Visibility graph analysis of very short-term heart rate variability during sleep

Hou, Fengzhen; Li, F.W.; Wang, J.; Yan, Fangrong

doi:10.1016/j.physa.2016.03.086

Cited by 22 publications

(22 citation statements)

References 29 publications

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“…Since sleep may vary with age and sex, and high body mass index (BMI) is a strong risk factor for sleep disorders (Hou et al, 2016 ), these three variables were included as covariates in the statistical test for correlation between pre-sleep EEG complexity and sleep latency, as well as subsequent sleep quality. General linear models (GLM) were thus employed for the statistical analyses in SPSS 19.0 between CI 1−30 and sleep latency, EEMD-SWA 90 , EEMD-SWA all , FFT-SWA 90 , FFT-SWA all and P theta , controlling age, gender, and BMI.…”

Section: Methodsmentioning

confidence: 99%

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

et al. 2018

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Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1−30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.

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Section: Methodsmentioning

confidence: 99%

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

et al. 2018

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“…Recently, visibility graphs have been employed to analyze the resulting time series from physiological data as in Hou et al (2016), Jiang et al (2013), and Shao (2010), in the analysis of complex networks for cardiorespiratory interactions (Long, 2015) or a modified visibility graph for the suicidal tendency (Bhaduri et al, 2016). However, in these works, the concept of maxcliques from graph theory was not implemented in the characterization and classification of waveforms.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph

Rodríguez-Torres

Paredes-Hernández

Vázquez-Mendoza

et al. 2020

Front. Bioeng. Biotechnol.

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Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.

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“…The basic function of the intelligent alarm clock is to monitor the ANR during the nap and awaken the user after good ANR has been detected. HR and HRV are effective markers of the autonomic nervous activity [15][16][17][18], and they can be derived from the ECG signal conveniently acquired through wearable ECG devices. The conventional indexes of HR and HRV are the mean of the RR intervals, standard deviation of the RR intervals, square root of the mean of the squares of differences between successive RR intervals (RMSSD), low frequency power (0.04 Hz-0.15 Hz), and high frequency power (0.15 Hz-0.4 Hz) [18].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Biological Alarm Clock for Monitoring Autonomic Nervous Recovery During Nap

Xie¹,

Wang²,

Liu³

et al. 2019

IJCIS

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Intelligent biological alarm clock ECG Heart rate Heart rate variability Autonomic nervous system A B S T R A C TNap is an effective way to reduce daily-level fatigue after several hours of work. However, no alarm clock, which intelligently manages the nap duration with good autonomic nervous recovery (ANR) from fatigue, has been reported in literature. In this work, an intelligent biological alarm clock algorithm was designed on the basis of electrocardiogram (ECG) and electroencephalogram (EEG) data acquisition and analysis. ECG data samples were collected from 31 subjects in 278 times of nap experiments and categorized into good, moderate, and poor ANR datasets according to the degree of sympathetic withdrawal and parasympathetic activation during the nap. In practice, the alarm clock automatically classified the new-coming ECG data as good, moderate, or poor ANR with a classifier trained by the abovementioned ANR datasets. A prototype system of the intelligent alarm clock algorithm was implemented and validated in real-scene naps. The prototype system detected falling asleep during the closed-eye naps with a true positive rate of 93.55% and a true negative rate of 100%.

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Visibility graph analysis of very short-term heart rate variability during sleep

Cited by 22 publications

References 29 publications

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph

Intelligent Biological Alarm Clock for Monitoring Autonomic Nervous Recovery During Nap

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