Ectopic beats detection and correction methods: A review

Impact of QRS misclassifications on heart-rate-variability parameters (results from the CARLA cohort study)

BACKGROUND

Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies. The aim of this study was therefore to evaluate MEANS for its ability to detect non-sinus ECG beats and artifacts and to compare HRV parameters in relation to ECG processing. Additionally, we analyzed how ECG processing affects the statistical association of HRV with cardiovascular disease (CVD) risk factors.

METHODS

20-min ECGs from 1,674 subjects of the population-based CARLA study were available for HRV analysis. All ECGs were processed with the ECG computer program MEANS. A reference standard was established by experienced clinicians who visually inspected the MEANS-processed ECGs and reclassified beats if necessary. HRV parameters were calculated for 5-minute segments selected from the original 20-minute ECG. The effects of misclassified typified normal beats on i) HRV calculation and ii) the associations of CVD risk factors (sex, age, diabetes, myocardial infarction) with HRV were modeled using linear regression.

RESULTS

Compared to the reference standard, MEANS correctly classified 99% of all beats. The averaged sensitivity of MEANS across all ECGs to detect non-sinus beats was 76% [95% CI: 74.1;78.5], but for supraventricular extrasystoles detection sensitivity dropped to 38% [95% CI: 36.8;38.5]. Time-domain parameters were less affected by false sinus beats than frequency parameters. Compared to the reference standard, MEANS resulted in a higher SDNN on average (mean absolute difference 1.4ms [95% CI: 1.0;1.7], relative 4.9%). Other HRV parameters were also overestimated as well (between 6.5 and 29%). The effect estimates for the association of CVD risk factors with HRV did not differ between the editing methods.

CONCLUSION

We have shown that the use of the automated MEANS algorithm may lead to an overestimation of HRV due to the misclassification of non-sinus beats, especially in frequency domain parameters. However, in population-based studies, this has no effect on the observed associations of HRV with risk factors, and therefore an automated ECG analyzing algorithm as MEANS can be recommended here for the determination of HRV parameters.

Exploring Autonomic Alterations during Seizures in Temporal Lobe Epilepsy: Insights from a Heart-Rate Variability Analysis

Epilepsy's impact on cardiovascular function and autonomic regulation, including heart-rate variability, is complex and may contribute to sudden unexpected death in epilepsy (SUDEP). Lateralization of autonomic control in the brain remains the subject of debate; nevertheless, ultra-short-term heart-rate variability (HRV) analysis is a useful tool for understanding the pathophysiology of autonomic dysfunction in epilepsy patients. A retrospective study reviewed medical records of patients with temporal lobe epilepsy who underwent presurgical evaluations. Data from 75 patients were analyzed and HRV indices were extracted from electrocardiogram recordings of preictal, ictal, and postictal intervals. Various HRV indices were calculated, including time domain, frequency domain, and nonlinear indices, to assess autonomic function during different seizure intervals. The study found significant differences in HRV indices based on hemispheric laterality, language dominancy, hippocampal atrophy, amygdala enlargement, sustained theta activity, and seizure frequency. HRV indices such as the root mean square of successive differences between heartbeats, pNN50, normalized low-frequency, normalized high-frequency, and the low-frequency/high-frequency ratio exhibited significant differences during the ictal period. Language dominancy, hippocampal atrophy, amygdala enlargement, and sustained theta activity were also found to affect HRV. Seizure frequency was correlated with HRV indices, suggesting a potential relationship with the risk of SUDEP.

Evaluation of premature ventricular complexes during in-hospital ECG monitoring as a predictor of ventricular tachycardia in an intensive care unit cohort

In-hospital electrocardiographic (ECG) monitors are typically configured to alarm for premature ventricular complexes (PVCs) due to the potential association of PVCs with ventricular tachycardia (VT). However, no contemporary hospital-based studies have examined the association of PVCs with VT. Hence, the benefit of PVC monitoring in hospitalized patients is largely unknown. This secondary analysis used a large PVC alarm data set to determine whether PVCs identified during continuous ECG monitoring were associated with VT, in-hospital cardiac arrest (IHCA), and/or death in a cohort of adult intensive care unit patients. Six PVC types were examined (i.e., isolated, bigeminy, trigeminy, couplets, R-on-T, and run PVCs) and were compared between patients with and without VT, IHCA, and/or death. Of 445 patients, 48 (10.8%) had VT; 11 (2.5%) had IHCA; and 49 (11%) died. Isolated and run PVC counts were higher in the VT group (p = 0.03 both), but group differences were not seen for the other four PVC types. The regression models showed no significant associations between any of the six PVC types and VT or death, although confidence intervals were wide. Due to the small number of cases, we were unable to test for associations between PVCs and IHCA. Our findings suggest that we should question the clinical relevance of activating PVC alarms as a forewarning of VT, and more work should be done with larger sample sizes. A more precise characterization of clinically relevant PVCs that might be associated with VT is warranted.

Atypical Response to Affective Touch in Children with Autism: Multi-Parametric Exploration of the Autonomic System

This study aimed at evaluating the autonomic response to pleasant affective touch in children with Autism Spectrum Disorders (ASD) and age-matched typically developing (TD) peers, thanks to multiple autonomic nervous system (ANS) parameters and by contrasting CT (C-tactile fibers) high- vs. low-density territory stimulations. We measured pupil diameter, skin conductance, and heart rate during gentle stroking of two skin territories (CT high- and low-density, respectively, forearm and palm of the hand) in thirty 6-12-year-old TD children and twenty ASD children. TD children showed an increase in pupil diameter and skin conductance associated with a heart rate deceleration in response to tactile stimulations at the two locations. Only the pupil was influenced by the stimulated location, with a later dilation peak following CT low-density territory stimulation. Globally, ASD children exhibited reduced autonomic responses, as well as different ANS baseline values compared to TD children. These atypical ANS responses to pleasant touch in ASD children were not specific to CT-fiber stimulation. Overall, these results point towards both basal autonomic dysregulation and lower tactile autonomic evoked responses in ASD, possibly reflecting lower arousal and related to social disengagement.

Characterizing cardiac autonomic dynamics of fear learning in humans

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness.

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use.

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10^-5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.

Premature ventricular complexes during continuous electrocardiographic monitoring in the intensive care unit: Occurrence rates and associated patient characteristics

AIMS AND OBJECTIVES

This study examined the occurrence rate of specific types of premature ventricular complex (PVC) alarms and whether patient demographic and/or clinical characteristics were associated with PVC occurrences.

BACKGROUND

Because PVCs can signal myocardial irritability, in-hospital electrocardiographic (ECG) monitors are typically configured to alert nurses when they occur. However, PVC alarms are common and can contribute to alarm fatigue. A better understanding of occurrences of PVCs could help guide alarm management strategies.

DESIGN

A secondary quantitative analysis from an alarm study.

METHODS

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was followed. Seven PVC alarm types (vendor-specific) were described, and included isolated, couplet, bigeminy, trigeminy, run PVC (i.e. VT >2), R-on-T and PVCs/min. Negative binomial and hurdle regression analyses were computed to examine the association of patient demographic and clinical characteristics with each PVC type.

RESULTS

A total of 797,072 PVC alarms (45,271 monitoring hours) occurred in 446 patients, including six who had disproportionately high PVC alarm counts (40% of the total alarms). Isolated PVCs were the most frequent type (81.13%) while R-on-T were the least common (0.29%). Significant predictors associated with higher alarms rates: older age (isolated PVCs, bigeminy and couplets); male sex and presence of PVCs on the 12-lead ECG (isolated PVCs). Hyperkalaemia at ICU admission was associated with a lower R-on-T type PVCs.

CONCLUSIONS

Only a few distinct demographic and clinical characteristics were associated with the occurrence rate of PVC alarms. Further research is warranted to examine whether PVCs were associated with adverse outcomes, which could guide alarm management strategies to reduce unnecessary PVC alarms.

RELEVANCE TO CLINICAL PRACTICE

Targeted alarm strategies, such as turning off certain PVC-type alarms and evaluating alarm trends in the first 24 h of admission in select patients, might add to the current practice of alarm management.

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey-Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.

Group-level physiological synchrony and individual-level anxiety predict positive affective behaviors during a group decision-making task

Joint performance can lead to the synchronization of physiological processes among group members during a shared task. Recently, it has been shown that synchronization is indicative of subjective ratings of group processes and task performance. However, different methods have been used to quantify synchronization, and little is known about the effects of the choice of method and level of analysis (individuals, dyads, or triads) on the results. In this study, participants performed a decision‐making task in groups of three while physiological signals (heart rate and electrodermal activity), positive affective behavior, and personality traits were measured. First, we investigated the effects of different levels of analysis of physiological synchrony on affective behavior. We computed synchrony measures as (a) individual contributions to group synchrony, (b) the average dyadic synchrony within a group, and (c) group‐level synchrony. Second, we assessed the association between physiological synchrony and positive affective behavior. Third, we investigated the moderating effects of trait anxiety and social phobia on behavior. We discovered that the effects of physiological synchrony on positive affective behavior were particularly strong at the group level but nonsignificant at the individual and dyadic levels. Moreover, we found that heart rate and electrodermal synchronization showed opposite effects on group members' display of affective behavior. Finally, trait anxiety moderated the relationship between physiological synchrony and affective behavior, perhaps due to social uncertainty, while social phobia did not have a moderating effect. We discuss these results regarding the role of different physiological signals and task demands during joint action.

Impact Statement

Despite the inherent multilevel structure of groups, little is known regarding how physiological coupling between group members relates to their behaviors during joint group tasks at multiple levels (individual, dyadic, and group). We showed that the relationship between physiological synchrony and smiling/laughing behaviors made by group members were particularly strong at the group level but nonsignificant at the individual and dyadic levels. By using innovative quantification methods—Multidimensional Recurrence Quantification Analysis—we highlight the importance of modeling data in a way that allows for multilevel considerations within groups.

Autonomic Nervous System Maturation and Emotional Coordination in Interactions of Preterm and Full-Term Infants With Their Parents: Protocol for a Multimethod Study

Background

There is limited knowledge on the physiological and behavioral pathways that may affect the developmental outcomes of preterm infants and particularly on the link between autonomic nervous system maturation and early social human behavior. Thus, this study attempts to investigate the way heart rate variability (HRV) parameters are related to emotional coordination in interactions of preterm and full-term infants with their parents in the first year of life and the possible correlation with the developmental outcomes of infants at 18 months.

Objective

The first objective is to investigate the relationship between emotional coordination and HRV in dyadic full-term infant–parent (group 1) and preterm infant–parent (group 2) interactions during the first postpartum year. The second objective is to examine the relationship of emotional coordination and HRV in groups 1 and 2 in the first postpartum year with the developmental outcomes of infants at 18 months. The third objective is to investigate the effect of maternal and paternal postnatal depression on the relation between emotional coordination and HRV in the two groups and on developmental outcomes at 18 months. The fourth objective is to examine the effect of family cohesion and coping on the relation between emotional coordination and HRV in the two groups and on developmental outcomes at 18 months.

Methods

This is an observational, naturalistic, and longitudinal study applying a mixed method design that includes the following: (1) video recordings of mother-infant and father-infant interactions at the hospital, in the neonatal period, and at home at 2, 4, 6, 9, and 12 months of the infants’ life; (2) self-report questionnaires of parents on depressive symptoms, family cohesion, and dyadic coping of stress; (3) infants’ HRV parameters in the neonatal period and at each of the above age points during and after infant-parent video recordings; and (4) assessment of toddlers’ social and cognitive development at 18 months through an observational instrument.

Results

The study protocol has been approved by the Research Ethics Committee of the University of Crete (number/date: 170/September 18, 2020). This work is supported by the Special Account for Research Funds of the University of Crete (grant number: 10792-668/08.02.2021). All mothers (with their partners) of full-term and preterm infants who give birth between March 2021 and January 2022 at the General University Hospital of Crete (northern Crete, Greece) will be invited to participate. The researcher will invite the parents of infants to participate in the study 1 to 2 days after birth. Data collection is expected to be completed by March 2023, and the first results will be published by the end of 2023.

Conclusions

Investigating the regulatory role of HRV and social reciprocity in preterm infants may have implications for both medicine and psychology.

International Registered Report Identifier (IRRID)

PRR1-10.2196/28089

A FEATURE SELECTION-BASED ALGORITHM FOR DETECTION OF ATRIAL FIBRILLATION USING SHORT-TERM ECG

In the presence of premature atrial contraction (PAC), premature ventricular contraction (PVC) or other ectopic beats, RR intervals (RRIs) may be disturbed, which results in other types of heart disease being misdiagnosed as atrial fibrillation (AF). In this study, a low-complexity AF detection method based on short ECG is proposed, which includes RRIs modification and feature selection. The extracted RRIs are used to determine whether the potential RRI interference exists and to modify it. Next, based on the modified RRIs, the features are evaluated and selected by the methods of correlation criterion, Fisher criterion, and minimum redundancy maximum relevance criterion. Finally, filtered features are classified by the artificial neural network (ANN). The algorithm is validated in a test set including 2332 AF, 313 normal (NOR), 239 atrioventricular block (IAVB), 81 left bundle branch block (LBBB), 624 right bundle branch block (RBBB), 426 PAC and 564 PVC. Compared with the previous detection method of AF based on the RRIs, the proposed method achieved an overall sensitivity of 94.04% and an overall specificity of 86.74%. The specificity of the test set containing only AF and NOR is up to 99.04%. Meanwhile, the overall false-positive rate (FPR) of PAC and PVC can be reduced by 9.19%. While ensuring accuracy, this method effectively reduces the probability of misdiagnosis of PVC and PAC as AF. It is an automatic detection method of AF suitable for inter-patient clinical short-term ECG.

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.

Validation of the Polar V800 heart rate monitor and comparison of artifact correction methods among adults with hypertension

Heart rate variability (HRV) measurements via ambulatory monitors have become common. We examined the validity of recording R-R intervals using the Polar V800^™ compared to 12-lead electrocardiograms (ECG) among middle-aged (44.7±10.1years); overweight to obese (29.8±4.3 kg.m^-2) adults (n = 25) with hypertension (132.3±12.2/ 84.3±10.2 mmHg). After resting for 5-min in the supine position, R-R intervals were simultaneously recorded using the Polar V800^™ and the 12-lead ECG. Artifacts present in uncorrected (UN) R-R intervals were corrected with the Kubios HRV Premium (ver. 3.2.) automatic (AC) and threshold-based (TBC) correction, and manual correction (MC) methods. Intra-class correlation coefficients (ICC), Bland-Altman limits of agreement (LoA), and effect sizes (ES) were calculated. We detected 71 errors with the Polar V800^™ for an error rate of 0.85%. The bias (LoAs), ES, and ICC between UN and ECG R-R intervals were 0.69ms (-215.80 to +214.42ms), 0.004, and 0.79, respectively. Correction of artifacts improved the agreeability between the Polar V800^™ and ECG HRV measures. The biases (LoAs) between the AC, TBC, and MC and ECG R-R intervals were 3.79ms (-130.32 to +137.90ms), 1.16ms (-92.67 to +94.98ms), and 0.37ms (-41.20 to +41.94ms), respectively. The ESs of AC, TBC, and MC were 0.024, 0.008, and 0.002, and ICCs were 0.91, 0.95, and 1.00, respectively. R-R intervals measured using the Polar V800^™ compared to 12-lead ECG were comparable in adults with hypertension, especially after the artifacts corrected by MC. However, TBC correction also yielded acceptable results.

Ambulatory Electrocardiographic Monitoring and Ectopic Beat Detection in Conscious Mice

Ambulatory electrocardiography (AECG) is a primary diagnostic tool in patients with potential arrhythmic disorders. To study the pathophysiological mechanisms of arrhythmic disorders, mouse models are widely implemented. The use of a technique similar to AECG for mice is thus of great relevance. We have optimized a protocol which allows qualitative, long-term ECG data recording in conscious, freely moving mice. Automated algorithms were developed to efficiently process the large amount of data and calculate the average heart rate (HR), the mean peak-to-peak interval and heart rate variability (HRV) based on peak detection. Ectopic beats are automatically detected based on aberrant peak intervals. As we have incorporated a multiple lead configuration in our ECG set-up, the nature and origin of the suggested ectopic beats can be analyzed in detail. The protocol and analysis tools presented here are promising tools for studies which require detailed, long-term ECG characterization in mouse models with potential arrhythmic disorders.

Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion

Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20-30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3-9% and other previously proposed predictors of ECV by 15-30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.

Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.

Association between heart rate variability indices and features of spontaneous ventricular tachyarrhythmias in mice

Direct evidence is limited for the association between heart rate variability (HRV) indices and ventricular tachyarrhythmias (VTAs). While galectin-3 (Gal-3) is regarded as a causal factor for cardiac remodelling and a biomarker for arrhythmias, its regulation on VTAs and HVR is unknown. Using aged transgenic (TG) mice with cardiac overexpression of β₂ -adrenoceptors and spontaneous VTAs, we studied whether changes in HRV indices correlated with the severity of VTAs, and whether Gal-3 gene knockout (KO) in TG mice might limit VTA. Body-surface ECG was recorded (10-minute period) in 9- to 10-month-old mice of non-transgenic (nTG), TG and TG × Gal-3 knockout (TG/KO). Time-domain, frequency-domain and nonlinear-domain HRV indices were calculated using the R-R intervals extracted from ECG signals and compared with frequency of VTAs. TG and TG/KO mice developed frequent VTAs and showed significant changes in certain time-domain and nonlinear-domain HRV indices relative to nTG mice. The severity of VTAs in TG and TG/KO mice in combination, estimated by VTA counts and arrhythmia score, was significantly correlated with certain time-domain and nonlinear-domain HRV indices. In conclusion, significant changes in HRV indices were evident and correlated with the severity of spontaneous VTAs in TG mice. The frequency of VTA and HRV indices were largely comparable between TG and TG/KO mice. Deletion of Gal-3 in TG mice altered certain HRV indices implying influence by neuronally localized Gal-3 on autonomic nervous activity.

Smartphone PPG: signal processing, quality assessment, and impact on HRV parameters

Photoplethysmography (PPG) is a simple optical technique used to detect blood volume changes in the micro-vascular bed of tissue in order to track the heartbeat. Smart-phone PPG, performed with the phones camera, has became popular in recent years due to a boom in digital health apps that help people monitor their health parameters. However, many apps struggle with getting readings that are accurate enough to estimate heart rate variability (HRV) one of the most popular biomarkers in the preventive health space. The main obstacle is the multitude of factors that impact PPG results: unique technical characteristics of different smartphone models, frames per second (FPS) rate and the way color is recoarded, brightness and ambient flash levels, finger placement, in-measurement movement, etc. These factors may decrease the accuracy of the signal extracted from the camera's video stream and produce additional errors in the computation of HRV parameters. Thus, there is a need to estimate signal quality and predict possible bias in HRV parameter calculation. In this paper, we describe the method for processing signal from smartphone cameras, estimating signal quality, recognizing RR intervals, and predicting bias of simple HRV parameters.

Focal epileptic seizures anticipation based on patterns of heart rate variability parameters

BACKGROUND AND OBJECTIVE

Heart rate variability parameters are studied by the research community as potential valuable indices for seizure detection and anticipation. This paper investigates heart activity abnormalities during focal epileptic seizures in childhood.

METHODS

Seizures affect both the sympathetic and parasympathetic system which is expressed as abnormal patterns of heart rate variability (HRV) parameters. In the present study, a clinical dataset containing 42 focal seizures in long-term electrocardiographic (ECG) recordings from drug-resistant pediatric epileptic patients (with age 8.2 ± 4.3 years) was analyzed.

RESULTS

Results indicate that the time domain HRV parameters (heart rate, SDNN, standard deviation of heart rate, upper envelope) and spectral HRV parameters (LF/HF, normalized HF, normalized LF, total power) are significantly affected during ictal periods. The HRV features were ranked in terms of their relevance and efficacy to discriminate non-ictal/ictal periods and the top-ranked features were selected using the minimum Redundancy Maximum Relevance algorithm for further analysis. Then, a personalized anticipation algorithm based on multiple regression was introduced providing an "epileptic index" of imminent seizures. The performance of the system resulted in anticipation accuracy of 77.1% and an anticipation time of 21.8 s.

CONCLUSIONS

The results of this analysis could permit the anticipation of focal seizures only using electrocardiographic signals and the implementation of seizure anticipation strategies for a range of real-life clinical applications.

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.

Energy-efficient Compressed Sensing for ambulatory ECG monitoring

Advances in Compressed Sensing (CS) are enabling promising low-energy implementation solutions for wireless Body Area Networks (BAN). While studies demonstrate the potential of CS in terms of overall energy efficiency compared to state-of-the-art lossy compression techniques, the performance of CS remains limited. The aim of this study is to improve the performance of CS-based compression for electrocardiogram (ECG) signals. This paper proposes a CS architecture that combines a novel redundancy removal scheme with quantization and Huffman entropy coding to effectively extend the Compression Ratio (CR). Reconstruction is performed using overcomplete sparse dictionaries created with Dictionary Learning (DL) techniques to exploit the highly structured nature of ECG signals. Performance of the proposed CS implementation is evaluated by analyzing energy-based distortion metrics and diagnostic metrics including QRS beat-detection accuracy across a range of CRs. The proposed CS approach offers superior performance to the most recent state-of-the-art CS implementations in terms of signal reconstruction quality across all CRs tested. Furthermore, QRS detection accuracy of the technique is compared with the well-known lossy Set Partitioning in Hierarchical Trees (SPIHT) compression technique. The proposed CS approach outperforms SPIHT in terms of achievable CR, using the area under the receiver operator characteristic (ROC) curve (AUC). For an application where a minimum AUC performance threshold of 0.9 is required, the proposed technique extends the CR from 64.6 to 90.45 compared with SPIHT, ensuring a 40% saving on wireless transmission costs. Therefore, the results highlight the potential of the proposed technique for ECG computer-aided diagnostic systems.