Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms

A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia

Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.

Co-ordination of brain and heart oscillations during non-rapid eye movement sleep

Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r² = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r² = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health.

Automatic sleep staging by cardiorespiratory signals: a systematic review

BACKGROUND

Because of problems with the recording and analysis of the EEG signal, automatic sleep staging using cardiorespiratory signals has been employed as an alternative. This study reports on certain critical points which hold considerable promise for the improvement of the results of the automatic sleep staging using cardiorespiratory signals.

METHODS

A systematic review.

RESULTS

The review and analysis of the literature in this area revealed four outstanding points: (1) the feature extraction epoch length, denoting that the standard 30-s segments of cardiorespiratory signals do not carry enough information for automatic sleep staging and that a 4.5-min length segment centering on each 30-s segment is proper for staging, (2) the time delay between the EEG signal extracted from the central nervous system activity and the cardiorespiratory signals extracted from the autonomic nervous system activity should be considered in the automatic sleep staging using cardiorespiratory signals, (3) the information in the morphology of ECG signals can contribute to the improvement of sleep staging, and (4) applying convolutional neural network (CNN) and long short-term memory network (LSTM) deep structures simultaneously to a large PSG recording database can lead to more reliable automatic sleep staging results.

CONCLUSIONS

Considering the above-mentioned points simultaneously can improve automatic sleep staging by cardiorespiratory signals. It is hoped that by considering the points, staging sleep automatically using cardiorespiratory signals, which does not have problems with the recording and analysis of EEG signals, yields results acceptably close to the results of automatic sleep staging by EEG signals.

The effect of three-circle post standing (Zhanzhuang) qigong on the physical and psychological well-being of college students: A randomized controlled trial

BACKGROUND

Qigong has a long-term application by integration of mind, breath and body to prevent and cure diseases. Researches show that qigong practice could adjust anxiety, the mechanism may found on brain and heart functions. Currently there are limitations on qigong's anxiety-release mechanism study between mind and body, and existing studies lack of evidence on electrophysiology research. Our objective to analyse qigong's anxiety-release effect and mechanism.

METHODS

A two-arm randomized clinical trial with 144 qigong naïve anxiety subjects without cerebral or cardiovascular diseases or other severe syndromes will be allocated to either a body and breath regulation group (n = 72) or a body regulation group (n = 72). Participants will conduct three-circle post standing qigong exercise 5 times per week for 8 weeks, while the three-circle post standing qigong combined with abdominal breath regulation (TCPSQ-BR) group will combined with abdominal breath regulation. The primary outcome will be the Self-Rating Anxiety Scale (SAS), and the secondary outcome will be complexity-based measures of heart rate and electroencephalogram (EEG) signals assessed at baseline and 8 weeks. Multiscale entropy analysis will be used as measure of complexity.

CONCLUSION

This study will be investigate the effects of qigong's anxiety-release by SAS, and will analyze the coordinates of EEG and heart rate variability (HRV) signals before and after three-circle post standing qigong (TCPSQ) practice, and to analyse their synergies by complex signal process method.

ETHICS AND TRAIL REGISTRATION

The protocol was approved by the institutional review boards of Beijing University of Chinese Medicine (2018BZHYLL0109). This study was registered with the "Chinese Clinical Trail Registry" in the WHO Registry Network (ChiCTR-Bon-17010840).

Sulprostone-Induced Gastric Dysrhythmia in the Ferret: Conventional and Advanced Analytical Approaches

Nausea and emesis resulting from disease or drug treatment may be associated with disrupted gastric myoelectric activity (GMA). Conventional analytical techniques can determine the relative degrees of brady-, normo-, and tachygastric power, but lose information relative to the basic slow wave shape. The aim of the present study was to investigate the application of advanced analytical techniques in the analysis of disrupted GMA recorded after administration of sulprostone, a prostaglandin E_3/1 agonist, in ferrets. Ferrets were implanted with radiotelemetry devices to record GMA, blood pressure, heart rate (HR) and core body temperature 1 week before the administration of sulprostone (30 μg/kg) or vehicle (saline, 0.5 mL/kg). GMA was initially analyzed using fast Fourier transformations (FFTs) and a conventional power partitioning. Detrended fluctuation analysis (DFA) was also applied to the GMA recordings to reveal information relative to the fluctuation of signals around local trends. Sample entropy (SampEn) analysis was used for examining the regularity of signals. Conventional signal processing techniques revealed that sulprostone increased the dominant frequency (DF) of slow waves, with an increase in the percentage power of the tachygastric range and a decrease in the percentage power of the normogastric range. DFA revealed that sulprostone decreased the fluctuation function, indicative of a loss of the variability of GMA fluctuations around local trends. Sulprostone increased SampEn values, indicating a loss of regularity in the GMA data. Behaviorally, sulprostone induced emesis and caused defecation. It also increased blood pressure and elevated HR, with an associated decrease in HR variability (HRV). Further analysis of HRV revealed a decrease in both low-frequency (LF) and high-frequency (HF) components, with an overall increase in the LF/HF ratio. Sulprostone did not affect core body temperature. In conclusion, DFA and SampEn permit a detailed analysis of GMA, which is necessary to understand the action of sulprostone to modulate gastric function. The action to decrease HRV and increase the LF/HF ratio may be consistent with a shift toward sympathetic nervous system dominance, commonly seen during nausea.

Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality

Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability.

Sleep staging algorithm based on multichannel data adding and multifeature screening

BACKGROUND

Sleep staging is an important basis of sleep research, which is closely related to both normal sleep physiology and sleep disorders. Many studies have reported various sleep staging algorithms of which the framework generally consists of three parts: signal preprocessing, feature extraction and classification. However, there are few studies on the superposition of signals and feature screening for sleep staging.

OBJECTIVE

The objectives were to (1) Analyze the effective signal enhancement based on the superposition of homologous and heterogeneous signals, (2) Find a better way to use multichannel signals, (3) Study a systematic method of feature screening for sleep staging, and (4) Improve the performance of automatic sleep staging.

METHODS

In this paper, a novel method of signal preprocessing and feature screening was proposed. In the signal preprocessing, multi-channel signal superposition was applied to improve the effective information contained in the original signal. In the feature screening, 62 features were initially selected including the time-domain features, frequency-domain features and nonlinear features, and a ReliefF algorithm was employed to select 14 features highly correlated to sleep stages from the former 62 features. Then, Pearson correlation coefficients were used to remove 2 redundant features from the 14 features to eventually obtain 12 features. Next, with the aforementioned signal preprocessing method, the 12 selected features and a support vector machine (SVM) classifier were used for sleep staging based on thirty recordings.

RESULTS

Comparing the performance of sleep staging using different single-channel signals and different multi-channel superposition signals, we found that the best performance was obtained while using the superposition of two electroencephalogram (EEG) signals. The overall accuracies of sleep staging with 2-6 classes obtained by superposing the two EEG signals reach 98.28%, 95.50%, 94.28%, 93.08% and 92.34%, respectively, and the kappa coefficient of sleep staging with 6 classes reaches 84.07%.

CONCLUSIONS

Among the proposed sleep staging methods of using single-channel signal and multi-channel signal superposition, the best performance and consistency were obtained while using the superposition of two electroencephalogram (EEG) signals. The multichannel signal superposition method pointed out a valuable direction for improving the performance of automatic sleep staging in both theoretical research and engineering applications, and the proposed systematical feature screening method opened up a reasonable pathway for better selecting type and number of features for sleep staging.

Robust Fetal Heart Beat Detection via R-Peak Intervals Distribution

Monitoring fetal heart rate during pregnancy is essential to assist clinicians in making more timely decisions. Non-invasive monitoring of fetal heart activities using abdominal ECGs is useful for diagnosis of heart defects. However, the extracted fetal ECGs are usually too weak to be robustly detected. Thus, it is a necessity to enhance fetal R-peak since their peaks may be hidden within the signal due to the immaturity of the fetal cardiovascular system. Therefore, to improve the detection of the fetal heartbeat, a novel fetal R-peak enhancement technique was proposed to statistically generate the weighting mask according to the distribution of the neighboring temporal intervals between each pair of peaks. Two sets of simulations were designed to validate the reliability of the method: challenges with different levels of (1) noise contamination and (2) R-peak interval changing rate. The simulation results showed that the weighting mask improved the accuracy of the R-peak detection rate by 25% and decreased the false alarm rate by 20% with white noise contamination, and ensured high R-peak detection rate (>80%), especially with mild noise contamination (noise amplitude ratio <1.5 and noise rate per minute <25%). For the simulations with continuous R-peak intervals changing, the masking process can still effectively eliminate noise contamination especially when the amplitude of the sinusoidal fetal R-R intervals is lower than 50 ms. For the real fetus ECGs, the detection rate was increased by 3.498%, whereas the false alarm rate was decreased by 3.933%. Next, we implemented the fetal R-peak enhancement technique to investigate fractal regulation and multiscale entropy of the real fetal heartbeat intervals. Both scaling exponent (∼0.6 to ∼1 in scale 4-15) and entropy measure (scale 6-10) increased with gestational ages (22-40 weeks). The results confirmed fractal slope and complexity of fetal heartbeat intervals can reflect the maturation of fetus organism.

Sleep stage detection using only heart rate

Getting enough quality sleep plays a vital role in protecting our mental health, physical health, and quality of life. Sleep deprivation can make it difficult to concentrate on daily activities, and lower sleep quality is associated with hypertension, hyperglycemia, and hyperlipidemia. The amount of sleep we get is important, but in recent years, quality sleep has also been deemed significant. Polysomnography, which has been the gold standard in assessing sleep quality based on stages, requires that the subject be attached to electrodes, which can disrupt sleep. An easier method to objectively measure sleep is therefore needed. The aim of this study was to construct an easy and objective sleep stage monitoring method. A cross-sectional study for healthy subjects has been done in our research. A new easy model for monitoring the sleep stages is built on only heart rate calculated by the electrocardiogram. This enabled us to easily assess the sleep quality based on five stages. This experiment included a total of 50 subjects. The overall accuracy in determining the five sleep stages was 66.0 percent. Four stages for sleep are identified accurately compared with other conventional methods. Despite there are no five sleep stage separation method using only heart rate, our method achieved the five separation for sleep with a relatively good accuracy. This study represents a great contribution to the field of sleep science. Because sleep stages can be recognized by the heart rate alone, sleep can be noninvasively assessed with any heart rate meter. This method will make it easier to determine sleep stages and diagnose sleep disorders.

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

PRIMARY OBJECTIVE

The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality.

DESIGN AND METHODS

Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions.

RESULTS

Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%.

CONCLUSIONS

HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.

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

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.

Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals

Judiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.

Spurious cross-frequency amplitude-amplitude coupling in nonstationary, nonlinear signals

Recent studies of brain activities show that cross-frequency coupling (CFC) play an important role in memory and learning. Many measures have been proposed to investigate the CFC phenomenon, including the correlation between the amplitude envelopes of two brain waves at different frequencies - cross-frequency amplitude-amplitude coupling (AAC). In this short communication, we describe how nonstationary, nonlinear oscillatory signals may produce spurious cross-frequency AAC. Utilizing the empirical mode decomposition, we also propose a new method for assessment of AAC that can potentially reduce the effects of nonlinearity and nonstatonarity and, thus, help to avoid the detection of artificial AACs. We compare the performances of this new method and the traditional Fourier-based AAC method. We also discuss the strategies to identify potential spurious AACs.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

Paradoxical response to an emotional task: trait characteristics and heart-rate dynamics

The present study evaluated the heart-rate dynamics of subjects reporting decreased (responders) or paradoxically increased relaxation (nonresponders) at the end of a threatening movie. Heart-rate dynamics were characterized by indices extracted through recurrence quantification analysis (RQA) and detrended fluctuation analysis (DFA). These indices were studied as a function of a few individual characteristics: hypnotizability, gender, absorption, anxiety, and the activity of the behavioral inhibition and activation systems (BIS/BAS). Results showed that (a) the subjective experience of responsiveness is associated with the activity of the behavioral inhibition system and (b) a few RQA and DFA indices are able to capture the influence of cognitive-emotional traits, including hypnotizability, on the responsiveness to the threatening task.

Prolonged menstruation and increased menstrual blood with generalized δ electroencephalogram power: A case report

Estradiol changes associated with the menstrual cycle have a great impact on brain activation. δ frequency mainly appears during normal sleep status or brain injury diseases, including encephalitis and mental confusion. The current case report presents a 51-year-old female with prolonged menstruation and increased menstrual blood volume whose electroencephalogram (EEG) recording demonstrated a rare generalized 3 Hz δ frequency band in the waking status. The patient had been suffering from heart palpitations and dizziness for 6 months and was receiving treatment in the Department of Neurology (Second Xiangya Hospital). The individual had been experiencing prolonged menstruation and increased menstrual blood volume for 6 years. Gynecologial examination revealed secondary anemia and hysteromyoma. Hemoglobin levels were decreased to 69 g/l. Physical and neurological examinations, and computed tomography results appeared normal. The EEG recording indicated a generalized 3 Hz δ frequency band with 30–80 μV power and a long-range δ frequency band when the patient was hyperventilating. The prolonged menstruation and increased menstrual blood volume may have induced the generalized δ frequency without brain injury. To the best of our knowledge, this is the first formal case report of prolonged menstruation and increased menstrual blood volume with the abnormality of δ EEG power.