Editorial: Cardiorespiratory Coupling-Novel Insights for Integrative Biomedicine

Acampa, Maurizio; Voss, Andreas; Bojić, Tijana

doi:10.3389/fnins.2021.671900

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…CONCLUSION. Central autonomic dynamics specific for sleep stages (13) causes peripheral consequences on CCV system and respiratory system, the supersystems (14,15,16,17,18,19,20,21) that can be pathologically changed. These peripheral effects can precipitate life-threatening CCV events that demand prompt and physiologically justified, sleep phase specific treatment.…”

Section: Nocturnal Cardiovascular Events Nocturnal Myocardial Infarctionmentioning

confidence: 99%

“…Материал и методы исследования. Наблюдали 264 пациента после реконструктивных операций на сердце и сосудах: 143 -коронарное шунтирование (аортокоронарное шунтирование -АКШ, маммарокоронарное шунтирование -МКШ), 19 -пороки сердца (ПС), 102 -баллонная ангиопластика (БА) и коронарное стентирование (КС). Как правило, пациенты поступали в кардиологическое отделение санатория на 15 день после АКШ и ПС, на 7-10 день после БА и КС.…”

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12th All-Russian Conference "Contradictions of Modern Cardiology: Controversial and unresolved issues". Collection of abstracts

Editorial

2023

Russ J Cardiol

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Section: Nocturnal Cardiovascular Events Nocturnal Myocardial Infarctionmentioning

confidence: 99%

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12th All-Russian Conference "Contradictions of Modern Cardiology: Controversial and unresolved issues". Collection of abstracts

Editorial

2023

Russ J Cardiol

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“…Particularly, relevant studies have mainly focused on heart rate variability (HRV) to evaluate cardiac autonomic activity in women with preeclampsia, suggesting that preeclamptic women may show increased cardiac sympathetic activity with decreased parasympathetic activity compared to normotensive control women (2)(3)(4)(5). Novel evidence indicates that changes to the central respiratory network's connections with the sympathetic nervous system and the vagus nerve may significantly impact CRC (6). However, the autonomic mechanisms involved in preeclampsia have not yet been fully explored by quantifying the CRC.…”

Section: Introductionmentioning

confidence: 99%

Changes in the autonomic cardiorespiratory activity in parturient women with severe and moderate features of preeclampsia

Pichardo-Carmona,

Reyes-Lagos,

Ceballos-Juárez

et al. 2023

Front. Immunol.

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BackgroundCardiorespiratory coupling (CRC) is a physiological phenomenon that reflects the mutual interaction between the cardiac and respiratory control systems. It is mainly associated with efferent vagal activity from the central autonomic network. Few studies have explored the autonomic changes of CRC in preeclampsia, a critical obstetric complication related to possible autonomic dysfunctions and inflammatory disturbances. This study examined the autonomic mechanisms of CRC in women with severe and moderate preeclampsia and healthy controls by applying nonlinear methods based on information theory, such as mutual information (MI) and Renyi’s mutual information (RMI) and the linear and nonlinear analysis of the Pulse-Respiration Quotient (PRQ).MethodsWe studied three groups of parturient women in the third trimester of pregnancy with a clinical diagnosis of preeclampsia without severe symptoms (P, 38.5 ± 1.4 weeks of pregnancy, n=19), preeclampsia with severe symptoms (SP, 37.5 ± 0.9 weeks of pregnancy, n=22), and normotensive control women (C, 39.1 ± 1.3 weeks of pregnancy, n=20). 10-minutes of abdominal electrocardiograms (ECG) and respiratory signals (RESP) were recorded in all the participants. Subsequently, we obtained the maternal beat-to-beat (RR) and breath-to-breath (BB) time series from ECG and RESP, respectively. The CRC between RR and BB was quantified by nonlinear methods based on information theory, such as MI and RMI, along with the analysis of the novel index of PRQ. Subsequently, we computed the mean PRQ (mPRQ) and the normalized permutation entropy (nPermEn_PRQ) from the PRQ time series generated from BB and RR. In addition, we examined the vagal activity in the three groups by the logarithm of the median of the distribution of the absolute values of successive RR differences (logRSA). ResultsThe MI and RMI values were significantly lower (p<0.05) in the preeclamptic groups compared to the control group. However, no significant differences were found between the preeclamptic groups. The logRSA and nPermEn_PRQ indices were significantly lower (p<0.05) in SP compared to C and P.ConclusionOur data suggest that parturient women with severe and mild preeclampsia may manifest an altered cardiorespiratory coupling compared with normotensive control women. Disrupted CRC in severe preeclampsia could be associated with vagal withdrawal and less complex cardiorespiratory dynamics. The difference in vagal activity between the preeclamptic groups may suggest a further reduction in vagal activity associated with the severity of the disease.

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“…Several studies have demonstrated the feasibility of cardiorespiratory signals and/or HRV features in predicting sleep stages 7 , 10 , 11 . Additional insights have been provided by investigating the mutual interactions of cardiorespiratory oscillations 12 – 14 . Studies analyzing cardiorespiratory interactions during sleep have shown that cardiorespiratory phase coupling is strongly affected during sleep apnea events 15 and that the temporal dynamics of slow-wave cortical activity during sleep are correlated with cardiorespiratory phase coupling 16 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders

Morokuma,

Hayashi,

Kanegae

et al. 2023

Sci Rep

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Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19–82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea–hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland–Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33–0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.

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Editorial: Cardiorespiratory Coupling-Novel Insights for Integrative Biomedicine

Cited by 7 publications

References 16 publications

12th All-Russian Conference "Contradictions of Modern Cardiology: Controversial and unresolved issues". Collection of abstracts

12th All-Russian Conference "Contradictions of Modern Cardiology: Controversial and unresolved issues". Collection of abstracts

Changes in the autonomic cardiorespiratory activity in parturient women with severe and moderate features of preeclampsia

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders

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