Entropy Analysis of Heart Rate Variability in Different Sleep Stages

Modulatory effects of low-intensity retinal ultrasound stimulation on rapid and non-rapid eye movement sleep

Prior investigations have established that the manipulation of neural activity has the potential to influence both rapid eye movement and non-rapid eye movement sleep. Low-intensity retinal ultrasound stimulation has shown effectiveness in the modulation of neural activity. Nevertheless, the specific effects of retinal ultrasound stimulation on rapid eye movement and non-rapid eye movement sleep, as well as its potential to enhance overall sleep quality, remain to be elucidated. Here, we found that: In healthy mice, retinal ultrasound stimulation: (i) reduced total sleep time and non-rapid eye movement sleep ratio; (ii) changed relative power and sample entropy of the delta (0.5-4 Hz) in non-rapid eye movement sleep; and (iii) enhanced relative power of the theta (4-8 Hz) and reduced theta-gamma coupling strength in rapid eye movement sleep. In Alzheimer's disease mice with sleep disturbances, retinal ultrasound stimulation: (i) reduced the total sleep time; (ii) altered the relative power of the gamma band during rapid eye movement sleep; and (iii) enhanced the coupling strength of delta-gamma in non-rapid eye movement sleep and weakened the coupling strength of theta-fast gamma. The results indicate that retinal ultrasound stimulation can modulate rapid eye movement and non-rapid eye movement-related neural activity; however, it is not beneficial to the sleep quality of healthy and Alzheimer's disease mice.

Heart Rate Variability Analysis of Healthy Individuals and Patients with Ischemia and Arrhythmia

This article presents the results of a study of the cardiac activity of patients diagnosed with arrhythmia and ischemic heart disease. The obtained results were compared with the results obtained from a healthy control group. The studies were conducted on long-term cardiac recordings (approximately 24 h) registered by means of Holter monitoring, and the observations were made in the daily activities of the individuals. All processing, analysis and evaluations on the registered signals were performed by means of an established information demonstration cardiology system. The mathematical analysis included linear, non-linear and graphical methods for estimating and analyzing heart rate variability (HRV). Re-examinations were carried out on some of the observed individuals after six months of treatment. The results show an increase in the main time domain parameters of the HRV, such as the SDNN (from 86.36 ms to 95.47 ms), SDANN (from 74.05 ms to 82.14 ms), RMSSD (from 5.1 ms to 6.92 ms), SDNN index (from 52.4 to 58.91) and HRVTi (from 12.8 to 16.83) in patients with ischemia. In patients with arrhythmia, there were increases in the SDNN (from 88.4 ms to 96.44 ms), SDANN (from 79.12 ms to 83.23 ms), RMSSD (from 6.74 ms to 7.31 ms), SDNN index (from 53.22 to 59.46) and HRVTi (from 16.2 to 19.42). An increase in the non-linear parameter α (from 0.83 to 0.85) was found in arrhythmia; and in α (from 0.80 to 0.83), α1 (from 0.88 to 0.91) and α2 (from 0.86 to 0.89) in ischemia. The presented information system can serve as an auxiliary tool in the diagnosis and treatment of cardiovascular diseases.

Non-Monotonic Complexity of Stochastic Model of the Channel Gating Dynamics

The simple model of an ionic current flowing through a single channel in a biological membrane is used to depict the complexity of the corresponding empirical data underlying different internal constraints and thermal fluctuations. The residence times of the channel in the open and closed states are drawn from the exponential distributions to mimic the characteristics of the real channel system. In the selected state, the dynamics are modeled by the overdamped Brownian particle moving in the quadratic potential. The simulated data allow us to directly track the effects of temperature (signal-to-noise ratio) and the channel's energetic landscape for conformational changes on the ionic currents' complexity, which are hardly controllable in the experimental case. To accurately describe the randomness, we employed four quantifiers, i.e., Shannon, spectral, sample, and slope entropies. We have found that the Shannon entropy predicts the anticipated reaction to the imposed modification of randomness by raising the temperature (an increase of entropy) or strengthening the localization (reduction of entropy). Other complexity quantifiers behave unpredictably, sometimes resulting in non-monotonic behaviour. Thus, their applicability in the analysis of the experimental time series of single-channel currents can be limited.

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device

In clinical conditions, polysomnography (PSG) is regarded as the "golden standard" for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into "good sleep" and "poor sleep" based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F₁-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring.

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.

Modulation effect of low-intensity transcranial ultrasound stimulation on REM and NREM sleep

Previous studies have shown that modulating neural activity can affect rapid eye movement (REM) and non-rapid eye movement (NREM) sleep. Low-intensity transcranial ultrasound stimulation (TUS) can effectively modulate neural activity. However, the modulation effect of TUS on REM and NREM sleep is still unclear. In this study, we used ultrasound to stimulate motor cortex and hippocampus, respectively, and found the following: (i) In healthy mice, TUS increased the NREM sleep ratio and decreased the REM sleep ratio, and altered the relative power and sample entropy of the delta band and spindle in NREM sleep and that of the theta and gamma bands in REM sleep. (ii) In sleep-deprived mice, TUS decreased the ratio of REM sleep or the relative power of the theta band during REM sleep. (iii) In sleep-disordered Alzheimer’s disease (AD) mice, TUS increased the total sleep time and the ratio of NREM sleep and modulated the relative power and the sample entropy of the delta and spindle bands during NREM and that of the theta band during REM sleep. These results demonstrated that TUS can effectively modulate REM and NREM sleep and that modulation effect depends on the sleep state of the samples, and can improve sleep in sleep-disordered AD mice.

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection

Recently, tools developed for detecting human activities have been quite prominent in contributing to health issue prevention and long-term healthcare. For this occasion, the current study aimed to evaluate the performance of eye-movement complexity features (from multi-scale entropy analysis) compared to eye-movement conventional features (from basic statistical measurements) on detecting daily computer activities, comprising reading an English scientific paper, watching an English movie-trailer video, and typing English sentences. A total of 150 students participated in these computer activities. The participants’ eye movements were captured using a desktop eye-tracker (GP3 HD Gazepoint™ Canada) while performing the experimental tasks. The collected eye-movement data were then processed to obtain 56 conventional and 550 complexity features of eye movement. A statistic test, analysis of variance (ANOVA), was performed to screen these features, which resulted in 45 conventional and 379 complexity features. These eye-movement features with four combinations were used to build 12 AI models using Support Vector Machine, Decision Tree, and Random Forest (RF). The comparisons of the models showed the superiority of complexity features (85.34% of accuracy) compared to conventional features (66.98% of accuracy). Furthermore, screening eye-movement features using ANOVA enhances 2.29% of recognition accuracy. This study proves the superiority of eye-movement complexity features.