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.

• MeSH
• blokáda Tawarova raménka diagnóza   terapie   MeSH
• elektrokardiografie MeSH
• lidé MeSH
• srdeční resynchronizační terapie * MeSH
• srdeční selhání * diagnóza   terapie   MeSH
• vektorkardiografie MeSH
• výsledek terapie MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The automated detection of arrhythmia in a Holter ECG signal is a challenging task due to its complex clinical content and data quantity. It is also challenging due to the fact that Holter ECG is usually affected by noise. Such noise may be the result of the regular activity of patients using the Holter ECG-partially unplugged electrodes, short-time disconnections due to movement, or disturbances caused by electric devices or infrastructure. Furthermore, regular patient activities such as movement also affect the ECG signals and, in connection with artificial noise, may render the ECG non-readable or may lead to misinterpretation of the ECG. OBJECTIVE: In accordance with the PhysioNet/CinC Challenge 2017, we propose a method for automated classification of 1-lead Holter ECG recordings. APPROACH: The proposed method classifies a tested record into one of four classes-'normal', 'atrial fibrillation', 'other arrhythmia' or 'too noisy to classify'. It uses two machine learning methods in parallel. The first-a bagged tree ensemble (BTE)-processes a set of 43 features based on QRS detection and PQRS morphology. The second-a convolutional neural network connected to a shallow neural network (CNN/NN)-uses ECG filtered by nine different filters (8×  envelograms, 1×  band-pass). If the output of CNN/NN reaches a specific level of certainty, its output is used. Otherwise, the BTE output is preferred. MAIN RESULTS: The proposed method was trained using a reduced version of the public PhysioNet/CinC Challenge 2017 dataset (8183 records) and remotely tested on the hidden dataset on PhysioNet servers (3658 records). The method achieved F1 test scores of 0.92, 0.82 and 0.74 for normal recordings, atrial fibrillation and recordings containing other arrhythmias, respectively. The overall F1 score measured on the hidden test-set was 0.83. SIGNIFICANCE: This F1 score led to shared rank #2 in the follow-up PhysioNet/CinC Challenge 2017 ranking.

• MeSH
• diagnóza počítačová metody   MeSH
• elektrokardiografie metody   MeSH
• fibrilace síní diagnóza   MeSH
• lidé MeSH
• počítačové zpracování signálu * MeSH
• rozpoznávání automatizované metody   MeSH
• strojové učení * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Although cardiac resynchronization therapy (CRT) is beneficial in heart failure patients with left bundle branch block, 30% of these patients do not respond to the therapy. Identifying these patients before implantation of the device is one of the current challenges in clinical cardiology. METHODS: We verified the diagnostic contribution and an optimized computerized approach to measuring ventricular electrical activation delay (VED) from body surface 12-lead ECGs. We applied the method to ECGs acquired before implantation (baseline) in the MADIT-CRT trial (Multicenter Automatic Defibrillator Implantation-Cardiac Resynchronization Therapy). VED values were dichotomized using its quartiles, and we tested the association of VED values with the MADIT-CRT primary end point of heart failure or death. Multivariate Cox proportional models were used to estimate the risk of study end points. In addition, the association between VED values and hemodynamic changes after CRT-D implantation was examined using 1-year follow-up echocardiograms. RESULTS: Our results showed that left bundle branch block patients with baseline VED <31.2 ms had a 35% risk of MADIT-CRT end points, whereas patients with VED ≥31.2 ms had a 14% risk (P<0.001). The hazard ratio for predicting primary end points in patients with low VED was 2.34 (95% confidence interval, 1.53-3.57; P<0.001). Higher VED values were also associated with beneficial hemodynamic changes. These strong VED associations were not found in the right bundle branch block and intraventricular conduction delay cohorts of the MADIT-CRT trial. CONCLUSIONS: Left bundle branch block patients with a high baseline VED value benefited most from CRT, whereas left bundle branch block patients with low VED did not show CRT benefits.

• MeSH
• akční potenciály * MeSH
• blokáda Tawarova raménka diagnóza   mortalita   patofyziologie   terapie   MeSH
• časové faktory MeSH
• defibrilátory implantabilní * MeSH
• elektrická defibrilace škodlivé účinky   přístrojové vybavení   mortalita   MeSH
• elektrokardiografie * MeSH
• klinické rozhodování MeSH
• lidé středního věku MeSH
• lidé MeSH
• multicentrické studie jako téma MeSH
• obnova funkce MeSH
• prediktivní hodnota testů MeSH
• randomizované kontrolované studie jako téma MeSH
• retrospektivní studie MeSH
• senioři MeSH
• srdeční frekvence MeSH
• srdeční resynchronizační terapie * škodlivé účinky   mortalita   MeSH
• srdeční selhání diagnóza   mortalita   patofyziologie   terapie   MeSH
• výběr pacientů MeSH
• výsledek terapie MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH