Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea

Korkalainen, Henri; Leppänen, Timo; Duce, Brett; Kainulainen, Samu; Aakko, Juhani; Leino, Aila; Kalevo, Laura; Afara, Isaac O.; Myllymaa, Sami; Töyräs, Juha

doi:10.1109/jbhi.2020.3043507

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…The proposed automatic sleep staging method, which is based on EEG, still needs improvement, mainly in the following aspects: (1) The impact of other physiological parameters should be considered. As sleep is a complex process, physiological parameters, such as electrocardio, myoelectricity, electro-oculogram, and breath, have a certain influence on sleep ( 33 ). Hence, more parameters should be introduced into the study of sleep stages, which can further increase the accuracy of automatic sleep staging.…”

Section: Discussionmentioning

confidence: 99%

Intelligent automatic sleep staging model based on CNN and LSTM

Liu

Dai

Zhou

et al. 2022

Front. Public Health

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Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.

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Section: Discussionmentioning

confidence: 99%

Intelligent automatic sleep staging model based on CNN and LSTM

Liu

Dai

Zhou

et al. 2022

Front. Public Health

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“…A possible source of this ambiguity captured by the hypnodensity may be sleep stage shifts occurring within one 30-s epoch. Korkalainen et al (2021a) used a deep learning approach based on the traditional 30-s epoch duration as well as based on shorter epoch durations (15-, 5-, 1-, and 0.5-s) to evaluate differences in sleep architecture between obstructive sleep apnea (OSA) severity groups. The authors reported decreases in sleep continuity with increases in OSA severity using Cox proportional hazards ratio or Kaplan-Meier survival curves, and these group differences became larger (Fiorillo et al, 2023a).…”

Section: Hypnodensity-derived Sleep Stages and Parametersmentioning

confidence: 99%

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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“…We will accomplish this by developing personalised SDB diagnostic parameters that predict future adverse health consequences and determine treatment needs in a patient‐specific manner. This includes an in‐depth assessment of the duration of respiratory events, the pattern of hypoxic burden during sleep, the level of sleep fragmentation, as well as the sympathetic and cardiovascular response to SDB events (Azarbarzin et al, 2019; Kainulainen, Duce, et al, 2020; Kainulainen, Töyräs, et al, 2020; Korkalainen et al, 2021; Kulkas et al, 2013; Muraja‐Murro et al, 2013, 2014).…”

Section: The Objectives Of the Sleep Revolutionmentioning

confidence: 99%

“…We will accomplish this by developing personalised SDB diagnostic parameters that predict future adverse health consequences and determine treatment needs in a patient-specific manner. This includes an in-depth assessment of the duration of respiratory events, the pattern of hypoxic burden during sleep, the level of sleep fragmentation, as well as the sympathetic and cardiovascular response to SDB events (Azarbarzin et al, 2019;Kainulainen, Duce, et al, 2020;Korkalainen et al, 2021;Kulkas et al, 2013;Muraja-Murro et al, 2013. Also, overtreatment of patients, who benefit less from the intervention, can be better avoided by employing these more sophisticated diagnostic parameters valid in all three dimensions "A" (respiratory events), "E" (acute systemic effect), and "O" (chronic end-organ impact) (Pevernagie et al, 2020;Randerath et al, 2018).…”

Section: The Objectives Of the Sleep Revolutionmentioning

confidence: 99%

The Sleep Revolution project: the concept and objectives

Arnardóttir

Íslind

Óskarsdóttir

et al. 2022

Journal of Sleep Research

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Summary Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea–hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.

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Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea

Cited by 16 publications

References 26 publications

Intelligent automatic sleep staging model based on CNN and LSTM

Intelligent automatic sleep staging model based on CNN and LSTM

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

The Sleep Revolution project: the concept and objectives

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