Correlation of sleep EEG frequency bands and Heart Rate Variability

Weak closed-loop vibrational stimulation improves the depth of slow-wave sleep and declarative memory consolidation

Sleep is a unique behavioral state that affects body functions and memory. Although previous studies suggested stimulation methods to enhance sleep, a new method is required that is practical for long-term and unconstrained use by people. In this study, we used a novel closed-loop vibration stimulation method that delivers a stimulus in interaction with the intrinsic heart rhythm and examined the effects of stimulation on sleep and memory. Twelve volunteers participated in the experiment and each underwent one adaptation night and two experimental conditions-a stimulation condition (STIM) and a no-stimulation condition (SHAM). The heart rate variability analysis showed a significant increase in the normalized high frequency and the normalized low frequency significantly decreased under the STIM during the slow-wave sleep (SWS) stage. Furthermore, the synchronization ratio between the heartbeat and the stimulus significantly increased under the STIM in the SWS stage. From the electroencephalogram (EEG) spectral analysis, EEG relative powers of slow-wave activity and theta frequency bands showed a significant increase during the STIM in the SWS stage. Additionally, memory retention significantly increased under the STIM compared to the SHAM. These findings suggest that the closed-loop stimulation improves the SWS-stage depth and memory retention, and further provides a new technique for sleep enhancement.

A 259.6 μW HRV-EEG Processor With Nonlinear Chaotic Analysis During Mental Tasks

A system-on-chip (SoC) with nonlinear chaotic analysis (NCA) is presented for mental task monitoring. The proposed processor treats both heart rate variability (HRV) and electroencephalography (EEG). An independent component analysis (ICA) accelerator decreases the error of HRV extraction from 5.94% to 1.84% in the preprocessing step. Largest Lyapunov exponents (LLE), as well as linear features such as mean and standard variation and sub-band power, are calculated with NCA acceleration. Measurements with mental task protocols result in confidence level of 95%. Thanks to the hardware acceleration, the chaos-processor fabricated in 0.13 μm CMOS technology consumes only 259.6 μW.

Intravenous laser blood irradiation, interstitial laser acupuncture, and electroacupuncture in an animal experimental setting: preliminary results from heart rate variability and electrocorticographic recordings

This is the first study to investigate intravenous (i.v.) laser blood irradiation, interstitial (i.st.) laser acupuncture, and electroacupuncture (EA) in combination with heart rate variability (HRV) and electrocorticogram. We investigated 10 male anesthetized Sprague-Dawley rats under the three conditions mentioned previously in Beijing, China, and data analysis was performed in Graz, Europe. For i.v. laser stimulation in the femoral vein and i.st. laser acupuncture at Neiguan (PC6), we used a European system (Modulas needle, Schwa-Medico, Germany; 658 nm, 50 mW, continuous wave mode), and for EA at Neiguan, a Chinese system (Hanshi-100A; Nanjing Jisheng Medical Technology Company, China; 15 Hz, 1 mA). HR, HRV, and electrocorticogram were recorded using a biophysical amplifier AVB-10 (Nihon-Kohden, Japan). HR changed significantly during i.st. laser acupuncture stimulation of Neiguan in anesthetized rats. Total HRV increased insignificantly during i.v. and i.st. laser stimulation. The LF/HF ratio showed significant changes only during i.v. laser blood irradiation. Integrated cortical EEG (electrocorticogram) decreased insignificantly during EA and i.v. laser blood irradiation. Further studies concerning dosage-dependent alterations are in progress.

On determining available stochastic features by spectral splitting in obstructive sleep apnea detection

Heart rate variability (HRV) is one of the promising directions for a simple and noninvasive way for obstructive sleep apnea syndrome detection. The time-frequency representations has been proposed before to investigate the non-stationary properties of the HRV during either transient physiological or pathological episodes. Within the framework of the filter-banked feature extraction, estimation of the spectral splitting for stochastic features extraction is an open issue. Usually, this splitting is fixed empirically without taking into account the actual informative distribution of time-frequency representations. In the present work, a relevance-based approach that aims to find a priori a boundaries in the frequency domain for the spectral splitting upon t-f planes is proposed. Results show that the approach is able to find the most informative frequency bands, achieving accuracy rate over 75%.

Training using short-time features for OSA discrimination

Heart rate variability (HRV) is one of the promising directions for a simple and noninvasive way for obstructive sleep apnea syndrome detection (OSA). The interaction between the sympathetic and parasympathetic systems on the HRV recordings, gives rise to several non-stationary components added to the signal. Aiming to improve the classifier accuracy for obstructive sleep apnoea detection, the use of more appropriated techniques for leading with non-stationarity and mixed dynamics, are needed. This work aims at searching a convenient training strategy of combining the feature set to be further fed in to the classifier, which should take into consideration the different dynamics in the HRV signal. Therefore, a set of the short-time features, extracted from a given HRV time-varying decomposition, and selected by spectral splitting is considered. Additionally, three methods of projection are used: none, simple, and multivariate. Finally, the different approaches are tested and compared, using k-nn and support vector machines (SVM) classifiers. Attained results show that using continuous wavelet transform with short-time features and multivariate projection, followed by a SVM classifier, allow to obtain a suitable OSA detection.

Heart Rate Variability, Homeostasis, and Brain Function

Measures of heart rate variability (HRV) are major indices of the sympathovagal balance in cardiovascular research. These measures are thought to reflect complex patterns of brain activation as well and HRV is now emerging as a descriptor thought to provide information on the nervous system organization of homeostatic responses in accordance with the situational requirements. Current models of integration equate HRV to the affective states as parallel outputs of the central autonomic network, with HRV reflecting its organization of affective, physiological, “cognitive,” and behavioral elements into a homeostatic response. Clinical application is in the study of patients with psychiatric disorders, traumatic brain injury, impaired emotion-specific processing, personality, and communication disorders. HRV responses to highly emotional sensory inputs have been identified in subjects in vegetative state and in healthy or brain injured subjects processing complex sensory stimuli. In this respect, HRV measurements can provide additional information on the brain functional setup in the severely brain damaged and would provide researchers with a suitable approach in the absence of conscious behavior or whenever complex experimental conditions and data collection are impracticable, as it is the case, for example, in intensive care units.

Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification

Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). Using both statistical analysis and Gaussian discriminative modelling approaches, this paper presents a pilot study of assessing the cross-correlation between EEG frequency bands and heart rate variability (HRV) in normal and sleep apnoea clinical patients. For the study we used EEG (delta, theta, alpha, sigma and beta) and HRV (LF(nu), HF(nu) and LF/HF) features from the spectral analysis. The statistical analysis in different sleep stages highlighted that in sleep apnoea patients, the EEG delta, sigma and beta bands exhibited a strong correlation with HRV features. Then the correlation between EEG frequency bands and HRV features were examined for sleep apnoea classification using univariate and multivariate Gaussian models (UGs and MGs). The MG outperformed the UG in the classification. When EEG and HRV features were combined and modelled with MG, we achieved 64% correct classification accuracy, which is 2 or 8% improvement with respect to using only EEG or ECG features. When delta and acceleration coefficients of the EEG features were incorporated, then the overall accuracy improved to 71%.