Time-varying analysis of the heart rate variability during A-phases of sleep: Healthy and pathologic conditions

Dorantes-Méndez, Guadalupe; Méndez, Martín O.; Alba, Alfonso; Parrino, Liborio; Milioli, Giulia

doi:10.1016/j.bspc.2017.09.010

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…It is worth mentioning that, although the behavior of these patterns has been cited in the literature [9,10], to the best of our knowledge, this is the first study dedicated to analyze the different characteristics of U-patterns.…”

Section: Discussionmentioning

confidence: 99%

“…[0.01-0.04] Hz). Mendez et al [10] observed a quickening of heart rate during EEG A-phases, recurrent events during sleep that last between 2-60 seconds (average duration of 7 seconds) [11,12], and showed there is a significant difference in HRV indexes between a group of healthy subjects and patients suffering from nocturnal front lobe epilepsy.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of U-Shape Patterns in RR-Interval Time Series During Sleep

Yazdani¹,

Cherqui²,

Bourdillon³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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The proposed study investigates a phenomenon, defined as "U-patterns", that takes place in the RR-interval time series during sleep. These patterns are defined as a Ushaped decrease-increase in the RR-intervals, with a duration of 20 to 40 seconds with a minimum decrease of 15% in the local RR-interval mean value. This paper studies statistical characteristics of U-patterns on subjects undergoing sleep deprivation. 15 healthy subjects (7males, 22.1 ± 1.7 yrs.) participated in an experiment over a span of 17 days, in three successive stages. A baseline phase of seven days, during which the subjects slept normally; A sleep deprivation phase of three days, during which they could only sleep three hours per night; Finally, in a 7day recovery phase subjects went back to sleeping normally, as they would in the baseline phase. While sleeping, polysomnographic data was recorded from the participants. U-patterns were extracted and their statistical characteristics were analyzed. Alongside the incidence of these patterns, their depth, duration and area were measured. U-patterns were present in all participating subjects. Moreover, these patterns were recurrent in all RRinterval time series. There was a significant difference in their repetition rate, depth and duration from baseline to sleep-deprivation and recovery. Results show that the characteristics of U-patterns change when subjects are undergoing sleep deprivation, suggesting these patterns can be used to identify patients suffering from sleep disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of U-Shape Patterns in RR-Interval Time Series During Sleep

Yazdani¹,

Cherqui²,

Bourdillon³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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“…A study by Ferri et al [22] of six normal children and adolescents supports the findings of altered sympathovagal balance. In their studies on HRV during CAP in healthy subjects and patients with nocturnal front lobe epilepsy (NFLE), Dorantes-Mendez et al [23,24] demonstrated a comparable significant shift towards the low-frequency components of HRV with a more pronounced shift in A3-phases than in A1-and A2-phases. Furthermore, a study on the relation between EEG changes defined by A-phases and the pulse wave amplitude (PWA) after airway obstruction in patients with obstructive sleep apnoea demonstrates a significant correlation between respiratory events combined with A-phases and respiratory events combined with PWA drops [25].…”

Section: Introductionmentioning

confidence: 99%

Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep

Hartmann

Ferri

Bruni

et al. 2021

Phil. Trans. R. Soc. A.

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The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical–cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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“…This technique has been utilizing the morphology of the pQRSt waves from the ECG signal to acquire four groups of feature sets to identify the severity of Obstructive Sleep Apnea (OSA) [6]. Another approach uses a variation between normal patients and epilepsy patients based on HRV using timevarying autoregressive modelling [7]. In addition, the features of intrinsic band functions come from ECG-Derived Respiration (EDR) Signals and HRV measurements [8].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Predict Potential Sleep Disorder based on Fractal Analysis from Non-overlapping Single Lead ECG Using Support Vector Machine

Purnomo¹

2021

IJIES

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Sleep disorders are challenging to diagnose. The complexity of records obtained from electrocardiogram (ECG) recordings requires manual inspection by experienced medical practitioners. Meanwhile, ECG records are still widely used to diagnose heart problems during sleep. To resolve the issue, the fractal analysis is a promising means to help identify the characteristics of non-overlapping apnea and non-apnea events based on signal scaling behaviour and QRS wave morphologies. Therefore, we propose a new approach to develop automatic sleep disorder classification to minimalize visual inspection and manual scoring. We employed the monofractal and the multifractal analyses to generate new features such as alpha1, residue1, alpha2, residue2, Dqmin, Dqmax, hqmin, hqmid, hqmax, and hqmaxhqmin. To improve the proposed method's performance, we used the ten new features that have been extracted, which are eventually being used as inputs space to a support vector machine (SVM). Through examining the feature set, we designed an optimum SVM model classifier to explore the usability of patterns to predict potential sleep disorder corresponding to apnea and non-apnea events. Hence, our approach through SVM with radial basis function (RBF) kernel is achieved to have accuracy, sensitivity, specificity of 92.16%, 88.24%, 94.12% respectively.

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Time-varying analysis of the heart rate variability during A-phases of sleep: Healthy and pathologic conditions

Cited by 7 publications

References 18 publications

Analysis of U-Shape Patterns in RR-Interval Time Series During Sleep

Analysis of U-Shape Patterns in RR-Interval Time Series During Sleep

Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep

A New Approach to Predict Potential Sleep Disorder based on Fractal Analysis from Non-overlapping Single Lead ECG Using Support Vector Machine

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