Showing papers on "QRS complex published in 2021"
TL;DR: Left bundle branch pacing appears to be a promising method for delivering CRT with similar improvements in symptoms and LV function with LBBP and HBP, significantly greater than those seen in patients treated with BVP, in this non-randomized study.
158 citations
TL;DR: LBBAP provides remarkably low and stable pacing thresholds and was associated with improved clinical and echocardiographic outcomes and provides an alternative option for CRT.
155 citations
TL;DR: In this paper, the authors established electrocardiographic (ECG) criteria for LBB capture and showed equivalency of LV activation times on ECG during native and paced LBB conduction.
77 citations
TL;DR: In this article, the feasibility and outcomes of LBBAP-optimized CRT (LOT-CRT) combined with coronary venous left ventricular pacing were evaluated in an international multicenter study.
67 citations
TL;DR: In this paper, the authors compared clinical outcomes between LBBAP and RVP among patients undergoing pacemaker implantation in bradycardia indications between April 2018 and October 2020.
62 citations
TL;DR: The data show that standard ECG can be helpful for an initial risk stratification of patients admitted for SARS-CoV-2 infectious disease.
Abstract: AIMS: The main severe complications of SARS-CoV-2 infection are pneumonia and respiratory distress syndrome. Recent studies, however, reported that cardiac injury, as assessed by troponin levels, is associated with a worse outcome in these patients. No study hitherto assessed whether the simple standard electrocardiogram (ECG) may be helpful for risk stratification in these patients. METHODS AND RESULTS: We studied 324 consecutive patients admitted to our Emergency Department with a confirmed diagnosis of SARS-CoV-2 infection. Standard 12-lead ECG recorded on admission was assessed for cardiac rhythm and rate, atrioventricular and intraventricular conduction, abnormal Q/QS wave, ST segment and T wave changes, corrected QT interval, and tachyarrhythmias. At a mean follow-up of 31 ± 11 days, 44 deaths occurred (13.6%). Most ECG variables were significantly associated with mortality, including atrial fibrillation (P = 0.002), increasing heart rate (P = 0.002), presence of left bundle branch block (LBBB; P < 0.001), QRS duration (P <0 .001), a QRS duration of ≥110 ms (P < 0.001), ST segment depression (P < 0.001), abnormal Q/QS wave (P = 0.034), premature ventricular complexes (PVCs; P = 0.051), and presence of any ECG abnormality [hazard ratio (HR) 4.58; 95% confidence interval (CI) 2.40-8.76; P < 0.001]. At multivariable analysis, QRS duration (P = 0.002), QRS duration ≥110 ms (P = 0.03), LBBB (P = 0.014) and presence of any ECG abnormality (P = 0.04) maintained a significant independent association with mortality. CONCLUSION: Our data show that standard ECG can be helpful for an initial risk stratification of patients admitted for SARS-CoV-2 infectious disease.
50 citations
TL;DR: In this article, the feasibility and efficacy of cardiac resynchronization therapy (CRT) via left bundle branch pacing (LBBP-CRT), compared with optimized biventricular pacing (BVP) with adaptive algorithm, in heart failure with reduced left ventricular ejection fraction ≤35% (HFrEF) and LBBB, was evaluated.
Abstract: AIMS The purpose of our study was to evaluate the feasibility and efficacy of cardiac resynchronization therapy (CRT) via left bundle branch pacing (LBBP-CRT) compared with optimized biventricular pacing (BVP) with adaptive algorithm (BVP-aCRT) in heart failure with reduced left ventricular ejection fraction ≤35% (HFrEF) and left bundle branch block (LBBB). METHODS AND RESULTS One hundred patients with HFrEF and LBBB undergoing CRT were prospectively enrolled in a non-randomized fashion and divided into two groups (LBBP-CRT, n = 49; BVP-aCRT, n = 51) in four centres. Implant characteristics and echocardiographic parameters were accessed at baseline and during 6-month and 1-year follow-up. The success rate for LBBP-CRT and BVP-aCRT was 98.00% and 91.07%. Fused LBBP had the greatest reduced QRS duration compared to BVP-aCRT (126.54 ± 11.67 vs. 102.61 ± 9.66 ms, P < 0.001). Higher absolute left ventricular ejection fraction (LVEF) and △LVEF was also achieved in LBBP-CRT than BVP-aCRT at 6-month (47.58 ± 12.02% vs. 41.24 ± 10.56%, P = 0.008; 18.52 ± 13.19% vs. 12.89 ± 9.73%, P = 0.020) and 1-year follow-up (49.10 ± 10.43% vs. 43.62 ± 11.33%, P = 0.021; 20.90 ± 11.80% vs. 15.20 ± 9.98%, P = 0.015, P = 0.015). There was no significant difference in response rate between two groups while higher super-response rate was observed in LBBP-CRT as compared to BVP-aCRT at 6 months (53.06% vs. 36.59%, P = 0.016) and 12 months (61.22% vs. 39.22%, P = 0.028) during follow-up. The pacing threshold was lower in LBBP-CRT at implant and during 1-year follow-up (both P < 0.001). Procedure-related complications and adverse clinical outcomes including heart failure hospitalization and mortality were not significantly different in two groups. CONCLUSIONS The feasibility and efficacy of LBBP-CRT demonstrated better electromechanical resynchronization and higher clinical and echocardiographic response, especially higher super-response than BVP-aCRT in HFrEF with LBBB.
36 citations
TL;DR: An improved RR interval-based cardiac arrhythmia classification approach that is significantly better and more accurate than the other classifiers used in this method.
35 citations
TL;DR: The results show that ECGdeli can reliably detect P waves, QRS complexes and T waves and can contribute to diagnose specific cardiac diseases by analyzing the ECG signal.
31 citations
TL;DR: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task, and can be extended to other medical signal research fields.
Abstract: Objective : Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. Methods : In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. Results : The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. Conclusion : Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. Significance : The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.
31 citations
TL;DR: Non-invasive assessment of biventricular 3D activation using the 12-lead ECG and MR imaging is feasible and potential applications include patient-specific modelling and pre-/per-procedural evaluation of ventricular activation.
Abstract: Aims: Non-invasive imaging of electrical activation requires high-density body surface potential mapping. The nine electrodes of the 12-lead electrocardiogram (ECG) are insufficient for a reliable reconstruction with standard inverse methods. Patient-specific modelling may offer an alternative route to physiologically constraint the reconstruction. The aim of the study was to assess the feasibility of reconstructing the fully 3D electrical activation map of the ventricles from the 12-lead ECG and cardiovascular magnetic resonance (CMR).
Methods and results: Ventricular activation was estimated by iteratively optimizing the parameters (conduction velocity and sites of earliest activation) of a patient-specific model to fit the simulated to the recorded ECG. Chest and cardiac anatomy of 11 patients (QRS duration 126-180 ms, documented scar in two) were segmented from CMR images. Scar presence was assessed by magnetic resonance (MR) contrast enhancement. Activation sequences were modelled with a physiologically based propagation model and ECGs with lead field theory. Validation was performed by comparing reconstructed activation maps with those acquired by invasive electroanatomical mapping of coronary sinus/veins (CS) and right ventricular (RV) and left ventricular (LV) endocardium. The QRS complex was correctly reproduced by the model (Pearson's correlation r = 0.923). Reconstructions accurately located the earliest and latest activated LV regions (median barycentre distance 8.2 mm, IQR 8.8 mm). Correlation of simulated with recorded activation time was very good at LV endocardium (r = 0.83) and good at CS (r = 0.68) and RV endocardium (r = 0.58).
Conclusion: Non-invasive assessment of biventricular 3D activation using the 12-lead ECG and MR imaging is feasible. Potential applications include patient-specific modelling and pre-/per-procedural evaluation of ventricular activation.
TL;DR: In this paper, the authors analyzed findings and trends on serial electrocardiograms (ECGs) in multisystem inflammatory syndrome in children (MIS-C) associated with coronavirus disease taken during the course of illness and at followup.
TL;DR: In this article, the authors used multivariable logistic regression analysis to compare the ECG features of patients who died during the hospitalization with those who survived and found that A-Fib, atrial flutter, and ST-segment depression were predictive of mortality.
Abstract: BACKGROUND: Cardiovascular events have been reported in the setting of coronavirus disease-19 (COVID-19). It has been hypothesized that systemic inflammation may aggravate arrhythmias or trigger new-onset conduction abnormalities. However, the specific type and distribution of electrocardiographic disturbances in COVID-19 as well as their influence on mortality remain to be fully characterized. METHODS: Electrocardiograms (ECGs) were obtained from 186 COVID-19-positive patients at a large tertiary care hospital in Northern Nevada. The following arrhythmias were identified by cardiologists: sinus bradycardia, sinus tachycardia, atrial fibrillation (A-Fib), atrial flutter, multifocal atrial tachycardia (MAT), premature atrial contraction (PAC), premature ventricular contraction (PVC), atrioventricular block (AVB), and right bundle branch block (RBBB). The mean PR interval, QRS duration, and corrected QT interval were documented. Fisher's exact test was used to compare the ECG features of patients who died during the hospitalization with those who survived. The influence of ECG features on mortality was assessed with multivariable logistic regression analysis. RESULTS: A-Fib, atrial flutter, and ST-segment depression were predictive of mortality. In addition, the mean ventricular rate was higher among patients who died as compared to those who survived. The use of therapeutic anticoagulation was associated with reduced odds of death; however, this association did not reach statistical significance. CONCLUSION: The underlying pathogenesis of COVID-19-associated arrhythmias remains to be established, but we postulate that systemic inflammation and/or hypoxia may induce potentially lethal conduction abnormalities in affected individuals. Longitudinal studies are warranted to evaluate the risk factors, pathogenesis, and management of COVID-19-associated cardiac arrhythmias.
TL;DR: In this paper, the authors present a review of the current literature in this subject and present several methods and criteria for differentiation between nonselective (ns) capture - capture of the HPS and the adjacent myocardium and myocardial-only capture.
Abstract: During His-Purkinje conduction system (HPS) pacing, it is crucial to confirm capture of the His bundle or left bundle branch versus myocardialonly capture. For this, several methods and criteria for differentiation between non-selective (ns) capture - capture of the HPS and the adjacent myocardium - and myocardial-only capture were developed. HPS capture results in faster and more homogenous depolarisation of the left ventricle than right ventricular septal (RVS) myocardial-only capture. Specifically, the depolarisation of the left ventricle (LV) does not require slow cell-to-cell spread of activation from the right side to the left side of the interventricular septum but begins simultaneously with QRS onset as in native depolarisation. These phenomena greatly influence QRS complex morphology and form the basis of electrocardiographic differentiation between HPS and myocardial paced QRS. Moreover, the HPS and the working myocardium are different tissues within the heart muscle that vary not only in conduction velocities but also in refractoriness and capture thresholds. These last two differences can be exploited for the diagnosis of HPS capture using dynamic pacing manoeuvres, namely differential output pacing, programmed stimulation and burst pacing. This review summarises current knowledge of this subject.
TL;DR: In this article, an embedded algorithm for the detection of the QRS complex of an ECG signal is presented, which is based on the shape and appearance of the signal and extracts certain characteristics like the shape of the complex, its slope, trend, and the duration between two successive QRS complexes, and then use them to increase detection accuracy.
Abstract: Electrocardiogram (ECG) is one of the most useful medical examinations for the monitoring of cardiovascular diseases. The position and the duration of the QRS complex on the ECG signal are very important in the diagnosis of these diseases. Even though several R-peak (and hence QRS complex) detection algorithms are available, most are based on complex computations that require off-line processing on a personal computer (PC). However, with the advances in wearable devices and telemedicine, an algorithm that can run efficiently on a microcontroller (or embedded system) is needed. This article presents the development of an embedded algorithm for the detection of the QRS complex of an ECG signal. The algorithm is based on the shape and appearance of the signal. It extracts certain characteristics like the shape of the QRS complex, its slope, trend, and the duration between two successive QRS complexes, and then use them to increase detection accuracy. First, the R-peak is detected through the application of three levels of tests using adaptive thresholds. Second, from each R position, the positions of Q and S are detected using three other tests. To evaluate the performance of the algorithm, the MIT-BIH database was used and the sensitivity, positive prediction, and F1 score were used as evaluation metrics. The algorithm obtained average F1 scores of 99.67%, 99.73%, and 99.83% for the MIT-BIH Arrhythmia, Pacemaker Rhythm, and the Normal Sinus Rhythm Databases, respectively. Both normal and abnormal ECG signals were used in this performance test. The algorithm was then implemented on a microcontroller system, and its accuracy and run time were evaluated. The obtained F1 score results were the same as on the personal computer (PC) and an average run time of $16.23~\mu \text{s}$ per sample was obtained. The performance of the algorithm was also compared to other commonly used algorithms. The proposed algorithm has great potential in wearable systems for long-term monitoring.
Harvard University1, NewYork–Presbyterian Hospital2, Albert Einstein College of Medicine3, Memorial Sloan Kettering Cancer Center4, MedStar Washington Hospital Center5, University of South Florida6, University of Erlangen-Nuremberg7, State University of Campinas8, Lehigh Valley Hospital9, Mount Sinai Hospital10, Emory University11, University of Naples Federico II12, Hospital General Universitario Gregorio Marañón13, University of Southampton14, Lahey Hospital & Medical Center15, University of California, Los Angeles16, Aix-Marseille University17, University of Pennsylvania18, Imperial College London19, Brigham and Women's Hospital20, Toronto General Hospital21
TL;DR: In this article, the authors found that the QRS duration is increased in ICI myocarditis and is associated with increased MACE risk, and the association between ECG values and major adverse cardiac events (MACE) was also tested.
Abstract: Background Myocarditis is a highly morbid complication of immune checkpoint inhibitor (ICI) use that remains inadequately characterized. The QRS duration and the QTc interval are standardized electrocardiographic measures that are prolonged in other cardiac conditions; however, there are no data on their utility in ICI myocarditis. Methods From an international registry, ECG parameters were compared between 140 myocarditis cases and 179 controls across multiple time points (pre-ICI, on ICI prior to myocarditis, and at the time of myocarditis). The association between ECG values and major adverse cardiac events (MACE) was also tested. Results Both the QRS duration and QTc interval were similar between cases and controls prior to myocarditis. When compared with controls on an ICI (93±19 ms) or to baseline prior to myocarditis (97±19 ms), the QRS duration prolonged with myocarditis (110±22 ms, p Conclusions The QRS duration is increased in ICI myocarditis and is associated with increased MACE risk. Use of this widely available ECG parameter may aid in ICI myocarditis diagnosis and risk-stratification.
Vanderbilt University1, University of Michigan2, Beth Israel Deaconess Medical Center3, International University of Health and Welfare4, University of California, San Francisco5, Dartmouth College6, University of Wisconsin-Madison7, University of Texas MD Anderson Cancer Center8, Heidelberg University9, University of Paris10
TL;DR: In this article, the authors describe the electrocardiographic (ECG) findings in ICI-myocarditis, compare them to acute cellular rejection (ACR), and evaluate their prognostic significance.
Abstract: Importance Immune-checkpoint inhibitor (ICI)-myocarditis often presents with arrhythmias, but electrocardiographic (ECG) findings have not been well described. ICI-myocarditis and acute cellular rejection (ACR) following cardiac transplantation share similarities on histopathology; however, whether they differ in arrhythmogenicity is unclear. Objectives To describe ECG findings in ICI-myocarditis, compare them to ACR, and evaluate their prognostic significance. Design Cases of ICI-myocarditis were retrospectively identified through a multicenter network. Grade 2R or 3R ACR was retrospectively identified within one center. Two blinded cardiologists interpreted ECGs. Setting 49 medical centers spanning 11 countries. Participants 147 adults with ICI-myocarditis, 50 adults with ACR. Exposure Myocarditis after ICI exposure per European Society of Cardiology criteria for clinically suspected myocarditis, grade 2R or 3R ACR per the International Society for Heart and Lung Transplantation working formulation for biopsy diagnosis of rejection. Outcomes All-cause mortality, myocarditis-related mortality; and composite endpoint (defined as myocarditis-related mortality and life-threatening ventricular arrhythmia). Results Of 147 patients, the median age was 67 years (58-77) with 92 (62.6%) men. At 30 days, ICI-myocarditis had an all-cause mortality of 39/146(26.7%), myocarditis-related mortality of 24/146(16.4%), and composite endpoint of 37/146(25.3%). All-cause mortality was more common in patients who developed complete heart block (12/25[48%] vs 27/121[22.3%], hazard ratio (HR)=2.62, 95% confidence interval [1.33-5.18],p=0.01) or life-threatening ventricular arrhythmias (12/22[55%] vs 27/124[21.8%], HR=3.10 [1.57-6.12],p=0.001) within 30 days after presentation. Compared to ACR, patients with ICI-myocarditis were more likely to experience life-threatening ventricular arrhythmias (22/147 [16.3%] vs 1/50 [2%];p=0.01) or third-degree heart block (25/147 [17.0%] vs 0/50 [0%];p=0.002). In ICI-myocarditis, overall mortality, myocarditis-related mortality, and composite outcome adjusted for age and sex were associated with pathological Q-waves on presenting ECG (hazard ratio by subdistribution model [HR(sh)]=5.98[2.8-12.79],p Conclusions ICI-myocarditis has more life-threatening arrhythmias than ACR and manifests as decreased voltage, conduction disorders, and repolarization abnormalities. Ventricular tachycardias, complete heart block, low-voltage, and pathological Q-waves were associated with adverse outcomes. NCT NCT04294771 Key Points Question What are the electrocardiographic manifestations of immune checkpoint inhibitor (ICI)-associated myocarditis? How do they compare to acute cellular rejection (ACR), which is resembling pathophysiologically to ICI-myocarditis? Which electrocardiographic features are associated with adverse outcomes? Findings ICI-myocarditis results in more frequent ventricular arrhythmias and high-degree atrioventricular blocks compared to ACR. Prolonged QRS intervals, decreased voltage, conduction disorders, and pathological Q-waves are predictors of adverse outcomes in ICI-associated myocarditis. Meaning ICI-associated myocarditis is a highly arrhythmogenic cardiomyopathy. Ventricular arrhythmias, conduction disorders, low-voltage, and pathological Q-waves are associated with a poor prognosis.
TL;DR: In this article, the authors investigated acute electrophysiological effects of LBBP and LVSP as compared to intrinsic ventricular conduction in patients with bradycardia.
Abstract: Background: Left bundle branch area pacing (LBBAP) has recently been introduced as a novel physiological pacing strategy. Within LBBAP, distinction is made between left bundle branch pacing (LBBP) and left ventricular septal pacing (LVSP, no left bundle capture). Objective: To investigate acute electrophysiological effects of LBBP and LVSP as compared to intrinsic ventricular conduction. Methods: Fifty patients with normal cardiac function and pacemaker indication for bradycardia underwent LBBAP. Electrocardiography (ECG) characteristics were evaluated during pacing at various depths within the septum: starting at the right ventricular (RV) side of the septum: the last position with QS morphology, the first position with r’ morphology, LVSP and—in patients where left bundle branch (LBB) capture was achieved—LBBP. From the ECG’s QRS duration and QRS morphology in lead V1, the stimulus- left ventricular activation time left ventricular activation time (LVAT) interval were measured. After conversion of the ECG into vectorcardiogram (VCG) (Kors conversion matrix), QRS area and QRS vector in transverse plane (Azimuth) were determined. Results: QRS area significantly decreased from 82 ± 29 µVs during RV septal pacing (RVSP) to 46 ± 12 µVs during LVSP. In the subgroup where LBB capture was achieved (n = 31), QRS area significantly decreased from 46 ± 17 µVs during LVSP to 38 ± 15 µVs during LBBP, while LVAT was not significantly different between LVSP and LBBP. In patients with normal ventricular activation and narrow QRS, QRS area during LBBP was not significantly different from that during intrinsic activation (37 ± 16 vs. 35 ± 19 µVs, respectively). The Azimuth significantly changed from RVSP (−46 ± 33°) to LVSP (19 ± 16°) and LBBP (−22 ± 14°). The Azimuth during both LVSP and LBBP were not significantly different from normal ventricular activation. QRS area and LVAT correlated moderately (Spearman’s R = 0.58). Conclusions: ECG and VCG indices demonstrate that both LVSP and LBBP improve ventricular dyssynchrony considerably as compared to RVSP, to values close to normal ventricular activation. LBBP seems to result in a small, but significant, improvement in ventricular synchrony as compared to LVSP.
TL;DR: This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R-R wave intervals, QRS duration, and verification of P-wave whether it is present or absent using threshold technique and proves that this computer-aided method helps medical practitioners improve the efficiency of their services.
Abstract: Cardiac arrhythmias impose a significant burden on the healthcare environment due to the increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is a common form of cardiac arrhythmia which begins from the lower chamber of the heart, and frequent occurrence of PVC beat might lead to mortality. ECG signals are the noninvasive and primary tool used to identify the actual life threat related to the heart. Nowadays, in society, the computer-assisted technique reduces doctors' burden to evaluate heart disease and heart arrhythmia automatically. Regardless of well-equipped and well-developed health facilities that are available for monitoring the cardiac condition, the success stories are yet unsatisfactorily due to the complexity of the cardiac disorder. The most challenging part in ECG signal analysis is to extract the accurate features relevant to the arrhythmia for classification due to the inter-patient variation. There are many morphological changes present in the ECG signals. Hence, there is a gap in the usage of appropriate methods for the extraction of features and classification models, which reduce the biased diagnosis of PVC arrhythmia. To predict PVC arrhythmia accurately is a quite challenging task owing to (a) QRS negative (b) long compensatory pause (c) p-wave (d) biased diagnosis of PVC detection due to the small feature set. This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R–R wave interval, QRS duration, and verification of P-wave whether it is present or absent using threshold technique. We propose the machine learning-data mining MACDM integrated approach using five different models of multiple logistic regression and four classifiers, namely, Random Forest (RF), K-Nearest Neighbor (KNN), Support vector machine (SVM), and Naive Bayes (NB). The experiment was conducted on the public benchmark MIT-BIH-AR to evaluate the performance of our proposed MACDM technique. The multiple logistic regression models constructed as a function of all independent variables achieved an accuracy of 99.96%, sensitivity 98.9%, specificity 99.20%, PPV 99.25%, and Youden's index parameter 98.24%. Thus, it is proved that this computer-aided method helps our medical practitioners improve the efficiency of their services.
TL;DR: ATV3 is a simple and distinct electrocardiographic pattern indicative of a site of origin from the septal margin of LV summit and showed higher sensitivity, specificity, predictive value, and accuracy than validated ECG criteria for predicting successful ablation in the region of the anterior LV ostium.
TL;DR: In this paper, the feasibility, safety, and outcomes of permanent LBBP in infranodal atrioventricular block (AVB) and PICM patients were evaluated.
Abstract: His bundle pacing (HBP) can reverse left ventricular (LV) remodeling in patients with right ventricular (RV) pacing-induced cardimyopathy (PICM) but may be unable to correct infranodal atrioventricular block (AVB). Left bundle branch pacing (LBBP) results in rapid LV activation and may be able to reliably pace beyond the site of AVB. Our study was conducted to assess the feasibility, safety, and outcomes of permanent LBBP in infranodal AVB and PICM patients. Patients with infranodal AVB and PICM who underwent LBBP for cardiac resynchronization therapy (CRT) were included. Clinical evaluation and echocardiographic and electrocardiographic assessments were recorded at baseline and follow-up. Permanent LBBP upgrade was successful in 19 of 20 patients with a median follow-up duration of 12 months. QRS duration (QRSd) increased from 139.3 ± 28.0 ms at baseline to 176.2 ± 21.4 ms (P < 0.001) with right ventricular pacing (RVP) and was shortened to 120.9 ± 15.2 ms after LBBP (P < 0.001). The mean LBBP threshold was 0.7 ± 0.3 V at 0.4 ms at implant and remained stable during follow-up. The left ventricular ejection fraction (LVEF) increased from 36.3% ± 6.5% to 51.9% ± 13.0% (P < 0.001) with left ventricular end-systolic volume (LVESV) reduced from 180.1 ± 43.5 to 136.8 ± 36.7 ml (P < 0.001) during last follow-up. LBBP paced beyond the site of block, which results in a low pacing threshold with a high success rate in infranodal AVB patients. LBBP improved LV function with stable parameters over the 12 months, making it a reasonable alternative to cardiac resynchronization pacing via a coronary sinus lead in infranodal AVB and PICM patients.
TL;DR: In this article, a new peak detection algorithm was proposed based on median and moving average (MA) filtering, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE).
Abstract: Heartbeats are important aspects for the study of heart diseases in medical sciences as they provide vital information on heart disorders and abnormalities in heart rhythms. Each heartbeat provides a QRS complex in the electrocardiogram (ECG) which is centered at the R-peak. The analysis of ECG is hindered by low-frequency noise, high-frequency noise, interference from P and T waves, and changes in QRS morphology. This paper presents a new peak detection algorithm that can suppress the noise and adapt to changes in ECG signal morphology for better detection performance. The proposed algorithm is based on median and moving average (MA) filtering, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). The filters are first used in preprocessing to reduce unwanted noise and interference. The data is then divided into smaller segments and each segment is then analyzed using two distinct thresholds, a time axis (x-axis) threshold and an amplitude (y-axis) threshold. Next, the false peaks are eliminated resulting from any residue of noise using an average value of peak-to-peak interval. A post-processing stage is added to eliminate any peak that is detected twice and to search for missed low-amplitude peaks. The proposed method is tested on MIT-BIH arrhythmia and Fantasia databases and provides better results in comparison to several state-of-the-art methods in the field. The mean sensitivity, positive predictivity, and detection error rates for the proposed method are 99.82%, 99.88%, and 0.31%, respectively, for the MIT-BIH arrhythmia database and 99.92%, 99.90%, and 0.18%, respectively, for the Fantasia database.
TL;DR: In this article, a novel adaptive thresholding and template waveform was proposed to detect key components in an electrocardiogram (ECG) which plays a vital role in identifying cardiovascular diseases.
TL;DR: LBBP is a safe and effective strategy of physiological pacing and can effectively overcome the limitations of HBP, and the pacing parameters remained stable over a period of 12 months follow-up.
Abstract: His bundle pacing (HBP) has evolved as the most physiological form of pacing but associated with limitations. Recently, left bundle branch pacing (LBBP) is emerging as an effective alternative strategy for HBP. Our study was designed to assess the feasibility, efficacy, electrophysiological parameters, and mid-term outcomes of LBBP in Indian population. All patients requiring permanent pacemaker implantation for symptomatic bradycardia and heart failure were prospectively enrolled. Echocardiography, QRS duration, pacing parameters, left bundle (LB) potentials, paced QRS duration, and peak left ventricular activation time (pLVAT) were recorded. LBBP was successful in 93 out of 99 patients (94% acute success). Mean age was 62.6 ± 13 years, male 59%, diabetes 69%, and coronary artery disease 65%. Follow-up duration was 4.8 months (range1–12 months). Indication for pacing included atrioventricular (AV) block 43%, cardiac resynchronization therapy 44%, and AV node ablation 4%. LB potential was noted in 37 patients (40%). QRS duration reduced from 144.38 ± 34.6 at baseline to 110.8 ± 12.4 ms after LBBP (p < 0.0001). Pacing threshold was 0.59 ± 0.22 V and sensed R wave 14.14 ± 7.19 mV, and it remained stable during follow-up. Lead depth in the septum was 9.62 mm. LV ejection fraction increased from 44.96 to 53.3% after LBBP (p < 0.0001). One died due to respiratory tract infection on follow up. LBBP is a safe and effective strategy (94% acute success) of physiological pacing. The pacing parameters remained stable over a period of 12 months follow-up. LBBP can effectively overcome the limitations of HBP.
TL;DR: This study demonstrates that compared to RVOP, LBBP can increase left ventricular early diastolic function, improve BNP levels, and has a tendency to increaseleft atrial myocardial elasticity and left atrial strain capacity in the short term in pacemaker-dependent patients.
TL;DR: In this paper, the authors discuss the current knowledge of left bundle branch pacing (LBBP) and discuss its feasibility and safety, with rare complications and high success rate, and discuss the potential of LBBP as a potential alternative pacing modality for both RVAP and cardiac resynchronization therapy with HBP or BVP.
Abstract: Cardiac pacing is an effective therapy for treating patients with bradycardia due to sinus node dysfunction or atrioventricular block. However, traditional right ventricular apical pacing (RVAP) causes electric and mechanical dyssynchrony, which is associated with increased risk for atrial arrhythmias and heart failure. Therefore, there is a need to develop a physiological pacing approach that activates the normal cardiac conduction and provides synchronized contraction of ventricles. Although His bundle pacing (HBP) has been widely used as a physiological pacing modality, it is limited by challenging implantation technique, unsatisfactory success rate in patients with wide QRS wave, high pacing capture threshold, and early battery depletion. Recently, the left bundle branch pacing (LBBP), defined as the capture of left bundle branch (LBB) via transventricular septal approach, has emerged as a newly physiological pacing modality. Results from early clinical studies have demonstrated LBBP's feasibility and safety, with rare complications and high success rate. Overall, this approach has been found to provide physiological pacing that guarantees electrical synchrony of the left ventricle with low pacing threshold. This was previously specifically characterized by narrow paced QRS duration, large R waves, fast synchronized left ventricular activation, and correction of left bundle branch block. Therefore, LBBP may be a potential alternative pacing modality for both RVAP and cardiac resynchronization therapy with HBP or biventricular pacing (BVP). However, the technique's widespread adaptation needs further validation to ascertain its safety and efficacy in randomized clinical trials. In this review, we discuss the current knowledge of LBBP.
TL;DR: In ventricular rate refractory AF patients with moderately reduced EF and narrow QRS undergoing AVNA, HBP could be a conceivable alternative to BiV pacing.
Abstract: Background: His bundle pacing (HBP) is a physiological alternative to biventricular (BiV) pacing. We compared short-term results of both pacing approaches in symptomatic atrial fibrillation (AF) patients with moderately reduced left ventricular (LV) ejection fraction (EF ≥35% and <50%) and narrow QRS (≤120 ms) who underwent atrioventricular node ablation (AVNA).Methods: Thirty consecutive AF patients who received BiV pacing or HBP in conjunction with AVNA between May 2015 and January 2020 were retrospectively assessed. Electrocardiographic, echocardiographic, and clinical data at baseline and 6 months after the procedure were assessed.Results: Twenty-four patients (age 68.8 ± 6.5 years, 50% female, EF 39.6 ± 4%, QRS 95 ± 10 ms) met the inclusion criteria, 12 received BiV pacing and 12 HBP. Both groups had similar acute procedure-related success and complication rates. HBP was superior to BiV pacing in terms of post-implant QRS duration, implantation fluoroscopy times, reduction of indexed LV volumes (EDVi 63.8 (49.6-81) mL/m2 vs. 79.9 (66-100) mL/m2, p = 0.055; ESVi 32.7 (25.6-42.6) mL/m2 vs. 46.4 (42.9-68.1) mL/m2, p = 0.009) and increase in LVEF (46 (41-55) % vs. 38 (35-42) %, p = 0.005). However, the improvement of the NYHA class was similar in both groups.Conclusions: In symptomatic AF patients with moderately reduced EF and narrow QRS undergoing AVNA, HBP could be a conceivable alternative to BiV pacing. Further prospective studies are warranted to address the outcomes between both 'ablate and pace' strategies.
26 Jul 2021
TL;DR: Progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves is reviewed and the potential impact of computer modeling on ECG interpretation is outlined.
Abstract: Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.
TL;DR: An algorithm to the diagnosis of LVH using ECG signal based on machine learning techniques were designed and revealed that the proposed work can diagnose LVH successfully using neural network classifiers.
Abstract: This work proposes a novel method for the detection of Left Ventricular Hypertrophy (LVH) from a multi-lead ECG signal. Left Ventricle walls become thick due to prolonged hypertension which may fail to pump heart effectively. The imaging techniques can be used as an alternative diagnose LVH; however, they are more expensive and time-consuming than proposed LVH. To overcome this issue, an algorithm to the diagnosis of LVH using ECG signal based on machine learning techniques were designed. In LVH detection, the pathological attributes such as R wave, S wave, inversion of QRS complex, changes in ST segment noticed in the ECG signal. This clinical information extracted as a feature by applying continuous wavelet transform. The signals were reconstructed with the frequency between 10 and 50 Hz from the wavelet. This followed by the detection of R wave and S wave peaks to obtain the relevant LVH diagnostic features. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Ensemble of Bagged Tree, AdaBoost classifiers were employed and the results are compared with four neural network classifiers including Multilayer Perceptron (MLP), Scaled Conjugate Gradient Backpropagation Neural Network (SCG NN), Levenberg–Marquardt Neural Network (LMNN) and Resilient Backpropagation Neural network (RPROP). The data source includes Left Ventricular Hypertrophy and healthy ECG signal from PTB diagnostic ECG database and St Petersburg INCART 12-Lead Arrhythmia Database. The results revealed that the proposed work can diagnose LVH successfully using neural network classifiers. The accuracy in detecting LVH is 86.6%, 84.4%, 93.3%,75.6%, 95.6%, 97.8%, 97.8%, 88.9% using SVM, KNN, Ensemble of Bagged Tree, AdaBoost, MLP, SCG NN, LMNN and RPROP classifiers, respectively.
TL;DR: A novel technique, i.e., fractional wavelet transform (FrWT) is proposed to be used as a feature extraction technique and results establish robustness of the proposed technique will go a long way in assisting the cardiologists in improving overall health care system in hospitals.
Abstract: An electrocardiogram (ECG) is an essential and fundamental diagnostic tool for assessing cardiac arrhythmias. It is mainly a combination of P, QRS, and T waves. But visual inspection of these waves may lead to wrong diagnosis due to physiological variability and noisy QRS complexes. Hence, computer-aided diagnosis (CAD) is required for accurate and efficient diagnosis of the clinical information. Therefore, in this paper, a novel technique, i.e., fractional wavelet transform (FrWT) is proposed to be used as a feature extraction technique. Afterward, Probabilistic Principal Component Analysis (PPCA) and K-Nearest Neighbor (KNN) are jointly used as classification (i.e., detection of R-peaks) tools for diagnosing heart abnormalities in various morphologies of the ECG signal robustly. The proposed technique has been evaluated on the basis of sensitivity (Se), detection error rate (Der), and positive predictive value (Ppv) for records in the MIT-BIH Arrhythmia database (M/B Ar DB). The proposed technique yields Se of 99.98%, Der of 0.036%, and Ppv of 99.98% for M/B Ar DB. These results establish robustness of the proposed technique, which will go a long way in assisting the cardiologists in improving overall health care system in hospitals.