RR Time Series Research Articles

GRAPHICAL METHODS FOR NON-LINEAR ANALYSIS OF ELECTROCARDIOGRAPHIC DATA

INTRODUCTION: One of the most widely used methods for studying the bioelectric activity of the heart is the electrocardiogram (ECG). An important diagnostic parameter that can be determined by the ECG is heart rate variability (HRV), which takes into account the difference between successive strokes of the heart. Changing HRV can be an indicator of a number of disease states, such as low HRV levels can show poor health that is not only associated with cardiovascular disease but also with other diseases such as internal, nervous, mental, and other disorders.
 OBJECTIVES: The subject of this article is the study of 24-hour ECG signals by applying non-linear graphical methods for HRV analysis. The non-linear graphical methods aim at obtaining graphical and quantitative information on the cardiovascular status of the study groups to complement the information obtained from traditional linear methods of analysis.
 METHODS: For the non-linear analysis of HRV, graphical methods were used: Poincaré plot and Recurrence plot were used, which are suitable for the examination of electrocardiographic signals. Two groups of people were investigated: 20 healthy controls and 20 patients with arrhythmia.
 RESULTS: Based on the nonlinear analysis of RR time series, the graphs of a healthy subject and a patient with arrhythmia were constructed using the Poincare plot. The graph of the healthy subject has the shape of a comet, while the graph of the patient with arrhythmia has the shape of a fan. The quantitative characteristics of patients with arrhythmia significantly change compared to the healthy subjects. The SD1 (p <0.003) and SD2 (p <0.0001) values decreased in patients with arrhythmia compared to the healthy controls. This reduction leads to reduction of the areas of the ellipse in the patients with arryhthmia. The ratio of SD1/SD2 (p <0.05) is lower for the healthy controls. The graphs obtained by the Recurrence plot of the investigated signals differ in healthy subjects and in patients with arrhythmia. For a healthy subject, the graph has a diagonal line and fewer squares showing a higher HRV. The graph of a patient with arrhythmia contains more squares, indicating periodicity in the investigated signal. The Recurrence Quantification Analysis showed that the values of the investigated parameters DET% (p <0.0001), REC% (p <0.0001) and ENTR (p <0.001) in patients with arrhythmia are increased.
 CONCLUSIONS: The importance of the graphical nonlinear methods used for the analysis of HRV consists in forming a parametric and graphical assessment of the patient's health status.

Fast time series detrending with applications to heart rate variability analysis

In this paper, we discuss a new fast detrending method for the nonstationary RR time series used in Heart Rate Variability (HRV) analysis. The described method is based on the diffusion equation, and we show numerically that it is equivalent to the widely used Smoothing Priors Approach (SPA) and Wavelet Smoothing Approach (WSA) methods. The speed of the proposed method is comparable to the WSA method and it is several orders of magnitude faster than the SPA method, which makes it suitable for very long time series analysis.

Diet-Induced Metabolic Syndrome Reduced Heart Rate Variability and Increased Irregularity and Complexity of Short-Term RR Time Series in Rabbits.

Simple SummaryIn recent years, obesity and metabolic syndrome (MetS) have become more prevalent, owing to increased unhealthy habits and sedentary lifestyles becoming public health problems. Both conditions are linked with a higher prevalence of sudden cardiac death (SCD), but the exact mechanisms are not known. An autonomic nervous system imbalance can produce atrial and ventricular arrhythmias, which cause SCD, and this can be quantified by analyzing heart rate variability (HRV). We investigated HRV using time-domain, frequency-domain and nonlinear analyses during the development of MetS in rabbits and found HRV modifications that could be associated with the higher prevalence of SCD in this pathological condition.Metabolic syndrome (MetS) has been linked to a higher prevalence of sudden cardiac death (SCD), but the mechanisms are not well understood. One possible underlying mechanism may be an abnormal modulation of autonomic activity, which can be quantified by analyzing heart rate variability (HRV). Our aim was to investigate the modifications of short-term HRV in an experimental rabbit model during the time-course of MetS development. NZW rabbits were randomly assigned to a control (n = 10) or a MetS group (n = 13), fed 28 weeks with control or high-fat, high-sucrose diets. After anesthesia, a 15-min ECG recording was acquired before diet administration and at weeks 14 and 28. We analyzed short RR time series using time-domain, frequency-domain and nonlinear analyses. A mixed-model factorial ANOVA was used for statistical analysis. Time-domain analysis showed a 52.4% decrease in the standard deviation of heart rate in animals from the MetS group at week 28, but no changes in the rest of parameters. In the frequency domain, we found a 9.7% decrease in the very low frequency and a 380.0% increase of the low frequency bands in MetS animals at week 28, whereas high frequency remained unchanged. Nonlinear analyses showed increased complexity and irregularity of the RR time series in MetS animals.

Mutual information between heart rate variability and respiration for emotion characterization

Objective: Interest in emotion recognition has increased in recent years as a useful tool for diagnosing psycho-neural illnesses. In this study, the auto-mutual and the cross-mutual information function, AMIF and CMIF respectively, are used for human emotion recognition. Approach: The AMIF technique was applied to heart rate variability (HRV) signals to study complex interdependencies, and the CMIF technique was considered to quantify the complex coupling between HRV and respiratory signals. Both algorithms were adapted to short-term RR time series. Traditional band pass filtering was applied to the RR series at low frequency (LF) and high frequency (HF) bands, and a respiration-based filter bandwidth was also investigated (). Both the AMIF and the CMIF algorithms were calculated with regard to different time scales as specific complexity measures. The ability of the parameters derived from the AMIF and the CMIF to discriminate emotions was evaluated on a database of video-induced emotion elicitation. Five elicited states i.e. relax (neutral), joy (positive valence), as well as fear, sadness and anger (negative valences) were considered. Main results: The results revealed that the AMIF applied to the RR time series filtered in the band was able to discriminate between the following: relax and joy and fear, joy and each negative valence conditions, and finally fear and sadness and anger, all with a statistical significance level p -value 0.05, sensitivity, specificity and accuracy higher than 70% and area under the receiver operating characteristic curve index AUC 0.70. Furthermore, the parameters derived from the AMIF and the CMIF allowed the low signal complexity presented during fear to be characterized in front of any of the studied elicited states. Significance: Based on these results, human emotion manifested in the HRV and respiratory signal responses could be characterized by means of the information-content complexity.

Modifications of short-term intrinsic pacemaker variability in diet-induced metabolic syndrome: a study on isolated rabbit heart.

Metabolic syndrome (MetS) describes a condition associated with multiple diseases concomitantly such as diabetes, hypertension, obesity, and dyslipidemia. It has been linked with higher prevalence of cardiovascular disease, atrial fibrillation, and sudden cardiac death. One of the underlying mechanisms could be altered automaticity, which would reflect modifications of sinus node activity. These phenomena can be evaluated analyzing the components of heart rate variability (HRV). Our aim was to examine the modifications of sinus node variability in an isolated heart model of diet-induced obesity and MetS. Male NZW rabbits were randomly assigned to high-fat (HF, n = 8), control (HF-C, n = 7), high-fat, high-sucrose (HFHS, n = 9), and control (HFHS-C, n = 9) groups, fed with their respective diets during 18/28weeks. After euthanasia, their hearts were isolated in a Langendorff system. We recorded 10-15min of spontaneous activity. Short RR time series were analyzed, and standard HRV parameters were determined. One-way ANOVA, Kruskal-Wallis test, and bivariate correlation were used for statistical analysis (p < 0.05). We did find an increase in the complexity and irregularity of intrinsic pacemaker activity as shown by modifications of approximate entropy, sample entropy, minimum multiscale entropy, and complexity index in HFHS animals. Even though no differences were found in standard time and frequency-domain analyses, spectral heterogeneity increased in HFHS group. Animal weight and glucose intolerance were highly correlated with the modifications of intrinsic pacemaker variability. Finally, modifications of intrinsic HRV seemed to be reliant on the number of components of MetS present, given that only HFHS group showed significant changes towards an increased complexity and irregularity of intrinsic pacemaker variability.

Detection of Fetal ECG R Wave From Single-Lead Abdominal ECG Using a Combination of RR Time-Series Smoothing and Template-Matching Approach

Fetal electrocardiograph (fECG) R-wave detection from maternal abdominal ECGs (aECGs) is challenging as it is easily distorted and often overwhelmed by a variety of unavoidable interference signals. In this study, we combined RR time-series smoothing with a template-matching (TM) approach to detect fECG R waves from a single-lead aECG. According to the quasi-periodicity of the R wave, we analyzed the cases of misaligned and missed R wave detection in the RR time series and developed an amendment algorithm to distinguish and correct them. The fECG R wave detection consists of two stages. First, an fECG R wave, which did not overlap with the maternal ECG QRS complex (mQRS), was detected. Subsequently, the amendment algorithm corrected the misaligned and missed R waves and predicted the approximate region of the fECG R wave that overlapped with the mQRS. A TM algorithm was implemented to detect the fECG R wave that overlapped with the mQRS in the approximate region. The fECG QRS complex (fQRS) and mQRS templates were built from the detected fQRS and mQRS that did not overlap with the fQRS, respectively, using singular value decomposition. The TM algorithm then determined the accurate location when the most optimal correlation occurred between the actual mQRS and the superposition of the mQRS and fQRS templates in the approximate region. The performance of the proposed method was assessed using F1 measure and Bland-Altman analysis. High accuracy values were obtained corresponding to 95% and -0.04±9.00 ms, respectively. The proposed method was therefore considered to enhance accurate extraction of the fECG R wave in both the clinical and commercial applications.

Enhancing Detection Accuracy for Clinical Heart Failure Utilizing Pulse Transit Time Variability and Machine Learning

Physiological signal variability can offer important insight into cardiovascular activity and clinical cardiovascular diseases. Heart rate variability (HRV) and pulse transit time variability (PTTV) are two important time series variabilities. However, combining HRV and PTTV can enhance the classification accuracy for heart failure which is unknown. In this paper, a simultaneous analysis of HRV and PTTV performed on both normal subjects and heart failure patients, was carried out, aiming to investigate the improvement of HRV-based heart failure detection with the assistant of PTTV analysis. Forty normal subjects and forty heart failure patients were enrolled. Standard limb lead-II electrocardiogram and radial artery pressure waveforms were synchronously recorded. HRV and PTTV analysis were performed on the acquired RR and PTT time series using the standard time- (MEAN, SDNN, and RMSSD), frequency- (LF, HF, and LF/HF), and non-linear (SD1, SD2, sample entropy, and fuzzy measure entropy) domain indices. The results showed that all HRV indices except MEAN (P = 0.1) and LF/HF (P = 0.9) showed significant differences (all P <0.01) between the two group, while only MEAN in PTTV significantly decreases in heart failure patients (P < 0.01). Moreover, when combined the HRV, PTTV indices, and the predicted probabilities generated from the distance distribution matrix-based convolutional neural network models, the highest classification performances were achieved by a support vector machine classifier, outputting a sensitivity of 0.93, a specificity of 0.88, and an accuracy of 0.90. This paper demonstrated the potential of PTTV analysis for the detection of clinical heart failure.

Higuchi Fractal Dimension of Heart Rate Variability During Percutaneous Auricular Vagus Nerve Stimulation in Healthy and Diabetic Subjects.

Analysis of heart rate variability (HRV) can be applied to assess the autonomic nervous system (ANS) sympathetic and parasympathetic activity. Since living systems are non-linear, evaluation of ANS activity is difficult by means of linear methods. We propose to apply the Higuchi fractal dimension (HFD) method for assessment of ANS activity. HFD measures complexity of the HRV signal. We analyzed 45 RR time series of 84 min duration each from nine healthy and five diabetic subjects with clinically confirmed long-term diabetes mellitus type II and with diabetic foot ulcer lasting more than 6 weeks. Based on HRV time series complexity analysis we have shown that HFD: (1) discriminates healthy subjects from patients with diabetes mellitus type II; (2) assesses the impact of percutaneous auricular vagus nerve stimulation (pVNS) on ANS activity in normal and diabetic conditions. Thus, HFD may be used during pVNS treatment, to provide stimulation feedback for on-line regulation of therapy in a fast and robust way.

Validity of the Polar V800 monitor for measuring heart rate variability in mountain running route conditions.

This study was conducted to test, in mountain running route conditions, the accuracy of the Polar V800™ monitor as a suitable device for monitoring the heart rate variability (HRV) of runners. Eighteen healthy subjects ran a route that included a range of running slopes such as those encountered in trail and ultra-trail races. The comparative study of a V800 and a Holter SEER 12 ECG Recorder™ included the analysis of RR time series and short-term HRV analysis. A correction algorithm was designed to obtain the corrected Polar RR intervals. Six 5-min segments related to different running slopes were considered for each subject. The correlation between corrected V800 RR intervals and Holter RR intervals was very high (r = 0.99, p < 0.001), and the bias was less than 1ms. The limits of agreement (LoA) obtained for SDNN and RMSSD were (- 0.25 to 0.32ms) and (- 0.90 to 1.08ms), respectively. The effect size (ES) obtained in the time domain HRV parameters was considered small (ES < 0.2). Frequency domain HRV parameters did not differ (p > 0.05) and were well correlated (r ≥ 0.96, p < 0.001). Narrow limits of agreement, high correlations and small effect size suggest that the Polar V800 is a valid tool for the analysis of heart rate variability in athletes while running high endurance events such as marathon, trail, and ultra-trail races.

Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework

Sleep is a physiological activity and human body restores itself from various diseases during sleep. It is necessary to get sufficient amount of sleep to have sound physiological and mental health. Nowadays, due to our present hectic lifestyle, the amount of sound sleep is reduced. It is very difficult to decipher the various stages of sleep manually. Hence, an automated system may be useful to detect the different stages of sleep. This paper presents a novel method for the classification of sleep stages based on RR-time series and electroencephalogram (EEG) signal. The method uses iterative filtering (IF) based multiresolution analysis approach for the decomposition of RR-time series into intrinsic mode functions (IMFs). The delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) waves are evaluated from EEG signal using band-pass filtering. The recurrence quantification analysis (RQA) and dispersion entropy (DE) based features are evaluated from the IMFs of RR-time series. The dispersion entropy and the variance features are evaluated from the different bands of EEG signal. The RR-time series features and the EEG features coupled with the deep neural network (DNN) are used for the classification of sleep stages. The simulation results demonstrate that our proposed method has achieved an average accuracy of 85.51%, 94.03% and 95.71% for the classification of ‘sleep vs wake’, ‘light sleep vs deep sleep’ and ‘rapid eye movement (REM) vs non-rapid eye movement (NREM)’ sleep stages.

Fetal heart rate extraction from abdominal electrocardiograms through multivariate empirical mode decomposition

Assessment of fetal heart rate (FHR) and fetal heart rate variability (fHRV) reveals important information about fetal well-being, specifically in high risk pregnancies. Abdominal electrocardiogram (abdECG) recording is a non-invasive method to capture fetal electrocardiograms. In this paper, we propose a methodology to extract FHR (fetal RR time series) from the abdECG recordings using the recently introduced multivariate empirical mode decomposition (MEMD) technique. MEMD breaks a signal into a finite set of intrinsic mode functions (IMFs). First, elimination of the noisier abdECG channels, based on comparison of similar indexed IMFs that were obtained through the MEMD technique, is conducted. Thereafter, denoising of the remaining abdECG channels is performed by eliminating certain similar indexed IMFs. The unwanted mother QRS complexes are removed from these noise-free abdECG channels, and the candidate fetal R-peaks are detected through a wavelet based approach. The proposed methodology is validated using an open source real-life clinical database. The proposed technique resulted in a high value (0.983) of cross correlation between the detected and true FHR signals.

A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL

Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.

Accuracy of the Garmin 920 XT HRM to perform HRV analysis.

Heart rate variability (HRV) analysis is widely used to investigate autonomous cardiac drive. This method requires periodogram measurement, which can be obtained by an electrocardiogram (ECG) or from a heart rate monitor (HRM), e.g. the Garmin 920 XT device. The purpose of this investigation was to assess the accuracy of RR time series measurements from a Garmin 920 XT HRM as compared to a standard ECG, and to verify whether the measurements thus obtained are suitable for HRV analysis. RR time series were collected simultaneously with an ECG (Powerlab system, AD Instruments, Castell Hill, Australia) and a Garmin XT 920 in 11 healthy subjects during three conditions, namely in the supine position, the standing position and during moderate exercise. In a first step, we compared RR time series obtained with both tools using the Bland and Altman method to obtain the limits of agreement in all three conditions. In a second step, we compared the results of HRV analysis between the ECG RR time series and Garmin 920 XT series. Results show that the accuracy of this system is in accordance with the literature in terms of the limits of agreement. In the supine position, bias was 0.01, - 2.24, + 2.26ms; in the standing position, - 0.01, - 3.12, + 3.11ms respectively, and during exercise, - 0.01, - 4.43 and + 4.40ms. Regarding HRV analysis, we did not find any difference for HRV analysis in the supine position, but the standing and exercise conditions both showed small modifications.

Theoretical and experimental comparison of lag-based and time-based exponential moving average models of QT hysteresis

Objective: In the electrocardiogram, adaptation of the QT interval to variations in heart rate is not instantaneous. Quantification of this hysteresis phenomenon relies on mathematical models describing the relation between the RR and QT time series. These models reproduce hysteresis through an effective RR interval computed as a linear combination of the history of past RR intervals. This filter depends on a time constant parameter that may be used as a biomarker. Approach: The most common hysteresis model is based on an autoregressive filter with an impulse response that decreases exponentially with the beat number (lag-based model). Recognizing that the QT time series is unevenly spaced, we propose two exponential moving average filters (time-based models) to define the effective RR interval: one with an impulse response that decreases exponentially with time in seconds, and one with a step response that relaxes exponentially with time in seconds. These two filters are neither linear nor time-invariant. Recurrence formulas are derived to enable efficient implementation. Main results: Application to clinical signals recorded during tilt table test, exercise and 24 h Holter demonstrates that the three models perform similarly in terms of goodness-of-fit. When comparing the hysteresis time constant in two conditions with different heart rates, however, the time-based models are shown to reduce the bias on the hysteresis time constant caused by heart rate acceleration and deceleration. Significance: Time-based models should be considered when intergroup differences in both heart rate and QT hysteresis are expected.

Characterization and classification of patients with different levels of cardiac death risk by using Poincaré plot analysis.

Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

Atrial fibrillation detection on compressed sensed ECG

Objective: Compressive sensing (CS) approaches to electrocardiogram (ECG) analysis provide efficient methods for real time encoding of cardiac activity. In doing so, it is important to assess the downstream effect of the compression on any signal processing and classification algorithms. CS is particularly suitable for low power wearable devices, thanks to its low-complex digital or hardware implementation that directly acquires a compressed version of the signal through random projections. In this work, we evaluate the impact of CS compression on atrial fibrillation (AF) detection accuracy. Approach: We compare schemes with data reconstruction based on wavelet and Gaussian models, followed by a P&T-based identification of beat-to-beat (RR) intervals on the MIT-BIH atrial fibrillation database. A state-of-the-art AF detector is applied to the RR time series and the accuracy of the AF detector is then evaluated under different levels of compression. We also consider a new beat detection procedure which operates directly in the compressed domain, avoiding costly signal reconstruction procedures. Main results: We demonstrate that for compression ratios up to 30 the AF detector applied to RR intervals derived from the compressed signal exhibits results comparable to those achieved when employing a standard QRS detector on the raw uncompressed signals, and exhibits only a 2% accuracy drop at a compression ratio of 60%. We also show that the Gaussian-based reconstruction approach is superior in terms of AF detection accuracy, with a negligible drop in performance at a compression ratio ⩽75%, compared to a wavelet approach, which exhibited a significant drop in accuracy at a compression ratio ⩾65%. Significance: The results suggest that CS should be considered as a plausible methodology for both efficient real time ECG compression (at moderate compression rates) and for offline analysis (at high compression rates).

Consecutive ultra-short-term heart rate variability to track dynamic changes in autonomic nervous system during and after exercise

Objective: Quantitative measurement of the dynamic changes in autonomic nervous system (ANS) during and after exercise has great significance in clinical, sports training and other fields. A consecutive ultra-short-term (30 s, UST) heart rate variability (HRV) method was proposed to track the exercise-induced autonomic control of heart rate (HR). Approach: Twenty-three healthy young men participated in the study. The first four stages of the Modified Bruce Protocol (S0–S3) were performed. Six HRV indices, i.e. HF (power of high frequency ranged from 0.15 to 0.4 Hz), LF (power of low frequency ranged from 0.04 to 0.15 Hz), LF/HF, SD1 and SD2 of Poincaré plot, and SD2/SD1, over 30 s were calculated every 5 s over 3 min RR time series during, as well as after, exercise. Main results: The results showed that during exercise, SD1, SD2, HF and LF dropped down quickly and tended to stabilize. Particularly, SD1 and HF showed a slight upward trend in the lower three stages while the declining time of SD2 in S3 lasted longer than the other stages. SD2/SD1 increased rapidly first and then decreased slowly. The values of SD2/SD1 in S3 remained higher than those in the other stages. After exercise, SD1, SD2, HF and LF kept increasing first and then declined slowly or fluctuated with decaying amplitudes. SD2/SD1 increased initially, then decreased and fluctuated slightly. Significance: Compared with the indices in frequency domain, the Poincaré indices were more sensitive and accurate in UST measurement of ANS during exercise. The results demonstrated that the UST method could characterize the dynamic changing tendency of ANS during and after exercise and quantify the differences of changes in ANS induced by exercise with different intensities. In particular, the vagal branch functioned dominantly in controlling HR in S0 but the effect of the sympathetic branch on HR enhanced with the increase of exercise intensity. In addition, the transient changes of ANS related with the sudden onset of exercise could also be reflected, despite perhaps being limited by the computation window width to some extent. Thus, the consecutive UST Poincaré indices could provide a feasible and simple method to measure quantitatively the exercise-induced dynamic changes in ANS.

Heartbeat monitoring from adaptively down-sampled electrocardiogram

Background and ObjectiveHeartbeats Holter monitoring is important for the detection of arrhythmias and possible anomalies, which are predictive of cardiovascular risks and infections. Reducing the number of acquired samples is useful to save energy and memory, but a proper down-sampling schedule is needed to record all useful information. MethodAn adaptive algorithm for the non-uniform down-sampling of data is used to reduce the mean sampling frequency of ECG data. The acquired data are processed to extract RR rhythm and to classify the heartbeats among a set of possible types of arrhythmias. ResultsThe proposed method is tested in terms of the ability to estimate the heart rate and to classify the heartbeats from the MIT-BIH Arrhythmia data down-sampled below the Nyquist limit. The mean accuracy in identifying the heartbeats was over the 98% and the RMS error in estimating the RR time series was lower than the 1%. Variability, spectral and complexity indexes extracted from RR series were estimated with a mean error that was lower than 10%. Classification accuracy was above the 95%. ConclusionsAn adaptive method to down-sample ECG data is discussed. It can be useful to save energy and to reduce memory occupation, while still preserving important information on the heartbeats.

Influence of 2nd-degree AV blocks, ECG recording length, and recording time on heart rate variability analyses in horses

ObjectivesTo assess the influence of 2nd-degree AV blocks (AVB) on RR interval-based heart rate variability (HRV) variables; to investigate the effect of using PP interval time series and of artifact filtering on HRV analyses; to investigate the influence of electrocardiogram (ECG) recording length and time of recording; and to calculate day-to-day variability and reference intervals of HRV variables. AnimalsThirty healthy adult horses. MethodsRR and PP interval time series were extracted from 10-h Holter ECGs and an automated filter was applied to the RR time series (RRf). Time-domain HRV variables were calculated based on RR, PP, and RRf time series and their relation to the number of AVBs was assessed. Hourly 10- and 60-min segments were extracted to investigate the influence of segment length and recording time on HRV variables. Day-to-day variability and reference intervals of HRV variables were calculated. ResultsVariables of short-term HRV were significantly influenced by the number of AVBs when based on RR, but not when based on PP- and RRf time series. PP- and RRf-based HRV variables were in good agreement. The majority of HRV variables were influenced by recording time and ECG segment length. Day-to-day variability of HRV variables was low when based on 10-h ECG recordings but moderate to high when based on 60-min and 10-min recordings. ConclusionsSecond-degree AVBs significantly influence conventional RR-based, but not PP- and RRf-based time-domain HRV variables. However, PP and RRf analyses have limitations and recording length and time of recording must be considered.

Changes in linear and nonlinear measures of RR and QT interval series after beer intake

Background/Aim. There are only several studies on the acute effect of alcoholic drinks intake on heart rhythm and this phenomenon is still not well understood. We wanted to examine whether linear and nonlinear measures of RR interval and QT interval series could quantify the effect of beer in healthy subjects. Methods. Eighteen young volunteers drank 500 mL of beer (21 g of ethanol). Electrocardiogram (ECG) recordings were taken in supine position: 20 minutes before (relaxation) and 60 minutes after drink intake. The RR interval series and the QT interval series were extracted from ECG and we calculated short-term (?1) and long-term (?2) scaling exponents and sample entropy (SampEn) for both series; low frequency (LF) and high frequency (HF) spectral components from RR interval series and QT variability (QTV). Blood pressure was measured every 10 minutes. Results. It was shown that beer induced changes in variability and correlation properties of these series. Immediate effect of beer intake was detected as a transient increase in the QT variability, heart rate and blood pressure. Delayed effects of beer were shortening of the RR and QT intervals and reduction of the HF spectral component. Beer intake also increased short-term scaling exponent (?1) of the RR time series and long-term scaling exponent (?2) of the QT time series. Conclusion. Our results suggest that acute effects of beer are reduced parasympathetic control of the heart and changed dynamic complexity of the ventricular repolarization.