Amplitude-aware Permutation Entropy Research Articles

Medium–Long-Term PV Output Forecasting Based on the Graph Attention Network with Amplitude-Aware Permutation Entropy

Medium–long-term photovoltaic (PV) output forecasting is of great significance to power grid planning, power market transactions, power dispatching operations, equipment maintenance and overhaul. However, PV output fluctuates greatly due to weather changes. Furthermore, it is frequently challenging to ensure the accuracy of forecasts for medium–long-term forecasting involving a long time span. In response to the above problems, this paper proposes a medium–long-term forecasting method for PV output based on amplitude-aware permutation entropy component reconstruction and the graph attention network. Firstly, the PV output sequence data are decomposed by ensemble empirical mode decomposition (EEMD), and the decomposed intrinsic mode function (IMF) subsequences are combined and reconstructed according to the amplitude-aware permutation entropy. Secondly, the graph node feature sequence is constructed from the reconstructed subsequences, and the mutual information of the node feature sequence is calculated to obtain the graph node adjacency matrix which is applied to generate a graph sequence. Thirdly, the graph attention network is utilized to forecast the graph sequence and separate the PV output forecasting results. Finally, an actual measurement system is used to experimentally verify the proposed method, and the outcomes indicate that the proposed method, which has certain promotion value, can improve the accuracy of medium–long-term forecasting of PV output.

Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches

Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

A multi-factor combined traffic flow prediction model with secondary decomposition and improved entropy weight method

Traffic flow (TF) prediction is vital in traffic systems. Aiming at the effect of noise on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an improved CEEMDAN based on amplitude-aware permutation entropy (AAPE), named ICEEMDAN, is proposed. Aiming at the parameter selection problems on variational mode decomposition (VMD), VMD improved by pelican optimization algorithm (POA) is proposed, called PVMD. Aiming at the parameter selection problems of bidirectional long short-term memory (BiLSTM) and kernel extreme learning machine (KELM), BiLSTM improved by sea-horse optimizer (SHO), named SHOBiLSTM, and KELM improved by nutcracker optimizer algorithm (NOA), named NOAKELM are proposed, respectively. Since the chaotic characteristics of TF, multi-factor combined TF prediction model with ICEEMDAN, PVMD, SHOBiLSTM, NOAKELM, improved entropy weight method is proposed. First, use ICEEMDAN to decompose TF and get intrinsic mode functions (IMFs). Second, refined composite multiscale dispersion entropy (RCMDE) is used to divide IMFs into the fuzzy and detail components, fuzzy components are secondarily decomposed by PVMD, IMFs and factors are multi-factor predicted by SHOBiLSTM and NOAKELM. Finally, improved entropy weight method is proposed to weight SHOBiLSTM and NOAKELM results. TF of the UK highway proves the superiority. Experiments indicate that the proposed model is better than other nine models with RMSE, MAE, MAPE and R2 of 29.5868, 22.0431, 0.0701 and 0.9947 for TF I. The outcomes suggest this proposed model precedes nine models at 99% confidence level, providing data basis and theoretical for travel plans and traffic system.

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis.

In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method's advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.

A new underwater acoustic signal denoising method based on modified uniform phase empirical mode decomposition, hierarchical amplitude-aware permutation entropy, and optimized improved wavelet threshold denoising

Underwater acoustic signal (UAS) denoising is base and prerequisite for UAS detection, recognition and classification. In order to perform UAS denoising effectively, a new UAS denoising method based on modified uniform phase empirical mode decomposition (MUPEMD), hierarchical amplitude-aware permutation entropy (HAAPE), and improved wavelet threshold denoising (IWTD) method optimized by sand cat swarm optimization (SCSO) (SIWTD), named MUPEMD-HAAPE-SIWTD, is proposed. Firstly, decompose original signal into a battery of intrinsic mode functions (IMFs) by MUPEMD. Secondly, determine the double threshold by HAAPE to classify signal into pure signal, mixed signal and noisy signal. Then, optimize IWTD by SCSO, so that it can adaptively select the optimal wavelet basis function to achieve the denoising of mixed signal. Finally, the denoised mixed signal is reconstructed with pure signal to obtain ultima denoised signal. The denoising experiments on Lorenz signal and Duffing signal demonstrate that signal-to-noise ratio can be improved by 9 dB–13 dB by the proposed method. The denoising experiments on simulating ship radiated noise signals and four kinds of actual ship radiated noise signals (https://www.aigei.com/, accessed on June 1, 2023) demonstrate that the proposed method makes phase diagram smoother and clearer, and makes the ability of suppressing noise higher.

A novel scheme based on modified hierarchical time-shift multi-scale amplitude-aware permutation entropy for rolling bearing condition assessment and fault recognition

To avoid production interruptions and equipment damage caused by rolling bearing failure, this study presents a novel diagnosis scheme applicable to both condition monitoring and fault recognition. In detail, to fully exploit feature information, a modified Hierarchical Time-Shift Multi-scale Amplitude-Aware Permutation Entropy (MHTSMAAPE) is put forward to map the raw vibration signal into a high-dimensional vector space of features. This method is developed from the AAPE algorithm and integrates the time-shift procedure and modified hierarchical analysis, extracting more feature details while ensuring entropy stability. Afterwards, the averaged value of the feature vector is regarded as an indicator to assess the condition of rolling bearing, this indicator exceeds a specific value indicates the rolling bearing steps into faulty. Then, the Unsupervised Discriminative Feature Selection (UDFS) algorithm is first introduced to directly pick fault-sensitive features from the vector space, relieving the calculation burden of the fault recognition task. The fault identification algorithm is based on Harris Hawks Optimization-Joint Opposite Selection optimized for Support Vector Machine (HHO-JOS-SVM) to improve classification accuracy. Finally, the proposed scheme fused by MHTSMAAPE, UDFS, and HHO-JOS-SVM is validated in two different cases with four common evaluation metrics, respectively. The experimental results demonstrate its reliability and robustness in rolling bearing condition assessment and fault recognition.

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet.

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.

Research on noise reduction method for ship radiate noise based on secondary decomposition

Underwater acoustic signal denoising is of great significance for the accurate identification of underwater targets, the research of military detection equipment and the monitoring of marine ecological environment. In this paper, a new denoising method for ship radiated noise based on combined secondary optimization decomposition model, amplitude-aware permutation entropy, dynamic interval threshold filtering and mutual information is proposed. Compared with one-time decomposition, the proposed method can better restore the waveform characteristics of the original signal and filter out the mixed residual noise. In addition, this paper discoveries the influence of differential operation on phase diagram morphology under high signal-to-noise ratio. The experimental results show that the differential phase diagram can show the weak noise contained in the signal with high signal-to-noise ratio. This new discovery has been applied to the evaluation of noise reduction effect of underwater acoustic signal for the first time.

Cardiovascular Response to Intravenous Glucose Injection during Hemodialysis with Assessment of Entropy Alterations.

The quality of autonomic blood pressure (BP) control can be assessed by the entropy of serial BP data. The aim of this study was to evaluate the effect of hemodialysis (HD) and glucose infusion (GI) on amplitude aware permutation entropy (AAPE) of hemodynamic variables during HD in chronic kidney disease patients with and without type-2 diabetes mellitus (DM). Twenty-one patients without DM (NDO) and ten with DM were studied. Thirty minutes after the start of HD, a 40% glucose solution was administered. Hemodynamic data were extracted from continuous recordings using the Portapres® system. AAPE decreased during HD in all patients and all hemodynamic signals with the exception of AAPE of mean and diastolic BP in DM patients. GI led to an increase in AAPE for cardiac output in all patients, while AAPE for heart rate and ejection time increased only in DM studies, and AAPE for systolic, diastolic, and mean arterial pressure, as well as total peripheral resistance, increased only in NDO patients. The reduction in entropy during HD indicates impaired autonomic control in response to external perturbations. This state is partially reversed by the infusion of glucose with differences in central and peripheral responsiveness in DM and NDO patients.

Texture analysis using two-dimensional permutation entropy and amplitude-aware permutation entropy

Entropy algorithms have been applied extensively for time series analysis. The entropy value given by the algorithm quantifies the irregularity of the data structure. For higher irregular data structures, the entropy is higher. Both permutation entropy (PE) and amplitude-aware permutation entropy (AAPE) have been previously used to analyze time series. These two metrics have the advantage, over others, of being computationally fast and simple. However, fewer entropy measures have been proposed to process images. Two-dimensional entropy algorithms can be used to study texture and analyze the irregular structure of images. Herein, we propose the extension of AAPE for two-dimensional analysis (AAPE2D). To the best of our knowledge, AAPE2D has never been proposed to analyze texture of images. For comparison purposes, we also study the two-dimensional permutation entropy (PE2D) to analyze the effect of the amplitude consideration in texture analysis. In this study, we compare AAPE2D method with PE2D in terms of irregularity discrimination, parameters sensitivity, and artificial texture differentiation. Both AAPE2D and PE2D appear to be interesting entropy-based approaches for image texture analysis. When applied to a biomedical dataset of chest X-rays with healthy subjects and pneumonia patients, both methods showed to statistically differentiate both groups for P<0.01. Finally, using a SVM model and multiscale entropy values as features, AAPE2D achieves an average of 75.7% accuracy which is slightly better than the results of PE2D. Overall, both entropy algorithms are promising and achieve similar conclusions. This work is a new step towards the development of other entropy-based texture measures.

A new hybrid prediction model of PM2.5 concentration based on secondary decomposition and optimized extreme learning machine.

As air pollution worsens, the prediction of PM2.5 concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM2.5 concentration accurately. PM2.5 concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R2 of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R2 of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.

A comprehensive working condition identification scheme for rolling bearings based on modified CEEMDAN as well as modified hierarchical amplitude-aware permutation entropy

As a pivotal part of a machine driven system, the health states of rolling bearings usually determine the normal operation of a whole item of equipment. Consequently, it is important to make accurate and timely judgments as to the operating conditions of rolling bearings. In this paper, a synthesized diagnosis technology, including fault pre-judgment and identification for rolling bearings is proposed. In the first section, a threshold value is defined on the basis of the sensitivity of amplitude-aware permutation entropy (AAPE) to bearing faults. Whether the bearing has defects is judged is based on this value. If a defect exists, a feature extraction scheme combining the modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and the modified hierarchical AAPE (MHAAPE) is adopted, to fully mine the hidden state features. Firstly, the scheme uses MCEEMDAN, which benefits from a good time-frequency decomposition capability, to divide the signal of trouble into a group of intrinsic mode functions (IMFs). Second, the MHAAPE of each IMF component is computed to form the candidate state features. Then, multi cluster feature selection is employed to compress the high-dimensional fault features to form the low-dimensional sensitive feature vectors required for subsequent classification. Finally, the sensitive feature vectors are input into a random forest classifier for training and classification, so as to ascertain the different fault types and severity. In addition, different contrastive methods are tested based on experimental data. The experiment results indicate that, compared to contrastive methods, the proposed scheme enjoys better performance, which can effectively judge whether the bearing is healthy and accurately identify different fault states in bearings.

Hierarchical Amplitude-Aware Permutation Entropy-Based Fault Feature Extraction Method for Rolling Bearings.

In order to detect the incipient fault of rolling bearings and to effectively identify fault characteristics, based on amplitude-aware permutation entropy (AAPE), an enhanced method named hierarchical amplitude-aware permutation entropy (HAAPE) is proposed in this paper to solve complex time series in a new dynamic change analysis. Firstly, hierarchical analysis and AAPE are combined to excavate multilevel fault information, both low-frequency and high-frequency components of the abnormal bearing vibration signal. Secondly, from the experimental analysis, it is found that HAAPE is sensitive to the early failure of rolling bearings, which makes it suitable to evaluate the performance degradation of a bearing in its run-to-failure life cycle. Finally, a fault feature selection strategy based on HAAPE is put forward to select the bearing fault characteristics after the application of the least common multiple in singular value decomposition (LCM-SVD) method to the fault vibration signal. Moreover, several other entropy-based methods are also introduced for a comparative analysis of the experimental data, and the results demonstrate that HAAPE can extract fault features more effectively and with a higher accuracy.

Decreased Resting-State Functional Complexity in Elderly with Subjective Cognitive Decline.

Individuals with subjective cognitive decline (SCD) are at high risk of developing preclinical or clinical state of Alzheimer’s disease (AD). Resting state functional magnetic resonance imaging, which can indirectly reflect neuron activities by measuring the blood-oxygen-level-dependent (BOLD) signals, is promising in the early detection of SCD. This study aimed to explore whether the nonlinear complexity of BOLD signals can describe the subtle differences between SCD and normal aging, and uncover the underlying neuropsychological implications of these differences. In particular, we introduce amplitude-aware permutation entropy (AAPE) as the novel measure of brain entropy to characterize the complexity in BOLD signals in each brain region of the Brainnetome atlas. Our results demonstrate that AAPE can reflect the subtle differences between both groups, and the SCD group presented significantly decreased complexities in subregions of the superior temporal gyrus, the inferior parietal lobule, the postcentral gyrus, and the insular gyrus. Moreover, the results further reveal that lower complexity in SCD may correspond to poorer cognitive performance or even subtle cognitive impairment. Our findings demonstrated the effectiveness and sensitiveness of the novel brain entropy measured by AAPE, which may serve as the potential neuroimaging marker for exploring the subtle changes in SCD.

Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs.

Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent (Hur) and amplitude-aware permutation entropy (AAPE) features were extracted from the EEG dataset. k-nearest neighbors (kNN) and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs. The main novelty of this paper is twofold: first, to investigate Hur as a complexity feature and AAPE as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA) and then integrating these features to propose a new CompEn hybrid feature fusion method towards developing the novel WT_CompEn gender recognition framework as a core for an automated gender recognition model to be sensitive for identifying gender roles in the brain-emotion relationship for females and males. The results illustrated the effectiveness of Hur and AAPE features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed WT_CompEn framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel WT_CompEn may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel WT_CompEn framework is a crucial goal for improving the process of automatic gender recognition from emotional-based EEG signals allowing for more comprehensive insights to understand various gender differences and human behavior effects of an intervention on the brain.

An integrated health condition detection method for rolling bearings using time-shift multi-scale amplitude-aware permutation entropy and uniform phase empirical mode decomposition

Rolling bearings usually works in the condition with high speed, high temperature and high pressure, so their faults need to be detected and judged in time. This paper presents a new multi-scale entropy measurement algorithm called time-shift multi-scale amplitude-aware permutation entropy (TSMAAPE), combining time-shift coarse-grained method and multi-scale amplitude-aware permutation entropy (MAAPE) algorithm together creatively, making the extraction of useful information in time series more sufficient. In this paper, this new entropy algorithm is combined with uniform phase empirical mode decomposition algorithm, improved max-relevance and min-redundancy (ImRMR) algorithm and random forest algorithm, which are used to implement signal preprocessing, select features for dimensionality reduction, execute fault recognition and classification respectively, forming a new comprehensive detection method of bearing fault. The new comprehensive method includes two crucial parts: fault pre-detection and fault identification. Firstly, the threshold set based on TSMAAPE method is used to judge whether the bearing is in fault or not, if there is a fault, the subsequent steps are performed to identify and classify the fault. Compared with other traditional feature extraction methods, the proposed method is more effective and robust. The experimental results of two groups of bearing data show that the average accuracy of this method can reach 98.6%. The comprehensive detection method is of great significance for the rapid and accurate detection and identification of bearing faults in practical engineering applications.

Flow rate measurement of oil-gas-water wavy flow through a combined electrical and ultrasonic sensor

Water-based dispersed wavy flow, whereby the water with dispersed oil droplets moves along the bottom of the pipe and the gas concurrently on the top, is a frequently encountered horizontal oil-gas-water three-phase flow pattern in petroleum production and transportation. Accurate flow metering of each individual phase is significant to academic research and industrial flow assurance. Therefore, we present a combined electrical and ultrasonic sensor to simultaneously and non-intrusively acquire online flowing information. This information includes: the water fraction estimated by the normalized voltage from a conductance sensor, the gas fraction calculated by locating the gas-liquid interface through a pulse-wave ultrasonic sensor consisting of three transducers, and the coupled velocity of oil droplets and gas-liquid interface from Doppler shift signal acquired by a continuous wave ultrasonic Doppler sensor. Based on the limited flowing information obtained by multimodality sensors, a novel theoretical model is established based on the analysis of the momentum balance between different phases to solve phase fractions and phase actual velocities for calculating phase flow rates. To make the model closed and solvable, the interfacial velocity and oil velocity are respectively extracted by decoupling the Doppler shift signal through amplitude-aware permutation entropy and noise assisted multivariate empirical mode decomposition methods. Finally, the phase flow rates are calculated by the solutions of the established model. Dynamic experiments are performed to verify the effectiveness and accuracy of the proposed method and theoretical model. This study could provide a potential solution for non-intrusive phase flow metering of industrial three phase flow.

An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

The impact of total sleep deprivation upon supine and head up tilt hemodynamics using non-linear analysis in firefighters

PurposeThe impact of 31 h of total sleep deprivation (TSD) on cardiovascular autonomic modulation was evaluated in 46 volunteers all professional fire brigade units officers (all men, mean age = 32.11 ± 6.4). MethodsHemodynamic examination was obtained at baseline and after 31 h of Total Sleep Deprivation (TSD). Each measurement period included supine and head-up tilt (HUT) test response. Continuous beat-to-beat systolic blood pressure (sBP), diastolic blood pressure (dBP) and mean blood pressure (mBP), heart rate (HR), stroke volume (SV), left ventricular ejection time (LVET) and cardiac output (CO) were calculated in rest supine position and in response to HUT. Nonlinear analyses were conducted using Permutation Entropy (PE), Amplitude Aware Permutation Entropy (AAPE) and Fractal Dimension (FD). ResultsSupine increase of HR and decrease in RRI, LVET and entropy of LVET were observed after 31-h of TSD. Increase in entropy of mBP was indicated by AAPE and PE methods. Response to HUT, values of spectral analysis of blood pressure (normalized units of high frequency of sBP and dBP) was altered. Permutation entropy of mBP in response to HUT were significantly changed after 31-h of TSD. ConclusionsTSD alters autonomic nervous system modulation of cardiac and vascular functioning while supine and in response to orthostatic stress. TSD resulted in changes in cardiac and both spectral and two methods of analysis of entropy of blood pressure when supine. Values of spectral analysis of blood pressure and permutation entropy of mean blood pressure in response to HUT were significantly changed after 31 h of TSD.

TdP Incidence in Methoxamine-Sensitized Rabbit Model Is Reduced With Age but Not Influenced by Hypercholesterolemia.

Metabolic syndrome is associated with hypercholesterolemia, cardiac remodeling, and increased susceptibility to ventricular arrhythmias. Effects of diet-induced hypercholesterolemia on susceptibility to torsades de pointes arrhythmias (TdP) together with potential indicators of arrhythmic risk were investigated in three experimental groups of Carlsson’s rabbit model: (1) young rabbits (YC, young control, age 12–16 weeks), older rabbits (AC, adult control, age 20–24 weeks), and older age-matched cholesterol-fed rabbits (CH, cholesterol, age 20–24 weeks). TdP was induced by α-adrenergic stimulation by methoxamine and IKr block in 83% of YC rabbits, 18% of AC rabbits, and 21% of CH rabbits. High incidence of TdP was associated with high incidence of single (SEB) and multiple ectopic beats (MEB), but the QTc prolongation and short-term variability (STV) were similar in all three groups. In TdP-susceptible rabbits, STV was significantly higher compared with arrhythmia-free rabbits but not with rabbits with other than TdP arrhythmias (SEB, MEB). Amplitude-aware permutation entropy analysis of baseline ECG could identify arrhythmia-resistant animals with high sensitivity and specificity. The data indicate that the TdP susceptibility in methoxamine-sensitized rabbits is affected by the age of rabbits but probably not by hypercholesterolemia. Entropy analysis could potentially stratify the arrhythmic risk and identify the low-risk individuals.