Ectopic Beats Research Articles

C23 ELECTROCARDIOGRAPHIC PREDICTORS OF PRIMARY VENTRICULAR FIBRILLATION DURING A FIRST ACUTE MYOCARDIAL INFARCTION

Abstract Introduction Few small studies evaluated electrocardiographic (ECG) predictors of ventricular fibrillation (VF) before reperfusion during a first acute myocardial infarction (AMI). None accounted for the time between symptom onset and ECG. Objectives To evaluate the ECG predictors of primary VF in the PREDESTINATION (PRimary vEntricular fibrillation and suDden dEath during a firST myocardIal iNfArcTION) population. Patients and methods: PREDESTINATION is an ongoing, prospective, multicenter, case–control study (1:2 match for sex and age) enrolling patients aged 18–80 with a first AMI, whether complicated (cases) or not (controls) by primary VF before reperfusion. For the present analysis, we matched 1:1 for age, sex, infarct site, and symptom–ECG time, the ECGs of cases before VF and those of controls revascularized at least 60 minutes after symptom onset. ECG traces were digitized in pdf and enlarged by 400%. Three blind analyzes were performed: on the entire population, on only anterior and only non–anterior AMI. Results PREDESTINATION currently includes 1478 patients (mean 59 years, 83% male), 533 of whom are cases. Among the cases, only 59 (58 ± 11 years, 10% females, 64% with anterior AMI, mean symptoms–ECG time 78±60 minutes, mean ECG–VF time 23±15 minutes) had a pre–VF ECG of adequate quality. Both in the general population (118 ECGs) and in anterior AMIs subgroup (76 ECGs), the presence of ventricular (VEB) or supraventricular ectopic beats (SVEB) was associated with an increased risk of developing VF (maximum OR 8.35 for VEBs in anterior AMIs). In the subgroup of anterior AMIs, the Tpeak–Tend interval in non–infarction leads, both corrected for the RR interval (Bazett) and for the QT interval (TpTe/QT) was significantly longer in cases compared to controls. A TpTe/QT ≥0.33 in non–infarction leads was associated with an OR of 4.6 (p=0.02) of developing VF. Conclusions the present analysis, aimed at identifying ECG predictors of primary VF, is the first to perform a rigorous case–control matching including symptom–ECG time. In addition to ectopic beats, a well–known marker and promoter of electrical instability, we identified, only for anterior AMIs, a single additional predictor (TpTe/QT), which correlates with an increased dispersion of repolarization and consequently of ventricular refractoriness.

Investigation into the importance of using natural PVCs and pathological models for potential-based ECGI validation.

Introduction: Premature ventricular contractions (PVCs) are one of the most commonly targeted pathologies for ECGI validation, often through ventricular stimulation to mimic the ectopic beat. However, it remains unclear if such stimulated beats faithfully reproduce spontaneously occurring PVCs, particularly in the case of the R-on-T phenomenon. The objective of this study was to determine the differences in ECGI accuracy when reconstructing spontaneous PVCs as compared to ventricular-stimulated beats and to explore the impact of pathophysiological perturbation on this reconstruction accuracy. Methods: Langendorff-perfused pig hearts (n = 3) were suspended in a human torso-shaped tank, and local hyperkalemia was induced through perfusion of a high-K+ solution (8mM) into the LAD. Recordings were taken simultaneously from the heart and tank surfaces during ventricular pacing and during spontaneous PVCs (including R-on-T), both at baseline and high K+. Epicardial potentials were reconstructed from torso potentials using ECGI. Results: Spontaneously occurring PVCs were better reconstructed than stimulated beats at baseline in terms of electrogram morphology [correlation coefficient (CC) = 0.74 ± 0.05 vs. CC = 0.60 ± 0.10], potential maps (CC = 0.61 ± 0.06 vs. CC = 0.51 ± 0.12), and activation time maps (CC = 0.86 ± 0.07 vs. 0.76 ± 0.10), though there was no difference in the localization error (LE) of epicardial origin (LE = 14 ± 6 vs. 15 ± 11mm). High K+ perfusion reduced the accuracy of ECGI reconstructions in terms of electrogram morphology (CC = 0.68 ± 0.10) and AT maps (CC = 0.70 ± 0.12 and 0.59 ± 0.23) for isolated PVCs and paced beats, respectively. LE trended worse, but the change was not significant (LE = 17 ± 9 and 20 ± 12mm). Spontaneous PVCs were less well when the R-on-T phenomenon occurred and the activation wavefronts encountered a line of block. Conclusion: This study demonstrates the differences in ECGI accuracy between spontaneous PVCs and ventricular-paced beats. We also observed a reduction in this accuracy near regions of electrically inactive tissue. These results highlight the need for more physiologically realistic experimental models when evaluating the accuracy of ECGI methods. In particular, reconstruction accuracy needs to be further evaluated in the presence of R-on-T or isolated PVCs, particularly when encountering obstacles (functional or anatomical) which cause line of block and re-entry.

P3 EXERTIONAL SYNCOPE AND VENTRICULAR ARRHYTHMIA IN A PATIENT WITH CARDIAC AMYLOIDOSIS

Abstract Systemic amyloidosis is a multisystem disorder caused by the misfolding, aggregation, and deposition of certain proteins in various organs and tissues. Cardiac involvement is common and worsens the prognosis. Atrial fibrillation is the most frequent arrhythmia in cardiac amiloidosis (CA), but many cases of ventricular arrhythmias (VA) and sudden cardiac death (SCD) have been described. We present a case of a 64–year–old man admitted following exertional syncope occurred after climbing a flight of stairs. His medical history included left gonarthrosis and surgery for right carpal tunnel syndrome one year before. Physical examination did not revealed signs of heart failure. A 12–lead electrocardiogram (ECG) showed a pseudoinfarction pattern with QS waves in V2 and V3 leads and low voltage in all limb leads. Routine blood tests revealed NT–pro–BNP 3436 pg/ml(n.v.0–125) and high–sensitive troponin T 67 pg/ml(n.v.< 58). A trans–thoracic echocardiogram showed left ventricle (LV) concentric thickening wall with granular and sparkling pat[1]tern, mild reduced ejection fraction, reduced global longitudinal strain with apical sparing, grade 3 diastolic disfunction, biatrial enlargement, mild mitral regurgitation, right ventricle free wall thickening (8 mm), mild reduced TAPSE, and mild pericardial effusion. Cardiac magnetic resonance (CMR) confirmed ventricular wall thickening with evidence at T1 mapping of interstitial infiltration more evident in the septum and inferior–lateral wall with apical savings. A total body 99mTc–HDP scintigraphy showed cardiac uptake with intensity similar to bone signal (Perugini Score 2) suggesting ATTR cardiac amyloidosis. Both kappa and lambda concentrations were normal. The genetic testing did not reveled mutations in the TTR gene. We concluded for a diagnosis of ATTR wild–type CA. The Holter ECG monitoring registered numerous ventricular ectopic beats and an episode of non–sustained ventricular tachycardia. A coronary angiography ruled out coronary artery disease. In consideration of the clinical–instrumental picture an ICD was implanted. Conclusions CA is still underdiagnosed. VA and SCD may further complicate the prognosis. Early diagnosis and adequate stratification of the arrhythmic risk are essential to ensure patients the best therapeutic strategies.

P282 PAUCISYMPTOMATIC POST–COVID MYOCARDITIS IN A YOUNG ATHLETE DURING THE DIAGNOSTIC INVESTIGATIONS BEFORE RETURNING TO COMPETITIVE PRACTICE, POSSIBLE USEFULNESS OF THE GLS ANALYSIS

Abstract It is now known that the Covid19 infection can cause myocardial inflammatory damage. Several studies have shown that the echocardiographic parameter of the global longitudinal strain (GLS) was reduced compared to the general population even in paucisymptomatic patients. A young competitive athlete came to our observation for the diagnostic investigations before returning to competitive practice (return to play protocol) after Sars–Cov2 infection. The electrocardiogram did not show significant abnormalities as well as spirometry examination. Negative inflammation indexes and markers of myocardial damage were observed. The cycloergometer test showed no signs of myocardial distress, rapid chronotropic increase and dyspnea at 100W work load. HolterECG was performed showing 830 isolated ventricular ectopic beats. Echocardiographic examination showed preserved left ventricular systolic function (FEVS 59% – Simpson method) with a moderate reduction of GLS parameter –16,9%. The cardiopulmonary exercise test (CPET) showed an early anaerobic threshold and a normal pVO2max but not in line with the presumed range for a top–level athlete. Putting together the symptoms described, the CPET data and GLS parameter, despite the negative first–level investigations he underwent MRI that documented a myocardial edema in the mid–basal region of inferior and inferolateral walls of the left ventricle. Suitability for competitive practice was blocked, a gradual physical readaptation path was started and therapy with low–dose bisoprolol was set. An important clinical improvement is reported at the 6–month follow–up with evidence of normalization of the GLS parameter –23%. The CPET howed a realignment of the HR/VCO2 curve with a late threshold and increment of pVO2max. The 6–month control MRI examination showed almost no signs of myocardial inflammation and so the patient was made suitable for returning to competitive sport. In our case first–level investigations did not support any hypothesis of myocardial inflammation. The presence of stress symptoms confirmed by CPET data but especially the reduction of GLS parameter led to a deepening investigation with MRI that confirmed the presence of myocardial inflammatory lesion. Our case suggests that within a “return to play” protocol for athletes with recent SarsCov2 infection, the analysis of left ventricular systolic function by GLS may represent an additional useful element to be considered in support of standard investigations.

C4 DOUBLE RHYTHM MAPPING FOR COHERENT ELECTROPHYSIOLOGICAL SUBSTRATE CHARACTERIZATION IN ATRIAL FIBRILLATION PATIENTS

Abstract Background Atrial fibrillation (AF) progresses with a cascade of combined structural, functional, and systemic dysfunctions. Electro–anatomical maps are employed to investigate pro–arrhythmic areas. However, single rhythm maps provide only a partial reconstruction: rhythm and wavefront directionality might mask some of these areas. Purpose. To demonstrate that the combined quantitative analysis of different rhythm maps (i.e., parallel sinus rhythm and paced maps) could allow consistent identification of structural and functional defects sustaining AF. Methods Thirty AF clinical procedures have been performed with mapping system support. In particular, a sinus rhythm activation map and a paced map have been created with a multipolar catheter by setting an extra beat every three sinus beats. Areas of abnormal conduction have been identified and a comparison of electrophysiological biomarkers (EGMs voltage and fractionation) among the two rhythms has been performed. Results This comparative analysis allowed us to identify both structural and functional electrophysiological phenomena. Specifically, we distinguished: (i) abnormal conduction zones confirmed in both maps, both characterized by signal fractionation and a lower voltage (0,56 ± 0,18 mV) with respect to healthy atrial tissue (2,40 ± 0,41 mV), and (ii) abnormal conduction zones visible in only one of the two maps. In the latter, we distinguished whether the absence of the phenomena was due to wavefront orthogonality to the diseased area (revealed thanks to a change in wavefront direction) or due to the presence of a functional phenomenon induced by the pacing. In the first group signal fractionation has been detected in both rhythms and a lower voltage has been found (1,79 ± 1,1 mV) with respect to normal conduction zones (3,26 ± 0,98 mV), while in functional cases EGMs fractionation has been identified only in paced maps and no significant difference has been observed in terms of voltage. Conclusion Double rhythm mapping provides a more robust characterization of atrial abnormal conduction phenomena which better represent AF electrical substrate. It allows the distinction of true structural dysfunctions from dynamic ones thanks to the different wavefront directionality and paced rhythm. Moreover, the structural defects have been confirmed by EGMs analysis, which revealed both low voltage amplitude and high fractionation, characteristics that have not been reported in functional cases.

C72 POSTERIOR WALL CHARACTERIZATION IN PATIENTS AFFECTED BY ATRIAL FIBRILLATION

Abstract Background Several clinical data in humans demonstrate that atrial fibrillation (AF) dominant frequencies and rotors are mostly localized on the posterior wall (PW), but the correlation among these phenomena and abnormal conduction properties during sinus (SR) and paced rhythm (PR) has never been analyzed. Aims. To provide a characterization of the left atrial PW in patients affected by persistent AF. In particular, the purpose is to describe the electrophysiological properties in terms of rotational and focal activities during AF, propagation uniformity during SR and functional sites definition during PR. Methods Six patients from two EP centers have been selected for this analysis. The clinical procedures have been performed with CARTO3 mapping system. All EGMs have been collected with Pentaray and Octaray catheters. For each patient, the module CARTOFINDER has been used during AF to identify areas with shorter and longer cycle length (site of conduction jump), rotational and focal activities; at least 15 areas have been collected in the PW. SR and PR map were performed after electrical cardioversion to exploit the functional phenomena. The paced map has been created by setting an extra beat from the coronary sinus every three sinus beats (pacing interval=local effective refractory period +40ms). Rotational and focal activities during AF have been identified and associated with signals collected during SR and PR. EGMs have been characterized in terms of voltage amplitude (mV), signal duration (ms) and fractionation. Results Abnormal areas of conduction recorded during AF were 33; at least one site of conduction jump was found in every patient, in most of the cases (77%, 10 out of 13) it was localized on the PW. 10 areas of focal activities have been found of which 7 (70%) were localized on the PW. Rotational activity has been found in 2 patients (in one on the PW). During SR and paced rhythm, at sites of interest, we found lower voltages (SR 1.9 ± 1,3mV, PR 1,7 ± 1,15mV), signal fragmentation and longer signal duration (SR 52,7 ± 8,85ms, PR 51,3 ± 11,04ms) compared to healthy tissue. Conclusion There is a strong correlation between sites of interest during AF and signal abnormalities recorded during SR and PR. This research work may pave the way to the study of abnormal conduction sites during SR which represent a relevant element for AF maintenance, providing a further step towards substrate characterization in patients affected by AF.

Ensemble classifier fostered detection of arrhythmia using ECG data.

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).

PO-02-229 AUTOMATED LOCALIZATION OF ATRIAL ECTOPIC BEATS USING 12-LEAD ECG MEAN SPATIO-TEMPORAL VECTOR POSITION: IMPORTANCE OF INCORPORATING PATIENT-SPECIFIC ANATOMIC MODELS

Localizing the origin of atrial ectopic activity from the 12-lead ECG can be challenging, particularly in patients with atrial pathology. To identify the origin of paced atrial ectopic beats (simulating atrial ectopy) using software that quantifies the mean spatio-temporal vector position of cardiac depolarization projected onto generic and patient-specific atrial models. 12-lead ECG data were collected during sinus rhythm (SR) and during regular pacing from the high and low right atrium (HRA/LRA), proximal and distal coronary sinus (CS), pulmonary veins (LSPV, LIPV, RSPV, RIPV), and left atrial appendage (LAA). The origin and trajectory of the atrial signals were calculated with software that analyzes P-waves from the 12-lead ECG and automatically constructs a net vector of electrical activity projected onto a standard generic model of human atria (CineECG; Nieuwerbrug, The Netherlands). The model atria were split into 9 segments, and the software accuracy was scored based on localization of the paced atrial signal origin to the correct segment. To see if patient-specific anatomy improved localization, we also incorporated atrial anatomy from cardiac MRIs acquired from a subset of 12 patients and compared localization to the generic model. We enrolled 19 patients (mean age 70±9, 4 female) with paroxysmal (63%) or persistent (37%) AF and left atrial (LA) enlargement (mean LA volume 58 cc/m2) undergoing pulmonary vein isolation for treating AF. Using the generic atrial model, the software correctly localized 105/142 (74%) beats to the correct atrial segment (Figure). Accuracy was better for RA compared to LA signals (90% vs. 62%, p < 0.05). Localization worked best in the coronary sinus (19/19, 100%) and worst in the LPVs (14/33, 42%). Patient-specific models improved localization of PV signals, particularly left-sided ones (generic vs. patient-specific: RPVs 70% vs 85%, p = 0.36, LPVs 42% vs 76%, p = 0.03). Accuracy improved and was similar for RA vs. LA localization when using patient-specific models (90% vs. 82%, p = 0.48). The novel software algorithm can automatically localize paced signals in both the left and right atria despite significant atrial pathology. Patient-specific anatomic models improve localization. This software holds promise for automatically localizing and classifying atrial arrhythmias and guiding therapy, including catheter ablation, based on standard 12-lead ECG data.

PO-01-037 RISK FACTORS FOR PRESENCE OF NON-PULMONARY VEIN TRIGGERS FOR ATRIAL FIBRILLATION

Targeting of non-pulmonary vein triggers (NPVTs) after pulmonary vein isolation (PVI) may reduce atrial fibrillation (AF) recurrence. Incrementally dosed isoproterenol infusion (up to 30mcg/min) and cardioversion of induced AF used to provoke NPVTs typically requires additional vasopressor support and increases procedure time. We aimed to identify risk factors for the presence of NPVTs in order to identify patient profiles in which NPVT provocation would have greatest yield. Using the Hospital of the University of Pennsylvania AF ablation registry, we compared patients in whom NPVTs were observed to patients in whom NPVTs were not observed using a standard provocation protocol. NPVTs were defined as a single or sequence of non-pulmonary vein atrial ectopic beats triggering non-sustained or sustained AF or focal atrial tachycardia. Continuous variables were compared using unpaired t-Test and categorical variables were compared using Fisher exact test. In a total of 1,306 AF ablation patients between January 2021 and August 2022, 87% underwent NPVT standard provocation testing and 10% of all patients had NPVTs observed. Compared to those without NPVTs, patients who had NPVTs were more likely to be female (41% vs 30%, OR 1.62 [1.12-2.35], p=0.01), and to have sinus node dysfunction (17% vs 7%, OR 2.83 [1.72-4.65], p=0.0002), prior AF ablation (68% vs 34%, OR 4.16 [2.82-6.19], p=<0.0001), and left atrial scar (>2cm2) (71% vs 36%, OR 4.28 [2.78-6.53], p=<0.0001) identified during voltage mapping (defined as peak to peak bipolar voltage <0.5mV of at least three abnormal contiguous electrograms outside pulmonary veins, Table 1). In this patient cohort, the absence of these four risk factors yielded NPVT provocation rate of 0.02%, while the presence of three or four risk factors led to NPVT provocation rate of 30-50% (Figure 1). Incremental presence of the NPVT associated risk factors allows prediction of the likelihood that provocation testing will lead to the induction of NPVTs and may influence decision making to undertake NPVT provocation testing during AF ablation.Tabled 1Table 1VariableOverall (N=1306)NPVT (N=130)No NPVT (N=1006)P ValueOdds Ratio (95% CI)Age (years)130665.89 ± 9.9965.00 ± 10.590.36BMI130629.16 ± 5.6229.95 ± 6.270.17LVEF (%)126656.42 ± 9.5755.28 ± 11.210.27LA volume index (mL/m2)62639.34 ± 17.8939.25 ± 17.180.97CHA2DS2VASc score13002.40 ± 1.452.41 ± 1.560.94Female40353 (41)350 (30)0.011.62 (1.12-2.35)Hx of CVA/TIA12314 (11)109 (9)0.53Diabetes20215 (12)187 (16)0.25Heart Failure / CM41738 (29)379 (32)0.55Hypertension1031105 (81)926 (79)0.65Obstructive Sleep Apnea42343 (33)380 (32)0.84Sinus Node Dysfunction10122 (17)79 (7)0.00022.83 (1.72-4.65)Non-Paroxysmal AF478/124151 (41)427 (38)0.63Had Previous AF Ablation48288 (68)394 (34)<0.00014.16 (2.82-6.19)Has LA scar (Any LVA)426/106978 (71)348 (36)<0.00014.28 (2.78-6.53)Values are reported as mean ± SD, or number (%). P value is comparing NPVT group vs No NPVT group. Continuous variables compared using unpaired t-Test, and categorical variables compared using Fisher exact test. BMI=body mass index; LVEF=left ventricular ejection fraction; LA=left atrium; CVA=cerebrovascular accident; TIA=transient ischemic attack, CM=cardiomyopathy; AF=atrial fibrillation; LVA=low voltage area; NPVT=non-pulmonary vein trigger Open table in a new tab

Automated localization of atrial ectopic beats using 12-lead ECG mean spatio-temporal vector position: Importance of incorporating patient-specific anatomic models

Las complicaciones en los pacientes hospitalizados en sala convencional suelen ser frecuentes y estar asociadas a mayor mortalidad, aumento de la estancia hospitalaria e incremento del coste sanitario. Las constantes fisiológicas monitorizadas habitualmente en los pacientes pueden predecir el riesgo de presentar estas complicaciones. Los sistemas de respuesta rápida son dispositivos de atención a pacientes en riesgo de descompensación en sala de hospitalización que se han creado con el objetivo de detectar estos episodios de forma temprana e instaurar un tratamiento precoz. Para identificar estas situaciones de riesgo están disponibles diferentes escalas de valoración basadas en los parámetros fisiológicos monitorizados. Funcionan mediante un sumatorio de puntos donde a mayor puntuación el paciente presenta un riesgo más alto de descompensación, facilitando la decisión de en qué momento activar el sistema de respuesta. La monitorización remota en sala juega un papel fundamental a modo de nexo entre la detección de estos pacientes y la activación de los sistemas. Actualmente los aparatos y sistemas de monitorización, tales como dispositivos portátiles, sensores inteligentes o dispositivos contactless están disponibles, permitiendo medir estas variables de forma simple, fiable y fácilmente exportable, disminuyendo así el error de transcripción y la carga de trabajo. Además, muchos de estos sistemas de monitorización permiten calcular de manera automática estas diferentes escalas e incluso activar directamente a los sistemas de respuesta rápida, permitiéndoles dar una respuesta proporcional al grado de alteración. En vista de los beneficios descritos sobre la implantación de estos sistemas, el presente artículo realiza una revision de sus componentes y de las herramientas actuales con las que cuenta para optimizar su utilidad.Patients admitted in hospital wards often present complications which are associated with an increase in mortality, length of stay and healthcare costs. The rapid response systems are basically organizations of care to inward patients who potentially can present these events with the objective of an early detection. In order to provide complementary monitoring, proper treatment or intensive care unit transfer if needed. The risk of developing adverse events could be predicted through the vital signs routinely monitored in hospitalized patients. Remote monitoring of the physiological parameters in the ward plays a fundamental role as a link between the detection of these patients and the activation of the rapid response system. Based on the information obtained there are available different assessment scales to identify risk situations. They work through a punctuation system, understanding that a higher score means higher risk of deteriorating; this information will support the decision process which finally activates the response system. Currently, monitoring devices and systems such as portable devices, smart sensors, contactless devices are widely available, allowing these variables to be measured in a simple, reliable and easily exportable way, reducing transcription error and workload. In addition, many of these monitoring systems allow these different scales to be calculated automatically and even directly activating the rapid response systems, allowing them to give a response proportional to the degree of alteration. In view of the benefits described with the implementation of these systems, this article reviews their components and the current tools that we have in order to optimize their usefulness.

Cardiac magnetic resonance findings and prognosis in type 1 myotonic dystrophy.

Cardiac involvement is a major determinant of prognosis in type 1 myotonic dystrophy (DM1), but limited information is available about myocardial remodeling and tissue changes. The aim of the study was to investigate cardiac magnetic resonance (CMR) findings and their prognostic significance in DM1. We retrospectively identified all DM1 patients referred from a neurology unit to our CMR laboratory from 2009 to 2020. Thirty-four patients were included (aged 45 ± 12, 62% male individuals) and compared with 68 age-matched and gender-matched healthy volunteers (43 male individuals, age 48 ± 15 years). At CMR, biventricular and biatrial volumes were significantly smaller (all P < 0.05), as was left ventricular mass (P < 0.001); left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) were significantly lower (all P < 0.01). Five (15%) patients had a LVEF less than 50% and four (12%) a RVEF less than 50%. Nine patients (26%) showed mid-wall late gadolinium enhancement (LGE; 5 ± 2% of LVM), and 14 (41%) fatty infiltration. Native T1 in the interventricular septum (1041 ± 53 ms) was higher than for healthy controls (1017 ± 28 ms) and approached the upper reference limit (1089 ms); the extracellular volume was slightly increased (33 ± 2%, reference <30%). Over 3.7 years (2.0-5.0), 6 (18%) patients died of extracardiac causes, 5 (15%) underwent device implantation; 5 of 21 (24%) developed repetitive ventricular ectopic beats (VEBs) on Holter monitoring. LGE mass was associated with the occurrence of repetitive VEBs (P = 0.002). Lower LV stroke volume (P = 0.017), lower RVEF (P = 0.016), a higher LVMi/LVEDVI ratio (P = 0.016), fatty infiltration (P = 0.04), and LGE extent (P < 0.001) were associated with death. DM1 patients display structural and functional cardiac abnormalities, with variable degrees of cardiac muscle hypotrophy, fibrosis, and fatty infiltration. Such changes, as evaluated by CMR, seem to be associated with the development of ventricular arrhythmias and a worse outcome.

The Elusive Progress of Prosodical Study: Essays in Honor of Thomas Cable

The Elusive Progress of Prosodical Study: Essays in Honor of Thomas Cable

Imbalanced Ectopic Beat Classification Using a Low-Memory-Usage CNN LMUEBCNet and Correlation-Based ECG Signal Oversampling

Objective: This study presents a low-memory-usage ectopic beat classification convolutional neural network (CNN) (LMUEBCNet) and a correlation-based oversampling (Corr-OS) method for ectopic beat data augmentation. Methods: A LMUEBCNet classifier consists of four VGG-based convolution layers and two fully connected layers with the continuous wavelet transform (CWT) spectrogram of a QRS complex (0.712 s) segment as the input of the LMUEBCNet. A Corr-OS method augmented a synthetic beat using the top K correlation heartbeat of all mixed subjects for balancing the training set. This study validates data via a 10-fold cross-validation in the following three scenarios: training/testing with native data (CV1), training/testing with augmented data (CV2), and training with augmented data but testing with native data (CV3). Experiments: The PhysioNet MIT-BIH arrhythmia ECG database was used for verifying the proposed algorithm. This database consists of a total of 109,443 heartbeats categorized into five classes according to AAMI EC57: non-ectopic beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), a fusion of ventricular and normal beats (F), and unknown beats (Q), with 90,586/2781/7236/803/8039 heartbeats, respectively. Three pre-trained CNNs: AlexNet/ResNet18/VGG19 were utilized in this study to compare the ectopic beat classification performance of the LMUEBCNet. The effectiveness of using Corr-OS data augmentation was determined by comparing (1) with/without using the Corr-OS method and (2) the Next-OS data augmentation method. Next-OS augmented the synthetic beat using the next heartbeat of one subject. Results: The proposed LMUEBCNet can achieve a 99.4% classification accuracy under the CV2 and CV3 cross-validation scenarios. The accuracy of the proposed LMUEBCNet is 0.4–0.5% less than the performance obtained from AlexNet/ResNet18/VGG19 under the same data augmentation and cross-validation scenario, but the parameter usage is only 10% or less than that of the AlexNet/ResNet18/VGG19 method. The proposed Corr-OS method can improve ectopic beat classification accuracy by 0.3%. Conclusion: This study developed a LMUEBCNet that can achieve a high ectopic beat classification accuracy with efficient parameter usage and utilized the Corr-OS method for balancing datasets to improve the classification performance.

Association of non-sustained atrial tachycardia and its duration in 24-h Holter monitoring with embolic stroke of unknown source.

Although supraventricular ectopic beats (SVE), including premature atrial contractions (PACs) and non-sustained atrial tachycardia (NSAT), are frequent in the general population, some study results indicate that they are pathologic. SVE may predict undiagnosed atrial fibrillation or be associated with the embolic pattern of ischemic stroke. The aim of this study was to identify the indicators most associated with embolic stroke among the parameters that suggest the burden of SVE. A total of 1920 consecutive acute ischemic stroke (AIS) patients were enrolled from two university hospitals. We defined embolic stroke of unknown source (ESUS) and small vessel occlusion (SVO) etiologies using stricter criteria than the existing conventional criteria. We enrolled 426 (SVO: 310 vs. ESUS: 116) patients who met the inclusion criteria. In the 24-h Holter monitoring parameters, total number of PACs and PAC-to-total beat ratio were not significantly different between the two groups. However, NSATs were more frequent, and the duration of the longest NSAT was longer in the ESUS group. Multivariate logistic regression revealed that high brain natriuretic peptide levels, presence of NSAT, history of previous stroke, and the longest NSAT duration significantly correlated with the ESUS etiology. The presence of NSAT and its duration are more important indicators of embolic stroke than the frequency of PACs is. Therefore, considering secondary prevention in AIS patients with ESUS, 24-h Holter monitoring parameters, such as the presence of NSAT and its duration, could be considered as potential sources of cardio-embolism.

Evaluation of autoantibodies to desmoglein-2 in dogs with and without cardiac disease

Autoantibodies to desmoglein-2 have been associated with arrhythmogenic right ventricular cardiomyopathy (ARVC) in people. ARVC is a common disease in the Boxer dog. The role of anti-desmoglein-2 antibodies in Boxers with ARVC and correlation with disease status or severity is unknown. This prospective study is the first to evaluate dogs of various breeds and cardiac disease state for anti-desmoglein-2 antibodies. The sera of 46 dogs (10 ARVC Boxers, 9 healthy Boxers, 10 Doberman Pinschers with dilated cardiomyopathy, 10 dogs with myxomatous mitral valve disease, and 7 healthy non-Boxer dogs) were assessed for antibody presence and concentration via Western blotting and densitometry. Anti-desmoglein-2 antibodies were detected in all dogs. Autoantibody expression did not differ between study groups and there was no correlation with age or body weight. In dogs with cardiac disease, there was weak correlation with left ventricular dilation (r = 0.423, p = 0.020) but not left atrial size (r = 0.160, p = 0.407). In ARVC Boxers there was strong correlation with the complexity of ventricular arrhythmias (r = 0.841, p = 0.007) but not total number of ectopic beats (r = 0.383, p = 0.313). Anti-desmoglein-2 antibodies were not disease specific in the studied population of dogs. Correlation with some measures of disease severity requires further study with larger populations.

Acute Effects of Coffee Consumption on Health among Ambulatory Adults

BackgroundCoffee is one of the most commonly consumed beverages in the world, but the acute health effects of coffee consumption remain uncertain.MethodsWe conducted a prospective, randomized, case-crossover trial to examine the effects of caffeinated coffee on cardiac ectopy and arrhythmias, daily step counts, sleep minutes, and serum glucose levels. A total of 100 adults were fitted with a continuously recording electrocardiogram device, a wrist-worn accelerometer, and a continuous glucose monitor. Participants downloaded a smartphone application to collect geolocation data. We used daily text messages, sent over a period of 14 days, to randomly instruct participants to consume caffeinated coffee or avoid caffeine. The primary outcome was the mean number of daily premature atrial contractions. Adherence to the randomization assignment was assessed with the use of real-time indicators recorded by the participants, daily surveys, reimbursements for date-stamped receipts for coffee purchases, and virtual monitoring (geofencing) of coffee-shop visits.ResultsThe mean (±SD) age of the participants was 39±13 years; 51% were women, and 51% were non-Hispanic White. Adherence to the random assignments was assessed to be high. The consumption of caffeinated coffee was associated with 58 daily premature atrial contractions as compared with 53 daily events on days when caffeine was avoided (rate ratio, 1.09; 95% confidence interval [CI], 0.98 to 1.20; P=0.10). The consumption of caffeinated coffee as compared with no caffeine consumption was associated with 154 and 102 daily premature ventricular contractions, respectively (rate ratio, 1.51; 95% CI, 1.18 to 1.94); 10,646 and 9665 daily steps (mean difference, 1058; 95% CI, 441 to 1675); 397 and 432 minutes of nightly sleep (mean difference, 36; 95% CI, 25 to 47); and serum glucose levels of 95 mg per deciliter and 96 mg per deciliter (mean difference, −0.41; 95% CI, −5.42 to 4.60).ConclusionsIn this randomized trial, the consumption of caffeinated coffee did not result in significantly more daily premature atrial contractions than the avoidance of caffeine. (Funded by the University of California, San Francisco, and the National Institutes of Health; CRAVE ClinicalTrials.gov number, NCT03671759.)

The incidence of spontaneous arrhythmias in telemetered beagle dogs, Göttingen Minipigs and Cynomolgus non-human primates: A HESI consortium retrospective analysis

IntroductionCharacterization of the incidence of spontaneous arrhythmias to identify possible drug-related effects is often an important part of the analysis in safety pharmacology studies using telemetry. MethodsA retrospective analysis in non-clinical species with and without telemetry transmitters was conducted. Electrocardiograms (24 h) from male and female beagle dogs (n = 131), Göttingen minipigs (n = 108) and cynomolgus non-human primates (NHP; n = 78) were analyzed. ResultsVentricular tachycardia (VT) was observed in 3% of the dogs but was absent in minipigs and NHPs. Ventricular fibrillation (VF) was not observed in the 3 species. Ventricular premature beats (VPBs) were more frequent during daytime and atrioventricular blocks (AVBs) were more frequent at night in all species. A limited number of animals exhibited a high arrhythmia frequency and there was no correlation between animals with higher frequency of an arrhythmia type and the frequency of other arrythmias in the same animals. Clinical chemistry or hematology parameters were not different with or without telemetry devices. NHP with a transmural left ventricular pressure (LVP) catheter exhibited a greater incidence of VPBs and PJCs compared to telemetry animals without LVP. DiscussionAll species were similar with regards to the frequency of ventricular ectopic beats (26–46%) while the dog seemed to have more frequent junctional complexes and AVB compared to NHP and minipigs. Arrhythmia screening may be considered during pre-study evaluations, to exclude animals with abnormally high arrhythmia incidence.

Two-stage detection method of supraventricular and ventricular ectopic beats based on sequential artificial features and heartbeats

Early detection of Supraventricular ectopic beats (SVEBs) and Ventricular ectopic beats (VEBs) is particularly important for preventing dangerous heart diseases. Researchers proposed many automatic detection methods. However, most of these methods do not utilize the contextual information of artificial features among neighbor heartbeats. Our method consists of two stages. The first stage aims to classify all heartbeats into the ectopic class and non-ectopic class. The ectopic class includes SVEBs and VEBs. The ectopic class includes other heartbeats. Firstly, we extracted Sequential Artificial Features (SAFs) from every 20 neighbor heartbeats, including RR intervals, skewness, existence flags of P-waves and T-waves. Secondly, we developed a framework to implement classification by using SAFs and heartbeats. This framework integrates four independent networks, three of them are used to implement the same classification but have different classification performances, the remaining one is used to fuse outputs of the other three networks. In the second stage, we designed a network to further classify ectopic heartbeats into SVEBs and VEBs. We validated the proposed method on the MIT-BIH Arrhythmia Database with the inter-patient model. For the ectopic class and non-ectopic class, the F1-score reaches 90.82 and 98.96 respectively. For SVEBs and VEBs, the F1-score reaches 80.46 and 95.10 respectively. Experimental results demonstrated that our method and SAFs are effective for SVEBs and VEBs classification.

Wrist photoplethysmography-based assessment of ectopic burden in hemodialysis patients

Background and Objective:Frequent ectopic beats in patients under hemodialysis may be associated with the occurrence of life-threatening arrhythmias. However, data regarding the prognostic significance of ectopic beats are limited due to the lack of accessible technology for long-term monitoring. This study aims to investigate the feasibility to assess the ectopic burden from continuously acquired wrist photoplethysmogram (PPG). Methods:The performance of two PPG-based ectopic beat detectors operating on the pulse-to-pulse basis was investigated against the reference ectopic beats detected from the electrocardiogram using 41 24-h recordings obtained from hemodialysis patients under uncontrolled conditions. The estimation accuracy of an average daily ectopic burden and the detection performance of high-risk ectopic burden (≥30 beats/h) were also compared to the reference electrocardiogram. Results:The sensitivity and positive predictive value of 68.1% and 59.5%, respectively, were reached by the better-performing PPG-based ectopic beat detector. A PPG-based average daily ectopic burden was estimated with a median error of 3 beats/h. Sensitivity of 82.7% and specificity of 89.8% were achieved when identifying high-risk ectopic burden in 1-h segments with at least 75% of acceptable-quality PPG. Conclusion:The study demonstrates the potential of the wrist PPG to monitor ectopic burden in an outpatient setting and identify patients eligible for early antiarrhythmic treatment.

An automated patient-specific ECG beat classification using LSTM-based recurrent neural networks

Customized electrocardiogram analysis is inevitable in the cardiac functionality assessment of every single individual patient. In this paper, we firmly introduce a novel framework for patient-specific electrocardiogram (ECG) beat classification characterized by the long-short term memory (LSTM) based recurrent neural networks (RNNs). A novel architecture is developed by governing two LSTM models connected in parallel, and each of them operates on the single-channel ECG input. The proposed model customizes the classification process and imbibes the trait of patient-specific analysis by training the model with the first 5-min segment of heartbeats of the respective patient’s ECG record and tests the same for the rest of the ECG record. The proposed framework undergoes five stages viz; pre-processing, QRS complex detection, segmentation, feature extraction, and LSTM-based RNNs for classification. The efficiency of the proposed framework is deeply examined by the support of various metrics viz; accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictivity (Ppr), F1 score, and G score and the performance of the proposed method is compared to that of some popular state-of-the-art techniques. The proposed framework is exposed to a highly popular and reliable MIT-BIH arrhythmia database for training and testing purposes. The obtained results are found promising and yield superior classification performance in recognizing ventricular ectopic beats (VEB) and supra-ventricular ectopic beats (SVEB). In addition, the proposed framework is recognized as simple, lightweight yet efficient which in turn makes it suitable for continuous monitoring applications in clinical settings for automated ECG analysis to assist the cardiologist.