Analysis Of Heart Rate Variability Signal Research Articles

Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network.

Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.

How to realize the effect of Kundalini yoga and Chinese Chi meditation on the HRV and ANS with GAN architecture? ‘HRV-GAN’: An alternative approach

The alternative approach highlighted in our study ameliorates the process of analyzing the effect on cardiac dynamics due to the practice of Chinese Chi meditation and Kundalini yoga by the implementation of ‘HRV-GAN'. It works by augmenting the dataset of Heart Rate Variability (HRV) signals by 31 times with the aid of the Generative Adversarial Network (GAN) and after that analyzing them without the need for any other equivalent conversion of the signal. The study highlights the distinct effects of practicing different types of meditation on HRV by augmenting the initial dataset entirely in an automated deep learning approach. The visualized results from the architecture are verified statistically. The dataset chosen for the study comprised just 24 meditative HRV samples in total, which have been successfully augmented to 744 samples for classification. The results indicated that meditation affects cardiac dynamics significantly, and diversion varies with the choice of meditation technique. The results obtained from the proposed semi-supervised deep learning model- 'HRV-GAN' was verified statistically by plotting required histograms, Recurrence Plots (RP), and Recurrence Quantification Analysis (RQA) analysis, which were in agreement with each other. HRV-GAN has achieved a sensitivity of 96.667% and an accuracy of 95.556 %. Our innovation can benefit the medical and biomedical community for research involving automated analysis of HRV signals because 'HRV-GAN' can perform appreciably even with fewer data samples for training a deep learning model.

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

The heart rate variability signal is a valuable tool for cardiovascular system diagnostics. Processing this signal detects arrhythmia during long-term cardiac monitoring. It is also analyzed to recognize abnormalities within the autonomic nervous system. Processing this signal helps in detecting various pathologies, such as atrial fibrillation (AF), supraventricular tachycardia (SVT), and congestive heart failure (CHF). As a beneficial alternative to the commonly used HRV spectrum analysis, quadratic time–frequency analysis of HRV signals could be helpful in heart pathology detection. Indeed, in this study, we have created a client-server paradigm deployed as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This platform detects arrhythmia in real-time by deploying time–frequency analysis, feature extraction, feature selection, and classification of Heart Rate Variability (HRV) signals. We gathered all these functionalities in a Graphical User Interface (GUI) in addition to data acquisition. As a client, a Raspberry Pi Zero ensures data acquisition and connects to a server over TCP/IP that involves a 4G/3G connection encrypted through the transport layer security (TLS). This telemedical tool continuously monitors the heart rate variability. In the case of an alarm, medical professionals may immediately interact with their patients in the hospital or at home.

HUMAN MODULATED HEART RATE VARIABILITY SIGNAL IN SPORTS USING MULTIFRACTAL ANALYSIS

The purpose is to apply the multifractal analysis method to the research of human physiology in the process of sports. The mass index spectrum is used for multifractal analysis of heart rate variability signal based on the heart rate variability signal analysis theory and fractal theory. Finally, 10 healthy college students are selected as the experimental subjects. RAC-3003 portable electronic measuring instrument is used to collect heart rate signals in different exercise stages. Finally, the data are analyzed by [Formula: see text] and Lo-[Formula: see text] analysis methods. The results show that the lowest value of ln[Formula: see text] is 3.1 and the highest value is 7.0 in different stages in the morning, and the lowest value is 3.3 and the highest value is 7.5 in different stages in the afternoon. The average value of random signal ln[Formula: see text] gradually increases from 2.6 to 3.7; whether in the morning or in the afternoon, the average Hurst exponent during exercise is lower than that before and after exercise, and the average Hurst exponent after exercise is slightly higher than that before exercise; the long-range correlation index of heart rate variability signal in each exercise stage first increases and then decreases, and the changes of short-range correlation index and long-range correlation index are opposite; the average of fitting intercept of [Formula: see text] curve is lower than that of Lo-[Formula: see text] curve in the first and third stages; the fractal coefficient of the original data in the first and third stages of exercise is significantly higher than that in the second stage, which indicates that the overall fractal degree of heart rate variability signal before and after exercise is higher.

Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods

Objective. The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls. Approach. The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method. Main results. The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, −17.5%; univariate AR, −18.3%; bivariate AR, −23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing. Significance. Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia.

In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.

Stratification of High-Risk Hypertensive Patients Using Hybrid Heart Rate Variability Features and Boosting Algorithms

Hypertension is a global challenge to the public health which can easily lead to life-threatening vascular diseases unless control measures are adopted. Considering the prevalence of vascular diseases and their fatality, early detection of high-risk patients is an important problem in the present world. Heart rate variability (HRV) analysis can be an effective prognostic tool to identify the characteristics of vulnerable patients, considering its reliability in predicting sudden cardiac deaths. However, challenge lies in identifying tenuous differences in HRV between the low-risk and high-risk patients at the early stage. With this motivation, we propose a hybrid approach based on dual-tree complex wavelet packet transform (DTCWPT) and linear time domain as well as nonlinear analysis of HRV signal to extract multitudinous features. A key issue before the HRV analysis of such patients is the presence of marked amount of ectopic beats, which is addressed by using time-varying auto regressive (TVAR) technique. The features extracted from TVAR edited HRV signals are shortlisted by minimum redundancy maximum relevance algorithm for an efficient classifier modeling. Furthermore in this study, we propose to use cost-sensitive RUSBoost (CS-RUSBoost) algorithm for handling the class imbalance problem of the data. A comparative performance evaluation of CS-RUSBoost with RUSBoost, SMOTEBoost, asymmetric AdaBoost algorithm shows a superior result by CS-RUSBoost with G-mean of 0.9352 and F1 score of 0.9347.

Modified Distribution Entropy as a Complexity Measure of Heart Rate Variability (HRV) Signal.

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (), in the context of HRV signal analysis. We thereby propose modified distribution entropy () to remove the physiological discrepancy involved in the computation of . The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “” a computationally inexpensive and physiologically more relevant complexity measure in comparison to .

A novel modular fetal ECG STAN and HRV analysis: Towards robust hypoxia detection.

This paper introduces a comprehensive fetal Electrocardiogram (fECG) Signal Extraction and Analysis Virtual Instrument that integrates various methods for detecting the R-R Intervals (RRIs) as a means to determine the fetal Heart Rate (fHR) and therefore facilitates fetal Heart Rate Variability (HRV) signal analysis. Moreover, it offers the capability to perform advanced morphological fECG signal analysis called ST segment Analysis (STAN) as it seamlessly allows the determination of the T-wave to QRS complex ratio (also called T/QRS) in the fECG signal. The integration of these signal processing and analytical modules could help clinical researchers and practitioners to noninvasively monitor and detect the life threatening hypoxic conditions that may arise in different stages of pregnancy and more importantly during delivery and could therefore lead to the reduction of unnecessary C-sections. In our experiments we used real recordings from a Fetal Scalp Electrode (FSE) as well as maternal abdominal electrodes. This Virtual Instrument (Toolbox) not only serves as a desirable platform for comparing various fECG extraction signal processing methods, it also provides an effective means to perform STAN and HRV signal analysis based on proven ECG morphological as well as Autonomic Nervous System (ANS) indices to detect hypoxic conditions.

A novel risk stratification model for patients with brain metastasis: Incorporating with physiological signals.

38 Background: Brain metastasis (BM) develops in 10%-40% of patients with malignancies. Survival expectancy is critical for choosing an optimal treatment for BM patients. Previous reports have proposed different risk stratification systems based on clinical and image-related factors. Heart rate variability (HRV), a physiological factor, has also been reported as a novel prognosticator for BM patients. In this study, we proposed a novel risk stratification model for BM patients by incorporating HRV signals. Methods: Patients with BM referred to our department for palliative RT were prospective recruited. Those who were not suitable for HRV evaluation were excluded for analysis. A 5-minute ECG was acquired before treatment started for each patient for subsequent HRV signal analysis. All patients underwent WBRT for 30-37.5 Gy in 10-15 daily fractions. Local boost to gross intracranial tumors were performed selectively. The primary end-point for this study was overall survival (OS). Survival analysis was carried out by Kaplan-Meier method and factors were compared by log-rank test and cox-regression. A novel risk stratification model combining HRV index SDNN and KPS was proposed. Results: Total 62 patients were recruited from Jan 2010. The median follow-up time was 40.7 months for patients who were still alive. Only 1 patient (1.6%) met the criterion of RPA class I. The median OS for all patients was 4.5 months. Univariate and multivariate analysis revealed that SDNN ≥ 10ms and KPS ≥ 70 were independent survival prognosticators. For a selected group of patients (n = 29, Group 1, SDNN ≥ 10ms and KPS ≥ 70) the median OS was 9.9 months, which is significantly higher than the rest 33 patients (Group 2) as of 2.4 months. The 3-month/6-month OS rate in Group 1 and Group 2 were 82.8%/72.4% and 36.4%/24.2% (p < 0.001). Conclusions: Using physiological factors for risk stratification is novel. Our results demonstrated that a group of patients with potential longer survival could be selected by using HRV index and KPS. This group of patients should be considered differently in treatment selection. Further studies are warranted to validate our findings and improve the quality of care in patients with BM.

Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis.

Heart Rate Variability (HRV) signal analysis provides a quantitative marker of the Autonomic Nervous System (ANS) function. A wristband-type wireless photoplethysmographic (PPG) device was custom-designed to collect and analyze the arterial pulse in the wrist. The proposed device is comprised of an optical sensor to monitor arterial pulse, a signal conditioning unit to filter and amplify the analog PPG signal, a microcontroller to digitize the analog PPG signal, and a Bluetooth module to transfer the data to a smart device. This paper proposes a novel model to represent the PPG signal as the summation of two Gaussian functions. The paper concludes with a verification procedure for HRV signal analysis during sedentary activities.

Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society.

Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes.

Ectopic beats detection and correction methods: A review

The present paper is concerned with a review and evaluation of different methods that are intended to detect and correct ectopic beats. Ectopic beats are abnormal beats that are due to unusual impulses. These abnormal excitations originate from atrio-ventricular junction or ventricles rather than the sino-atrial node. Ectopic beats can be seen in the ECG signal as abnormal waveforms. Their presence can extremely affect the heart rate variability (HRV) measures as they cause ambiguities. Thus, they must be detected and corrected before any HRV signal analysis. Indeed, ectopic beats have a remarkable influence on the time domain, frequency domain and nonlinear domain measurements of the HRV.In fact, too many efforts have been devoted to detect and correct the ectopic beats presence leading to the development of many methods and algorithms.In this article, we shed light on the different methods existing in the research domain literature; that are intended to be used in ectopic beats detection and correction. First, the different methods used in the ectopic beat detection are reviewed. Then, those dedicated to correct their presence are discussed. Finally, the effect of ectopic beats and the ectopic beat correction methods on the HRV signal parameters are highlighted.

Heart Rate Variability and Cardio-respiratory Coupling During Sleep in Patients Prior to Bariatric Surgery

Obesity is associated with increased cardiac risk of morbidly and mortality and for the development and progression of obstructive sleep apnea (OSA). Severity of obesity negatively affects the heart rate variability (HRV) in patients with indication for bariatric surgery (BS). The purpose of this study is to determine if the severity of obesity alters the autonomic cardiac regulation and the cardio-respiratory coupling during sleep using spectral analysis of HRV and respiration variability signals (RS) in patients prior to BS. Twenty-nine consecutive preoperative BS and ten subjects (controls) underwent polysomnography. The spectral and cross-spectral parameters of the HRV and RS were computed during different sleep stages (SS). Spectral analysis of the HRV and RV indicated lower respiration regularity during sleep and a lower HRV in obese patients (OP) during all SS when compared with controls (p < 0.05). Severely (SO) and super-obese patients (SOP) presented lower values of low frequency/high frequency (LF/HF) ratio and LF power during REM sleep and higher HF power (p < 0.05), while morbidly obese (MO) patients presented lower LF/HF ratio and LF power in SS-S2 and higher HF power when compared to controls (p < 0.05). The cross-spectral parameters showed that SOP presented lower percentage of tachogram power coherent with respiration in SS-S3 when compared to controls (p < 0.05). Patients prior to BS presented altered HRV and RV in all SS. SO, MO, and SOP presented altered cardio-respiratory coupling during sleep, and these alterations are related with severity of obesity and OSA parameters.

CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

Spectral analysis of photoplethysmographic signals: The importance of preprocessing

Heart rate variability (HRV) is an important and useful index to assess the responses of the autonomic nervous system (ANS). HRV analysis is performed using electrocardiography (ECG) or photoplethysmography (PPG) signals which are typically subject to noise and trends. Therefore, the elimination of these undesired conditions is very important to achieve reliable ANS activation results. The purpose of this study was to analyze and compare the effects of preprocessing on the spectral analysis of HRV signals obtained from PPG waveform. Preprocessing consists of two stages: filtering and detrending. The performance of linear Butterworth filter is compared with nonlinear weighted Myriad filter. After filtering, two different approaches, one based on least squares fitting and another on smoothness priors, were used to remove trends from the HRV signal. The results of two filtering and detrending methods were compared for spectral analysis accomplished using periodogram, Welch's periodogram and Burg's method. The performance of these methods is presented graphically and the importance of preprocessing clarified by comparing the results. Although both filters have almost the same performance in the results, the smoothness prior detrending approach was found more successful in removing trends that usually appear in the low frequency bands of PPG signals. In conclusion, the results showed that trends in PPG signals are altered during spectral analysis and must be removed prior to HRV analysis.

Sign series entropy analysis of short-term heart rate variability

Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physiological essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis (SSEA) is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.

Point-Counterpoint Comments

POINT-COUNTERPOINTPoint-Counterpoint CommentsDirk CysarzDirk CysarzPublished Online:01 Apr 2007https://doi.org/10.1152/japplphysiol.01446.2006This is the final version - click for previous versionMoreSectionsPDF (25 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations The following letter is in response to the Point:Counterpoint “Cardiovascular variability is/is not an index of autonomic control of circulation” that appeared in the August 2006 issue (vol 101: 676–682, 2006; http://jap.physiology.org/cgi/content/full/101/2/676).To the Editor: In principle, the analysis of heart rate variability (HRV) via spectral analysis offers the ability to quantify the amount of the modulation of both branches of the autonomic nervous system (4). Obviously, the physiological interpretation of the results of HRV analysis is more precise if the confounding factors are minimized. Hence, the experimental conditions need different methodological (e.g., steady-state conditions imposed by the spectral analysis) as well as physiological constraints (e.g., control of respiration). Such restrictions are often difficult to realize. Furthermore, the extrapolation of results obtained under restricted conditions to everyday conditions (such as, e.g., obtained from ambulatory Holter recordings) is limited. To overcome these shortcomings different methodological improvements have been proposed, e.g., refinements of the spectral analysis (3).Interestingly, the physiological interpretation of cardiovascular variability is essentially based on linear methods, especially spectral analysis. The oscillatory model seems to be adequate to quantify the modulations of the two branches of the autonomic nervous system. However, the various limitations of this model show its shortcomings and, hence, call for complementary approaches. The physiological interpretation of most nonlinear methods (e.g., approximate entropy) is still limited because the mathematical formalism underlying such methods cannot be transformed easily into physiological models of the autonomic nervous system. Nevertheless, there are other methods that probably could be interpreted in terms of physiology more easily. One example is the analysis of symbolic dynamics reflecting acceleration and deceleration of instantaneous heart rate. Such dynamics may be relatively easy to interpret and also supply additional information (1, 2). The refinement of the physiological interpretation of cardiovascular variability is not only a matter of constraints imposed by accepted methods but also depends on the potential physiological interpretation based on linear and nonlinear methods.REFERENCES1 Bettermann H, Amponsah D, Cysarz D, Van Leeuwen P. Musical rhythms in heart period dynamics—a cross-cultural and interdisciplinary approach to cardiac rhythms. Am J Physiol Heart Circ Physiol 277: H1762–H1770, 1999.Link | ISI | Google Scholar2 Cysarz D, Lange S, Matthiessen PF, Van Leeuwen P. Regular heartbeat dynamics are associated with cardiac health. Am J Physiol Regul Integr Comp Physiol 292: R368–R372, 2007.Link | ISI | Google Scholar3 Mateo J, Laguna P. Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model. IEEE Trans Biomed Eng 47: 985–996, 2000.Crossref | PubMed | ISI | Google Scholar4 Parati G, Mancia G, Rienzo MD, Castiglioni P. Point:Counterpoint: Cardiovascular variability is/is not an index of autonomic control of circulation. J Appl Physiol 101: 676–678, 2006.Link | ISI | Google Scholar Download PDF Previous Back to Top Next FiguresReferencesRelatedInformation Cited ByDifferent equestrian sports horses’ reactivity to startleJournal of Veterinary Behavior, Vol. 57 More from this issue > Volume 102Issue 4April 2007Pages 1716-1716 Copyright & PermissionsCopyright © 2007 the American Physiological Societyhttps://doi.org/10.1152/japplphysiol.01446.2006PubMed17194728History Published online 1 April 2007 Published in print 1 April 2007 Metrics

Dynamical complexity detection in short-term physiological series using base-scale entropy

Physiological systems generate complex fluctuations in their output signals that reflect the underlying dynamics. The base-scale entropy method was proposed as a complexity measure to investigate the complexity of time series. The advantages of this method are simplicity and extremely fast calculation for very short data sets. This method enables analyzing very short, nonstationary, and noisy data sets. We employed this method for short-term physiological time series for analysis of heart-rate variability signals. The results show that the simple and easily calculated measure can effectively detect the complexity dissimilarity of physiological time series in different physiological or pathological states, which is convenient for clinical applications.