Cardiac arrhythmias imprint specific signatures on Lorenz plots

Quantified Ataxic Breathing Can Detect Opioid-Induced Respiratory Depression Earlier in Normal Volunteers Infused with Remifentanil

BACKGROUND

Ataxic breathing (AB) is a well-known manifestation of opioid effects in animals and humans, but is not routinely included in monitoring for opioid-induced respiratory depression (OIRD). We quantified AB in normal volunteers receiving increasing doses of remifentanil. We used a support vector machine (SVM) learning approach with features derived from a modified Poincaré plot. We tested the hypothesis that AB may be found when bradypnea and reduced mental status are not present.

METHODS

Twenty-six healthy volunteers (13 female) received escalating target effect-site concentrations of remifentanil with a low baseline dose of propofol to simulate typical breathing patterns in drowsy patients who had received parenteral opioids. We derived respiratory rate (RR) from respiratory inductance plethysmography, mental alertness from the Modified Observer's Assessment of Alertness/Sedation Scale (MOAA/S), and AB severity on a 0 to 4 scale (categories ranging from none to severe) from the SVM. The primary outcome measure was sensitivity and specificity for AB to detect OIRD.

RESULTS

All respiratory measurements were obtained from unperturbed subjects during steady state in 121 assessments with complete data. The sensitivity of AB for detecting OIRD by the conventional method was 92% and specificity was 28%. As expected, 69 (72%) of the instances not diagnosed as OIRD using conventional measures were observed to have at least moderate AB.

CONCLUSIONS

AB was frequently present in the absence of traditionally detected OIRD as defined by reduced mental alertness (MOAA/S score of <4) and bradypnea (RR <8 breaths/min). These results justify the need for future trials to explore replicability with other opioids and clinical utility of AB as an add-on measure in recognizing OIRD.

Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care

BACKGROUND

Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Screening increases AF detection and facilitates the early adoption of comprehensive intervention. Long-term wearable devices have become increasingly popular for AF screening in primary care. However, interpreting data obtained by long-term wearable ECG devices is a problem in primary care. To diagnose the disease quickly and accurately, we aimed to build AF episode detection model based on a nonlinear Lorenz scattergram (LS) and deep learning.

METHODS

The MIT-BIH Normal Sinus Rhythm Database, MIT-BIH Arrhythmia Database and the Long-Term AF Database were extracted to construct the MIT-BIH Ambulatory Electrocardiograph (MIT-BIH AE) dataset. We converted the long-term ECG into a two-dimensional LSs. The LSs from MIT-BIH AE dataset was randomly divided into training and internal validation sets in a 9:1 ratio, which was used to develop and internally validated model. We built a MOBILE-SCREEN-AF (MS-AF) dataset from a single-lead wearable ECG device in primary care for external validation. Performance was quantified using a confusion matrix and standard classification metrics.

RESULTS

During the evaluation of model performance based on the LS, the sensitivity, specificity and accuracy of the model in diagnosing AF were 0.992, 0.973, and 0.983 in the internal validation set respectively. In the external validation set, these metrics were 0.989, 0.956, and 0.967, respectively. Furthermore, when evaluating the model's performance based on ECG records in the MS-AF dataset, the sensitivity, specificity and accuracy of model diagnosis paroxysmal AF were 1.000, 0.870 and 0.876 respectively, and 0.927, 1.000 and 0.973 for the persistent AF.

CONCLUSIONS

The model based on the nonlinear LS and deep learning has high accuracy, making it promising for AF screening in primary care. It has potential for generalization and practical application.

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs

BACKGROUND

Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM).

HYPOTHESIS

Beat-to-beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincaré plots, machine learning, and 3-dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism.

ANIMALS

Three groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n = 26), (2) HP/LSM (n = 26), and (3) sinus node dysfunction (n = 21).

METHODS

Heart rate parameters and Poincaré plot data were determined [median (25%-75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM.

RESULTS

Confusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincaré density grid. Sinus pauses >2 s differentiated (P < .0001) HP/LSM (2340; 583-3947 s) from sinus node dysfunction (8503; 7078-10 050 s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278-323 ms) vs HP/LSM (260; 251-292 ms; P = .008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565-698 ms) vs HP/LSM (843; 799-888 ms; P < .0001).

CONCLUSIONS

Number and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincaré density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction.

Comparability of Heart Rate Turbulence Methodology: 15 Intervals Suffice to Calculate Turbulence Slope – A Methodological Analysis Using PhysioNet Data of 1074 Patients

Heart rate turbulence (HRT) is a characteristic heart rate pattern triggered by a ventricular premature contraction (VPC). It can be used to assess autonomic function and health risk for various conditions, e.g., coronary artery disease or cardiomyopathy. While comparability is essential for scientific analysis, especially for research focusing on clinical application, the methodology of HRT still varies widely in the literature. Particularly, the ECG measurement and parameter calculation of HRT differs, including the calculation of turbulence slope (TS). In this article, we focus on common variations in the number of intervals after the VPC that are used to calculate TS (#TSRR) posing two questions: 1) Does a change in #TSRR introduce noticeable changes in HRT parameter values and classification? and 2) Do larger values of turbulence timing (TT) enabled by a larger #TSRR still represent distinct HRT? We compiled a free-access data set of 1,080 annotated long-term ECGs provided by Physionet. HRT parameter values and risk classes were determined both with #TSRR 15 and 20. A standard local tachogram was created by averaging the tachograms of only the files with the best heart rate variability values. The shape of this standard VPC sequence was compared to all VPC sequences grouped by their TT value using dynamic time warping (DTW) in order to identify HRT shapes. When calculated with different #TSRR, our results show only a little difference between the number of files with enough valid VPC sequences to calculate HRT (<1%) and files with different risk classes (5 and 6% for HRT0-2 and HRTA-C, respectively). In the DTW analysis, the difference between averaged sequences with a specific TT and the standard sequence increased with increasing TT. Our analysis suggests that HRT occurs in the early intervals after the VPC and TS calculated from late intervals reflects common heart rate variability rather than a distinct response to the VPC. Even though the differences in classification are marginal, this can lead to problems in clinical application and scientific research. Therefore, we recommend uniformly using #TSRR 15 in HRT analysis.

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Poincaré Plot Can Be a Useful Tool to Select Potential Responders to Metoprolol Therapy in Children with Vasovagal Syncope

Purpose

To explore the value of the longitudinal axis/transverse axis ratio (L/T) of Poincaré plot in selecting children with vasovagal syncope (VVS) who were suitable for metoprolol therapy.

Patients and Methods

Children with VVS hospitalized in Peking University First Hospital between January 2012 and June 2019 and treated with metoprolol were retrospectively included as the training set, and children with VVS hospitalized between July 2019 and December 2020 were included as the validation set. The sex, age at admission, height, weight, body mass index, course of disease, syncope symptom score before metoprolol treatment, treatment duration, supine heart rate (HR), supine systolic pressure, supine diastolic pressure, peak HR during the head-up tilt test (HUTT), changes of HR during HUTT, hemodynamic response during HUTT, left ventricular ejection fraction, left ventricular fractional shortening and the L/T of Poincaré plot were compared between responders and nonresponders in the training set. Logistic regression analysis was conducted to explore predictors. Receiver operating characteristic curve was utilized to determine the value of the predictors for selecting potential responders. Finally, the value of the predictors was further verified.

Results

In the training set including 105 children, the L/T in responders was distinctly higher than that in nonresponders (P < 0.001), and there was no apparent difference between the two groups in other indexes. The L/T was statistically related to the efficacy of metoprolol (P < 0.001). The L/T >2.7 yielded a sensitivity of 88.2% and a specificity of 82.8% for indicating responders to metoprolol. Taking L/T >2.7 to select potential responders in another 43 children with VVS in the validation set, the sensitivity was 96.6%, specificity 71.4%, and accuracy 88.4%.

Conclusion

The L/T of Poincaré plot >2.7 can be a useful tool to select potential responders to metoprolol therapy in children with VVS.

Lorenz Plot Analysis in Dogs with Sinus Rhythm and Tachyarrhythmias

Simple Summary

The Lorenz plot (LP) is a geometrical method to assess the dynamics of heart rate variability. It consists of a two-dimensional Cartesian coordinate system derived from electrocardiographic monitoring, in which each recorded R-R interval is plotted as a function of the previous R-R interval, and the values of each pair of successive R-R interval define a dot in the plot. The resultant clusters of dots can be evaluated quantitatively and qualitatively, and categorized into distinct geometrical patterns. In humans, several studies have demonstrated that the analysis of LP patterns (LPPs) has the potential to speed-up and improve the accuracy of arrhythmia detection and differentiation, especially in patients with tachyarrhythmias. As data on LP analysis are limited in dogs, this study describes the graphic features of LP derived from Holter recordings obtained in dogs with sinus rhythm and tachyarrhythmias, and analyzes the usefulness of LPP recognition in this species. We sought to evaluate if distinct cardiac rhythms imprint distinct and reproducible LPPs in dogs, as previously described in humans, and if each LLP can be used as a sensitive and specific indicator of a particular cardiac rhythm in this species.

Abstract

The Lorenz plot (LP), a graphical representation of heart rate variability, has been poorly studied in dogs to date. The present study aimed to describe the graphic features of LP in dogs with sinus rhythm (SR) and tachyarrhythmias, and to analyze the usefulness of its pattern recognition. One hundred and nineteen canine Holter recordings were retrospectively evaluated. Cardiac rhythms were classified as: SR; SR with frequent (>100) premature ectopies (atrial, SR-APCs; ventricular, SR-VPCs; atrial and ventricular, SR-APCs-VPCs); atrial fibrillation (AF); and AF with frequent VPCs (AF-VPCs). Lorenz plots were studied qualitatively and quantitatively, and classified by distinct LP patterns (LPPs). Repeatability and reproducibility of LPP classification and diagnostic value were determined. Recordings included: 48 SR, 9 SR-APCs, 35 SR-VPCs, 5 SR-APCs-VPCs, 4 AF, and 18 AF-VPCs. Ten LPPs were identified: comet (n = 12), torpedo (n = 3), Y-shaped (n = 6), diamond (n = 10), diamond with a central silent zone (n = 17), double side-lobe (DSL) (n = 47), triple side-lobe (n = 1), quadruple side-lobe (n = 2), fan (n = 18), and fan with DSL (n = 3). Repeatability and reproducibility of LPP classification were excellent. The DSL pattern was both highly sensitive (91.3%) and specific (94.5%) for SR with frequent premature ectopies, either APCs, or VPCs, or both. The remaining LPPs had lower diagnostic value (high specificity but low sensitivity). Distinct rhythms imprint distinct and reproducible LPPs in dogs. The majority of canine LPPs are specific but insensitive indicators of SR and tachyarrhythmias.

Using Three-Dimensional Lorenz Scatter Plots to Detect Patients with Atrioventricular Node Double Path Caused by Interpolated Ventricular Premature Systoles: A Case Study

A series of related electrophysiology phenomena can be caused by the occurrence of interpolated ventricular premature contraction. In our recent three-dimensional Lorenz R-R scatter plot research, we found that atrioventricular node double path caused by interpolated ventricular premature contraction imprints a specific pattern on three-dimensional Lorenz plots generated from 24-hour Holter recordings. We found two independent subclusters separated from the interpolated premature beat precluster, the interpolated premature beat cluster, and the interpolated premature beat postcluster, respectively. Combined with use of the trajectory tracking function and the leap phenomenon, our results reveal the presence of the atrioventricular node double conduction path.

Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review

AIMS

Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into these available classification methods by discussing previously used digital biomarkers and algorithms and make recommendations for future research.

METHODS

On the 14^th of September 2020, the PubMed database was searched for articles focusing on algorithms for AF detection in ECGs using the MeSH terms Atrial Fibrillation, Electrocardiography and Algorithms. Articles which solely focused on differentiation of types of rhythm disorders or prediction of AF termination were excluded.

RESULTS

The search resulted in 451 articles, of which 130 remained after full-text screening. Not only did the amount of research on methods for AF detection increase over the past years, but a trend towards more complex classification methods is observed. Furthermore, three different types of features can be distinguished: atrial features, ventricular features, and signal features. Although AF is an atrial disease, only 22% of the described methods use atrial features.

CONCLUSION

More and more studies focus on improving accuracy of classification methods for AF in ECGs. As a result, algorithms become increasingly complex and less well interpretable. Only a few studies focus on detecting atrial activity in the ECG. Developing innovative methods focusing on detection of atrial activity might provide accurate classifiers without compromising on transparency.

Poincaré Plot Image and Rhythm-Specific Atlas for Atrial Bigeminy and Atrial Fibrillation Detection

A detector based only on RR intervals capable of classifying other tachyarrhythmias in addition to atrial fibrillation (AF) could improve cardiac monitoring. In this paper a new classification method based in a 2D non-linear RRI dynamics representation is presented. For this aim, the concepts of Poincaré Images and Atlases are introduced. Three cardiac rhythms were targeted: Normal sinus rhythm (NSR), AF and atrial bigeminy (AB). Three Physionet open source databases were used. Poincaré Images were generated for all signals using different Poincaré plot configurations: RR, dRR and RRdRR. The study was computed for different time window lengths and bin sizes. For each rhythm, the Poincaré Images of the 80% of that rhythm's patients were used to create a reference image, a Poincaré Atlas. The remaining 20% were used as test set and classified into one of the three rhythms using normalized mutual information and 2D correlation. The process was iterated in a tenfold cross-validation and patient-wise dataset division. Sensitivity results obtained for RRdRR configuration and bin size 40 ms, for a 60 s time window were 94.35% ±3.68, 82.07% ±9.18 and 88.86% ±12.79 with a specificity of 85.52% ±7.46, 95.91% ±3.14, 96.10% ±2.25 for AF, NSR and AB respectively. Results suggest that a rhythms general RRI pattern may be captured using Poincaré Atlases and that these can be used to classify other signal segments using Poincaré Images. In contrast with other studies, the former method could be generalized to more cardiac rhythms and does not depend on rhythm-specific thresholds.

Heart rate variability analysis in horses for the diagnosis of arrhythmias

Heart rate variability (HRV) analysis has been performed on ECG-derived data sets for more than 170 years but is currently undergoing a rapid evolution, thanks to the expansion of the human and veterinary medical technology sector. Traditional HRV analysis was initially performed to identify changes in vago-sympathetic balance, while the most recent focus has expanded to include the use of complex computer algorithms, neural networks and machine learning technology to identify cardiac arrhythmias, particularly atrial fibrillation (AF). Some of these techniques have recently been translated for use in the field of equine cardiology, with particular focus on improving the diagnosis of arrhythmias both at rest and during exercise. This review focuses on understanding the basic HRV variables and important factors to consider when collecting data for use in HRV analysis. In addition, the use of HRV analysis for the diagnosis of arrhythmias is discussed from human, small animal and equine perspectives. Finally, the future of HRV analysis is briefly introduced, including an overview of future developments in this rapidly expanding and exciting field.

Visual representations of canine cardiac arrhythmias with Lorenz (Poincaré) plots

OBJECTIVE

To characterize the patterns associated with Lorenz plots (LPs) or Poincaré plots derived from the Holter recordings of dogs with various cardiac rhythms.

ANIMALS

77 dogs with 24-hour Holter recordings.

PROCEDURES

A 1-hour period from the Holter recordings from each of 20 dogs without arrhythmias and from each of 57 dogs with arrhythmias (10 each with supraventricular premature complexes, complex supraventricular ectopy, ventricular premature complexes, complex ventricular ectopy, and atrial fibrillation, and 7 with high-grade second-degree atrioventricular block) were used to generate the LPs. Patterns depicted in the LPs were described.

RESULTS

Arrhythmia-free Holter recordings yielded LPs with a Y-shaped pattern and variable silent zones. Recordings with single premature complexes yielded LPs with double side and triple side lobes. Complex ectopy was denoted by dots clustered in the lower left corner of the LPs. The LPs of recordings with atrial fibrillation had fan patterns consistent with a nonlinear relationship between atrial electrical impulses and atrioventricular nodal conduction. The recordings with atrioventricular block yielded LPs with island patterns consistent with variable atrioventricular nodal conduction.

CONCLUSIONS AND CLINICAL RELEVANCE

Distinct LP patterns were identified for common cardiac rhythms of dogs, supportive of nonrandom mechanisms as the cause of most rhythms. Visual interpretation of an LP generated from a Holter recording may aid in determining the arrhythmia type and understanding the arrhythmia's mechanism in dogs and other species.

Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human

The human and dog have sinus arrhythmia; however, the beat-to-beat interval changes were hypothesized to be different. Geometric analyses (R–R interval tachograms, dynamic Poincaré plots) to examine rate changes on a beat-to-beat basis were analyzed along with time and frequency domain heart rate variability from 40 human and 130 canine 24-h electrocardiographic recordings. Humans had bell-shaped beat-interval distributions, narrow interval bands across time with continuous interval change and linear changes in rate. In contrast, dogs had skewed non-singular beat distributions, wide interval bands {despite faster average heart rate of dogs [mean (range); 81 (64–119)] bpm compared to humans [74.5 (59–103) p = 0.005]} with regions displaying a paucity of intervals (zone of avoidance) and linear plus non-linear rate changes. In the dog, dynamic Poincaré plots showed linear rate changes as intervals prolonged until a point of divergence from the line of identity at a mean interval of 598.5 (95% CI: 583.5–613.5) ms (bifurcation interval). The dog had bimodal beat distribution during sleep with slower rates and greater variability than during active hours that showed singular interval distributions, higher rates and less variability. During sleep, Poincaré plots of the dog had clustered or branched patterns of intervals. A slower rate supported greater parasympathetic modulation with a branched compared to the clustered distribution. Treatment with atropine eliminated the non-linear patterns, while hydromorphone shifted the bifurcated branching and beat clustering to longer intervals. These results demonstrate the unique non-linear nature of beat-to-beat variability in the dog compared to humans with increases in interval duration (decrease heart rate). These results provoke the possibility that changes are linear with a dominant sympathetic modulation and non-linear with a dominant parasympathetic modulation. The abrupt bifurcation, zone of avoidance and beat-to-beat patterning are concordant with other studies demonstrating the development of exit block from the sinus node with parasympathetic modulation influencing not only the oscillation of the pacing cells, but conduction to the atria. Studies are required to associate the in vivo sinus node beat patterns identified in this study to the mapping of sinus impulse origin and exit from the sinus node.

Continuous 4-week ECG monitoring with adhesive electrodes reveals AF in patients with recent embolic stroke of undetermined source

BACKGROUND

Atrial fibrillation (AF) frequently escapes routine stroke workup due to its unpredictable and often asymptomatic nature, leaving a significant portion of patients at high risk of recurrent stroke. Recent trials emphasized continuous electrocardiogram (ECG) monitoring in the detection of occult AF. We screened AF in patients meeting the embolic stroke of unknown source (ESUS) criteria using an external miniaturized recorder with an adhesive electrode.

METHODS

Patients aged ≥50 with recent ESUS were prospectively screened and assigned to wear a 1-lead ECG device capable to record continuous ECG for up to 4 weeks. Electrodes were replaced every 3-4 days. Primary outcome was proportion of patients completing at least 80% of monitoring. Secondary outcome measures included incidence of AF and initiation of oral anticoagulation therapy after AF detection.

RESULTS

Fifty-seven patients were monitored (mean age 64.5 ± 8.2 years, median delay from stroke to the start of monitoring 8 days, IQR 4-44). Of these, 51 patients (89.5%) completed at least 80% of the desired monitoring period. We detected AF ≥30 s in seven patients (12.3%), all of whom initiated anticoagulation therapy. Atrial fibrillation was revealed in six patients (85.7%) within the first week of monitoring. Compared to patients without AF, patients with AF were older (70.6 ± 5.1 vs. 63.6 ± 8.3 years, p < 0.011) and more obese (body mass index 30.0 ± 3.4 vs. 26.6 ± 4.6, p < 0.039).

CONCLUSIONS

Prolonged ECG monitoring with an external device using adhesive electrodes is feasible in ESUS patients, since nine out of ten patients used the device appropriately and AF was detected in one out of eight patients.

[Extraction and recognition of attractors in three-dimensional Lorenz plot]

Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency ( A PF ) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, A PF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.

Role of cardiac electronic implantable device in the stratification and management of embolic risk of silent atrial fibrillation: are all atrial fibrillations created equal?

INTRODUCTION

Ischemic strokes may be associated with atrial fibrillation (AF). AF detection is critical in ischemic stroke survivors, often recommending a switch from antiplatelet therapy to oral anticoagulants for secondary prevention. Areas covered: Cardiac implantable electronic devices (CIED) with their long-term recording capability allows to document AF and to quantify the arrhythmia burden. Recent series in pacemaker and implantable cardioverter-defibrillator (ICD) recipients with no prior stroke showed that short episodes of AF increased stroke risk compared with those without AF recorded. Detection of AF by CIEDs represent a unique opportunity for promp prevention of embolic risk in silent AF. It will be attractive to identify AF before a stroke occurs. Expert commentary: The purpose of this article is to review the role of CIED to detect AF, to quantify the role of AF burden, and to guide primary and secondary stroke prevention.

Epileptic Patients are at Risk of Cardiac Arrhythmias: A Novel Approach using QT-nomogram, Tachogram, and Cardiac Restitution Plots

BACKGROUND

Sudden death is reported in patients who had a history of epilepsy and some authors believed that is due to cardiac arrhythmias.

OBJECTIVES

This study aimed to predict that the epileptic patients are at risk of serious cardiac arrhythmias by QT-nomogram, tachogram (Lorenz), and cardiac restitution plots.

METHODS

A total number of 71 healthy subjects (Group I) and 64 newly diagnosed epileptic patients (Group II) were recruited from Al-Yarmouk and Baghdad Teaching hospitals in Baghdad from March 2015 to July 2015 and included in this study. The diagnosis of epilepsy achieved clinically, electroencephalograph record and radio-images including computerized tomography and magnetic image resonance. At the time of entry into the study, an electrocardiography (ECG) was done, and the determinants of each ECG record were calculated. The QT-nomogram, tachogram, and cardiac restitution plots were used to identify the patients at risk of cardiac arrhythmias.

RESULTS

Significant prolonged corrected QT corrected (QTc) and JT corrected intervals were observed in female compared with male at age ≥50 years while the TQ interval was significantly prolonged in males of Group II. Eight patients of Group II had a significant pathological prolonged QTc interval compared with undetectable finding in Group I. QT nomogram did not disclose significant findings while the plots of Lorenz and restitution steepness disclose that the patients of Group II were vulnerable to cardiac arrhythmias. Abnormal ECG findings were observed in the age extremities (≤18 years and ≥50 years) in Group II compared with Group I.

CONCLUSION

Utilization of QT-nomogram, restitution steepness, and tachogram plots is useful tools for detection subclinical vulnerable epileptic patient with cardiac arrhythmias.

Appropriate threshold levels of cardiac beat-to-beat variation in semi-automatic analysis of equine ECG recordings

Background

Although premature beats are a matter of concern in horses, the interpretation of equine ECG recordings is complicated by a lack of standardized analysis criteria and a limited knowledge of the normal beat-to-beat variation of equine cardiac rhythm. The purpose of this study was to determine the appropriate threshold levels of maximum acceptable deviation of RR intervals in equine ECG analysis, and to evaluate a novel two-step timing algorithm by quantifying the frequency of arrhythmias in a cohort of healthy adult endurance horses.

Results

Beat-to-beat variation differed considerably with heart rate (HR), and an adaptable model consisting of three different HR ranges with separate threshold levels of maximum acceptable RR deviation was consequently defined. For resting HRs <60 beats/min (bpm) the threshold level of RR deviation was set at 20%, for HRs in the intermediate range between 60 and 100 bpm the threshold was 10%, and for exercising HRs >100 bpm, the threshold level was 4%. Supraventricular premature beats represented the most prevalent arrhythmia category with varying frequencies in seven horses at rest (median 7, range 2–86) and six horses during exercise (median 2, range 1–24).

Conclusions

Beat-to-beat variation of equine cardiac rhythm varies according to HR, and threshold levels in equine ECG analysis should be adjusted accordingly. Standardization of the analysis criteria will enable comparisons of studies and follow-up examinations of patients. A small number of supraventricular premature beats appears to be a normal finding in endurance horses. Further studies are required to validate the findings and determine the clinical significance of premature beats in horses.

Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

BACKGROUND

The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used.

OBJECTIVE

We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR).

METHODS

A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE.

RESULTS

During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01-26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02-1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01-7.96; p = 0.041).

CONCLUSIONS

In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT.

Controversies in cardioembolic stroke

OPINION STATEMENT

Cardioembolic (CE) stroke mechanisms account for a significant number of ischemic strokes; however, the true burden is likely underestimated. It is critically important to identify patients with CE strokes because these individuals have high recurrence rates and represent a subgroup of patients who may benefit from targeted therapy in the form of anticoagulation or device based treatments. Current guidelines offer recommendations for diagnosis and treatment of these patients; however, important questions remain. First, appropriate cardiac testing in the setting of CE must be individualized and the optimal duration of electrocardiographic monitoring to rule out atrial fibrillation (AF) is unclear. Second, risk stratification tools for AF remain understudied, and there is controversy about which anticoagulant agents are most appropriate. Lastly, important potential CE sources of stroke such as patent foramen ovale have garnered significant attention recently, and debate regarding how to manage these patients persists. In this review, we discuss some of the important controversies in diagnosing and treating patients with possible CE stroke, pointing to areas where future research might be particularly valuable.

Silent Atrial Fibrillation: A Critical Review

Atrial fibrillation (AF) in the most common cardiac arrhythmia, and is associated with an increased risk of thromboembolic events. Silent AF is an asymptomatic form of AF incidentally diagnosed during a routine test or manifesting as an arrhythmia-related complication. Although recent trials have clearly demonstrated that patients with sub-clinical AF are at increased risk of stroke, the real incidence of this form of AF is still unknown. In fact, studies about silent AF had been performed only in specific subgroups of patients such as those with implantable cardiac devices, with recent cryptogenic stroke or transient ischemic attack, and recently undergoing AF ablation. Continuous ECG-monitoring in patients without implantable cardiac devices may improve silent AF detection but its cost-effectiveness actually is not well established in all kind of patients. Moreover, recent data have revealed that only a small number of these patients may have sub-clinical AF within the month prior to their stroke suggesting a lack of temporal relationship between the stroke and the AF episode. This paper will review available data on different diagnostic tools for silent AF detection with a focus on their cost-effectiveness, analyzing the direct correlation between the arrhythmia and embolic events, and discussing areas of uncertainty where further research is required.

Atrial fibrillation in patients with cryptogenic stroke

BACKGROUND

Atrial fibrillation is a leading preventable cause of recurrent stroke for which early detection and treatment are critical. However, paroxysmal atrial fibrillation is often asymptomatic and likely to go undetected and untreated in the routine care of patients with ischemic stroke or transient ischemic attack (TIA).

METHODS

We randomly assigned 572 patients 55 years of age or older, without known atrial fibrillation, who had had a cryptogenic ischemic stroke or TIA within the previous 6 months (cause undetermined after standard tests, including 24-hour electrocardiography [ECG]), to undergo additional noninvasive ambulatory ECG monitoring with either a 30-day event-triggered recorder (intervention group) or a conventional 24-hour monitor (control group). The primary outcome was newly detected atrial fibrillation lasting 30 seconds or longer within 90 days after randomization. Secondary outcomes included episodes of atrial fibrillation lasting 2.5 minutes or longer and anticoagulation status at 90 days.

RESULTS

Atrial fibrillation lasting 30 seconds or longer was detected in 45 of 280 patients (16.1%) in the intervention group, as compared with 9 of 277 (3.2%) in the control group (absolute difference, 12.9 percentage points; 95% confidence interval [CI], 8.0 to 17.6; P<0.001; number needed to screen, 8). Atrial fibrillation lasting 2.5 minutes or longer was present in 28 of 284 patients (9.9%) in the intervention group, as compared with 7 of 277 (2.5%) in the control group (absolute difference, 7.4 percentage points; 95% CI, 3.4 to 11.3; P<0.001). By 90 days, oral anticoagulant therapy had been prescribed for more patients in the intervention group than in the control group (52 of 280 patients [18.6%] vs. 31 of 279 [11.1%]; absolute difference, 7.5 percentage points; 95% CI, 1.6 to 13.3; P=0.01).

CONCLUSIONS

Among patients with a recent cryptogenic stroke or TIA who were 55 years of age or older, paroxysmal atrial fibrillation was common. Noninvasive ambulatory ECG monitoring for a target of 30 days significantly improved the detection of atrial fibrillation by a factor of more than five and nearly doubled the rate of anticoagulant treatment, as compared with the standard practice of short-duration ECG monitoring. (Funded by the Canadian Stroke Network and others; EMBRACE ClinicalTrials.gov number, NCT00846924.).

Discriminating atrial flutter from atrial fibrillation using a multilevel model of atrioventricular conduction

BACKGROUND

The discrimination between atrial flutter (AFlu) and atrial fibrillation (AFib) can be made difficult by an irregular ventricular response owing to complex conduction phenomena within the atrioventricular (AV) node, known as multilevel AV block. We tested the hypothesis that a mathematical algorithm might be suitable to discriminate both arrhythmias.

OBJECTIVES

To discriminate AFlu with irregular ventricular response from AFib based on the sequence of R-R intervals.

METHODS

Intracardiac recordings of 100 patients (50 patients with AFib and 50 patients with AFlu) were analyzed. On the basis of a numerical simulation of variable flutter frequencies followed by 2 levels of AV block in series, a given sequence of R-R intervals was analyzed.

RESULTS

Although the ventricular response displays absolute irregularity in AFib, the sequences of R-R intervals follow certain rules in AFlu. We find that using a mathematical simulation of multilevel AV block, based on the R-R sequence of 16 ventricular beats, a stability of atrial activation could be predicted with a sensitivity of 84% and a specificity of 74%. When limiting the ventricular rate to 125 beats/min, discrimination could be performed with a sensitivity of even 89% and a specificity of 80%. In cases of AFlu, the atrial cycle length could be predicted with high accuracy.

CONCLUSION

On the basis of the electrophysiological mechanism of multilevel AV block, we developed a computer algorithm to discriminate between AFlu and Afib. This algorithm is able to predict the stability and cycle length of atrial activation for short R-R sequences with high accuracy.

Usefulness of continuous electrocardiographic monitoring for atrial fibrillation

The problem of early recognition of atrial fibrillation (AF) is greatly aggravated by the often silent nature of the rhythm disturbance. In about 1/3 of patients with this arrhythmia, patients are not aware of the so-called asymptomatic AF. In the past 15 years, the diagnostic data provided by implanted pacemakers and defibrillators have dramatically increased knowledge about silent AF. The unreliability of symptoms to estimate AF burden and to identify patients with and without AF has been pointed out not only by pacemaker trials but also in patients without implanted devices. The technology for continuous monitoring of AF has been largely validated. It is a powerful tool to detect silent paroxysmal AF in patients without previously documented arrhythmic episodes, such as those with cryptogenic stroke or other risk factors. Early diagnosis triggers earlier treatment for primary or secondary stroke prevention. Today, new devices are also available for pure electrocardiographic monitoring, implanted subcutaneously using a minimally invasive technique. In conclusion, this recent and promising technology adds relevant clinical and scientific information to improve risk stratification for stroke and may play an important role in testing and tailoring the therapies for rhythm and rate control.

Poincaré plots and tachograms reveal beat patterning in sick sinus syndrome with supraventricular tachycardia and varying AV nodal block

Using 24-h ambulatory electrocardiography, the RR intervals of all beats were determined in a West Highland white terrier with sick sinus syndrome characterized by long sinus pauses, bradycardia, supraventricular tachycardia (SVT) and varying degrees of atrioventricular (AV) heart block. Distinctive patterns of bradycardia and 1:1, 2:1, 3:1, 4:1 and 5:1 AV block associated with SVT were evident in the tachogram (RR interval distribution over time) and Poincaré plots (short-term heart rate variability plots of RRn versus RRn+1). These patterns differed from those of abrupt alteration in cycle length during long sinus pauses or bursts of supraventricular tachycardia. Recognition of such patterns may direct attention to time points for which close attention to the cardiac rhythm should be evaluated in the full-disclosure of the 24-h ECG recording.

"Zone of avoidance": RR interval distribution in tachograms, histograms, and Poincaré plots of a Boxer dog

The RR intervals of sinus and ventricular beats were determined by analysis of a 24-h ambulatory electrocardiogram in a Boxer before and after treatment with sotalol. These RR intervals were plotted using tachograms, histograms, and Poincaré plots. The tachogram demonstrated a 'band' wherein a range of RR intervals was infrequent, the histogram did not take the form of a single Gaussian distribution of RR intervals, and the Poincaré plot showed nonhomogeneous beat-to-beat variability. This type of patterning was described as a "zone of avoidance" potentially caused by the clustering of beats within specific ranges. Treatment with sotalol enhanced the "zone of avoidance". Further investigation is needed to understand the mechanism for this observation as well as any clinical implications.