Reduction in the QRS area after cardiac resynchronization therapy is associated with survival and echocardiographic response

Introduction Recent studies have shown that the baseline QRS area is associated with the clinical response after cardiac resynchronization therapy (CRT). In this study, we investigated the association of QRS area reduction ( increment QRS area) after CRT with the outcome. We hypothesize that a larger increment QRS area is associated with a better survival and echocardiographic response. Methods and Results Electrocardiograms (ECG) obtained before and 2-12 months after CRT from 1299 patients in a multi-center CRT-registry were analyzed. The QRS area was calculated from vectorcardiograms that were synthesized from 12-lead ECGs. The primary endpoint was a combination of all-cause mortality, heart transplantation, and left ventricular (LV) assist device implantation. The secondary endpoint was the echocardiographic response, defined as LV end-systolic volume reduction >= of 15%. Patients with increment QRS area above the optimal cut-off value (62 mu Vs) had a lower risk of reaching the primary endpoint (hazard ratio: 0.43; confidence interval [CI] 0.33-0.56, p = 109 mu Vs, survival, and echocardiographic response were better when the increment QRS area was >= 62 mu Vs (p = 109 mu Vs, increment QRS area was the only significant predictor of survival (OR: 0.981; CI: 0.967-0.994, p = .006). Conclusion increment QRS area is an independent determinant of CRT response, especially in patients with a large baseline QRS area. Failure to achieve a large QRS area reduction with CRT is associated with a poor clinical outcome

Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge

The relationship between ECG predictors of cardiac resynchronization therapy benefit.

IntroductionCardiac resynchronization therapy (CRT) is an effective treatment that reduces mortality and improves cardiac function in patients with left bundle branch block (LBBB). However, about 30% of patients passing the current criteria do not benefit or benefit only a little from CRT. Three predictors of benefit based on different ECG properties were compared: 1) "strict" left bundle branch block classification (SLBBB); 2) QRS area; 3) ventricular electrical delay (VED) which defines the septal-lateral conduction delay. These predictors have never been analyzed concurrently. We analyzed the relationship between them on a subset of 602 records from the MADIT-CRT trial.Methods & resultsSLBBB classification was performed by two experts; QRS area and VED were computed fully automatically. High-frequency QRS (HFQRS) maps were used to inspect conduction abnormalities. The correlation between SLBBB and other predictors was R = 0.613, 0.523 and 0.390 for VED, QRS area in Z lead, and QRS duration, respectively. Scatter plots were used to pick up disagreement between the predictors. The majority of SLBBB subjects- 295 of 330 (89%)-are supposed to respond positively to CRT according to the VED and QRS area, though 93 of 272 (34%) non-SLBBB should also benefit from CRT according to the VED and QRS area.ConclusionSLBBB classification is limited by the proper setting of cut-off values. In addition, it is too "strict" and excludes patients that may benefit from CRT therapy. QRS area and VED are clearly defined parameters. They may be used to optimize biventricular stimulation. Detailed analysis of conduction irregularities with CRT optimization should be based on HFQRS maps

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification

Abstract Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154

Fully automated QRS area measurement for predicting response to cardiac resynchronization therapy

BACKGROUND: Cardiac resynchronization therapy (CRT) is an established treatment in patients with heart failure and conduction abnormalities. However, a significant number of patients do not respond to CRT. Currently employed criteria for selection of patients for this therapy (QRS duration and morphology) have several shortcomings. QRS area was recently shown to provide superior association with CRT response. However, its assessment was not fully automated and required the presence of an expert. OBJECTIVE: Our objective was to develop a fully automated method for the assessment of vector-cardiographic (VCG) QRS area from electrocardiographic (ECG) signals. METHODS: Pre-implantation ECG recordings (N = 864, 695 left-bundle-branch block, 589 men) in PDF files were converted to allow signal processing. QRS complexes were found and clustered into morphological groups. Signals were converted from 12‑lead ECG to 3‑lead VCG and an average QRS complex was built. QRS area was computed from individual areas in the X, Y and Z leads. Practical usability was evaluated using Kaplan-Meier plots and 5-year follow-up data. RESULTS: The automatically calculated QRS area values were 123 ± 48 μV.s (mean values and SD), while the manually determined QRS area values were 116 ± 51 ms; the correlation coefficient between the two was r = 0.97. The automated and manual methods showed the same ability to stratify the population (hazard ratios 2.09 vs 2.03, respectively). CONCLUSION: The presented approach allows the fully automatic and objective assessment of QRS area values. SIGNIFICANCE: Until this study, assessing QRS area values required an expert, which means both additional costs and a risk of subjectivity. The presented approach eliminates these disadvantages and is publicly available as part of free signal-processing software

Fully automated QRS area measurement for predicting response to cardiac resynchronization therapy

Background: Cardiac resynchronization therapy (CRT) is an established treatment in patients with heart failure and conduction abnormalities. However, a significant number of patients do not respond to CRT. Currently employed criteria for selection of patients for this therapy (QRS duration and morphology) have several shortcomings. QRS area was recently shown to provide superior association with CRT response. However, its assessment was not fully automated and required the presence of an expert. Objective: Our objective was to develop a fully automated method for the assessment of vector-cardiographic (VCG) QRS area from electrocardiographic (ECG) signals. Methods: Pre-implantation ECG recordings (N = 864, 695 left-bundle-branch block, 589 men) in PDF files were converted to allow signal processing. QRS complexes were found and clustered into morphological groups. Signals were converted from 12‑lead ECG to 3‑lead VCG and an average QRS complex was built. QRS area was computed from individual areas in the X, Y and Z leads. Practical usability was evaluated using Kaplan-Meier plots and 5-year follow-up data. Results: The automatically calculated QRS area values were 123 ± 48 μV.s (mean values and SD), while the manually determined QRS area values were 116 ± 51 ms; the correlation coefficient between the two was r = 0.97. The automated and manual methods showed the same ability to stratify the population (hazard ratios 2.09 vs 2.03, respectively). Conclusion: The presented approach allows the fully automatic and objective assessment of QRS area values. Significance: Until this study, assessing QRS area values required an expert, which means both additional costs and a risk of subjectivity. The presented approach eliminates these disadvantages and is publicly available as part of free signal-processing software

Ultra-high-frequency ECG volumetric and negative derivative epicardial ventricular electrical activation pattern

Abstract From precordial ECG leads, the conventional determination of the negative derivative of the QRS complex (ND-ECG) assesses epicardial activation. Recently we showed that ultra-high-frequency electrocardiography (UHF-ECG) determines the activation of a larger volume of the ventricular wall. We aimed to combine these two methods to investigate the potential of volumetric and epicardial ventricular activation assessment and thereby determine the transmural activation sequence. We retrospectively analyzed 390 ECG records divided into three groups-healthy subjects with normal ECG, left bundle branch block (LBBB), and right bundle branch block (RBBB) patients. Then we created UHF-ECG and ND-ECG-derived depolarization maps and computed interventricular electrical dyssynchrony. Characteristic spatio-temporal differences were found between the volumetric UHF-ECG activation patterns and epicardial ND-ECG in the Normal, LBBB, and RBBB groups, despite the overall high correlations between both methods. Interventricular electrical dyssynchrony values assessed by the ND-ECG were consistently larger than values computed by the UHF-ECG method. Noninvasively obtained UHF-ECG and ND-ECG analyses describe different ventricular dyssynchrony and the general course of ventricular depolarization. Combining both methods based on standard 12-lead ECG electrode positions allows for a more detailed analysis of volumetric and epicardial ventricular electrical activation, including the assessment of the depolarization wave direction propagation in ventricles

3-Dimensional ventricular electrical activation pattern assessed from a novel high-frequency electrocardiographic imaging technique: principles and clinical importance

The study introduces and validates a novel high-frequency (100-400 Hz bandwidth, 2 kHz sampling frequency) electrocardiographic imaging (HFECGI) technique that measures intramural ventricular electrical activation. Ex-vivo experiments and clinical measurements were employed. Ex-vivo, two pig hearts were suspended in a human-torso shaped tank using surface tank electrodes, epicardial electrode sock, and plunge electrodes. We compared conventional epicardial electrocardiographic imaging (ECGI) with intramural activation by HFECGI and verified with sock and plunge electrodes. Clinical importance of HFECGI measurements was performed on 14 patients with variable conduction abnormalities. From 3 × 4 needle and 108 sock electrodes, 256 torso or 184 body surface electrodes records, transmural activation times, sock epicardial activation times, ECGI-derived activation times, and high-frequency activation times were computed. The ex-vivo transmural measurements showed that HFECGI measures intramural electrical activation, and ECGI-HFECGI activation times differences indicate endo-to-epi or epi-to-endo conduction direction. HFECGI-derived volumetric dyssynchrony was significantly lower than epicardial ECGI dyssynchrony. HFECGI dyssynchrony was able to distinguish between intraventricular conduction disturbance and bundle branch block patients

3-Dimensional ventricular electrical activation pattern assessed from a novel high-frequency electrocardiographic imaging technique:principles and clinical importance

The study introduces and validates a novel high-frequency (100–400 Hz bandwidth, 2 kHz sampling frequency) electrocardiographic imaging (HFECGI) technique that measures intramural ventricular electrical activation. Ex-vivo experiments and clinical measurements were employed. Ex-vivo, two pig hearts were suspended in a human-torso shaped tank using surface tank electrodes, epicardial electrode sock, and plunge electrodes. We compared conventional epicardial electrocardiographic imaging (ECGI) with intramural activation by HFECGI and verified with sock and plunge electrodes. Clinical importance of HFECGI measurements was performed on 14 patients with variable conduction abnormalities. From 3 × 4 needle and 108 sock electrodes, 256 torso or 184 body surface electrodes records, transmural activation times, sock epicardial activation times, ECGI-derived activation times, and high-frequency activation times were computed. The ex-vivo transmural measurements showed that HFECGI measures intramural electrical activation, and ECGI-HFECGI activation times differences indicate endo-to-epi or epi-to-endo conduction direction. HFECGI-derived volumetric dyssynchrony was significantly lower than epicardial ECGI dyssynchrony. HFECGI dyssynchrony was able to distinguish between intraventricular conduction disturbance and bundle branch block patients