%

Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Anderer¹,

Ross²,

Cerny³

et al. 2023

Front. Sleep

View full text Add to dashboard Cite

show abstract

Section: Hypnodensity Based On Autoscoring Using Neurological Signalsmentioning

confidence: 99%

“…Note the large difference in the size of the training data (between 10 and more than 15,000 PSGs) as well as in the size of the test data (between 8 and close to 3000). Table 3 summarizes validation results of AI-algorithms that applied a hold-out or crossvalidation; i.e., an internal validation based on data from the same dataset that has been used for training (Supratak et al, 2017;Sors et al, 2018;Phan et al, 2019;Abou Jaoude et al, 2020;Guillot et al, 2020;Korkalainen et al, 2020;Sun et al, 2020a;Alvarez-Estevez and Rijsman, 2021;Fiorillo et al, 2021Fiorillo et al, , 2023bJia et al, 2021;Nasiri and Clifford, 2021;Olesen et al, 2021;Pathak et al, 2021;Vallat and Walker, 2021;Brandmayr et al, 2022;Cho et al, 2022;Ji et al, 2022;Li C. et al, 2022;Sharma et al, 2022;Yubo et al, 2022). Table 4 summarizes the validation results of AI-algorithms which have been validated in datasets completely unseen by the model (Anderer et al, 2018(Anderer et al, , 2022bBiswal et al, 2018;Patanaik et al, 2018;Stephansen et al, 2018;Abou Jaoude et al, 2020;Alvarez-Estevez and Rijsman, 2021;Cesari et al, 2021Cesari et al, , 2022Vallat and Walker, 2021;Bakker et al, 2023).…”

Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 99%

Novel Technologies in the Diagnosis and Management of Sleep-disordered Breathing

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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A linear-attention-combined convolutional neural network for EEG-based visual stimulus recognition

Huang,

Chen,

Zhang

2024

Biocybernetics and Biomedical Engineering

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Relational local electroencephalography representations for sleep scoring

Cited by 7 publications

References 25 publications

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Novel Technologies in the Diagnosis and Management of Sleep-disordered Breathing

A linear-attention-combined convolutional neural network for EEG-based visual stimulus recognition

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