Gamma Power of Electroencephalogram Arousal Is Modulated by Respiratory Event Type and Severity in Obstructive Sleep Apnea

Pitkanen, Henna; Duce, Brett; Leppänen, Timo; Kainulainen, Samu; Kulkas, Antti; Myllymaa, Sami; Töyräs, Juha; Korkalainen, Henri

doi:10.1109/tbme.2021.3118229

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Azarbarzin et al [18] showed that the cardiovascular response also contains information on the arousal intensity. As obstruction type and the presence of desaturation, found to intensify the arousal response in the present study, have also been shown to strengthen the EEG changes in arousal [12] , [13] , the present study indirectly supports the findings of Azarbarzin et al [18] with a much larger subject population. The delays between the EEG arousal and the cardiovascular responses should also be taken into account when detecting arousals based on PPG if the timing of the arousals is considered important.…”

Section: Discussionsupporting

confidence: 90%

“…The American Academy of Sleep Medicine (AASM) defines an arousal as a sudden shift in the electroencephalogram (EEG) frequency [11] . The magnitude of the EEG activity change during an arousal is affected by the arousal-causing respiratory event severity (aspects such as obstruction type, i.e., apneas vs. hypopneas, and presence of blood oxygen desaturation) [12] , [13] .…”

Section: Introductionmentioning

confidence: 99%

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Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation

Luukinen,

Pitkänen,

Leppänen

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Objective: The aim of this study was to assess how the photoplethysmogram frequency and amplitude responses to arousals from sleep differ between arousals caused by apneas and hypopneas with and without blood oxygen desaturations, and spontaneous arousals. Stronger arousal causes were hypothesized to lead to larger and faster responses. Methods and procedures: Photoplethysmogram signal segments during and around respiratory and spontaneous arousals of 876 suspected obstructive sleep apnea patients were analyzed. Logistic functions were fit to the mean instantaneous frequency and instantaneous amplitude of the signal to detect the responses. Response intensities and timings were compared between arousals of different causes. Results: The majority of the studied arousals induced photoplethysmogram responses. The frequency response was more intense ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${p} < 0.001$\end{document} ) after respiratory than spontaneous arousals, and after arousals caused by apneas compared to those caused by hypopneas. The amplitude response was stronger ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${p} < 0.001$\end{document} ) following hypopneas associated with blood oxygen desaturations compared to those that were not. The delays of these responses relative to the electroencephalogram arousal start times were the longest ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${p} < 0.001$\end{document} ) after arousals caused by apneas and the shortest after spontaneous arousals and arousals caused by hypopneas without blood oxygen desaturations. Conclusion: The presence and type of an airway obstruction and the presence of a blood oxygen desaturation affect the intensity and the timing of photoplethysmogram responses to arousals from sleep. Clinical impact: The photoplethysmogram responses could be used for detecting arousals and assessing their intensity, and the individual variation in the response intensity and timing may hold diagnostically significant information.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation

Luukinen,

Pitkänen,

Leppänen

et al. 2024

IEEE J. Transl. Eng. Health Med.

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“…This is consistent with the literature, where the interscorer agreement in EEG segment classification into arousals or stable sleep has been lower in R&K (Rechtschaffen & Kales, 1968) stages 1+2 than 3+4 (Drinnan et al, 1998). As EEG high‐frequency content related to arousals is known to increase more when the arousals occur in deep sleep compared to light sleep (Pitkänen et al, 2022), the EEG frequency shift indicating an arousal may be more easily distinguishable in deeper sleep.…”

Section: Discussionmentioning

confidence: 99%

Multi‐centre arousal scoring agreement in the Sleep Revolution

Pitkänen,

Nikkonen,

Rissanen

et al. 2023

Journal of Sleep Research

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SummaryWe investigated arousal scoring agreement within full‐night polysomnography in a multi‐centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second‐by‐second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04–0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second‐by‐second agreement was fair (Fleiss’ kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07–0.68). Fleiss’ kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes.

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“…Furthermore, the performance of the OSA screening improved when the model was trained by adding the number of arousals in addition to NREM/REM EEG features ( Supplementary Figure S3 ). Although the current study did not cover the contents related to the arousal index in-depth, given that the recent findings demonstrated that gamma power in EEG arousal differs according to the severity of the respiratory event and sleep stages ( Pitkänen et al, 2021 ), future studies may need to closely examine such characteristics and incorporate them into building the models as needed.…”

Section: Discussionmentioning

confidence: 99%

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

Kang

Kim

et al. 2023

Front. Neurosci.

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IntroductionSleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease.MethodsThe first crucial step in evaluating individuals’ quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance.ResultsThe performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures.DiscussionThe current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals’ sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

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Gamma Power of Electroencephalogram Arousal Is Modulated by Respiratory Event Type and Severity in Obstructive Sleep Apnea

Cited by 9 publications

References 30 publications

Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation

Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation

Multi‐centre arousal scoring agreement in the Sleep Revolution

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

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