The left bundle branch pacing compared to left ventricular septal myocardial pacing increases interventricular dyssynchrony but accelerates left ventricular lateral wall depolarization,

PurposeThe aim of this proof-of-concept study is to introduce new high-dynamic ECG technique with potential to detect temporal-spatial distribution of ventricular electrical depolarization and to assess the level of ventricular dyssynchrony.Methods5-kHz 12-lead ECG data was collected. The amplitude envelopes of the QRS were computed in an ultra-high frequency band of 500–1000 Hz and were averaged (UHFQRS). UHFQRS V lead maps were compiled, and numerical descriptor identifying ventricular dyssynchrony (UHFDYS) was detected.ResultsAn electrical UHFQRS maps describe the ventricular dyssynchrony distribution in resolution of milliseconds and correlate with strain rate results obtained by speckle tracking echocardiography. The effect of biventricular stimulation is demonstrated by the UHFQRS morphology and by the UHFDYS descriptor in selected examples.ConclusionsUHFQRS offers a new and simple technique for assessing electrical activation patterns in ventricular dyssynchrony with a temporal-spatial resolution that cannot be obtained by processing standard surface ECG. The main clinical potential of UHFQRS lies in the identification of differences in electrical activation among CRT candidates and detection of improvements in electrical synchrony in patients with biventricular pacing.Electronic supplementary materialThe online version of this article (doi:10.1007/s10840-017-0268-0) contains supplementary material, which is available to authorized users.

Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations. The method was trained and tested on data obtained from St Anne's University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.

Introduction: The present study introduces a new ultra-high-frequency 14-lead electrocardiogram technique (UHF-ECG) for mapping ventricular depolarization patterns and calculation of novel dyssynchrony parameters that may improve the selection of patients and application of cardiac resynchronization therapy (CRT).Methods: Components of the ECG in sixteen frequency bands within the 150 to 1000 Hz range were used to create ventricular depolarization maps. The maximum time difference between the UHF QRS complex centers of mass of leads V1 to V8 was defined as ventricular electrical dyssynchrony (e-DYS), and the duration at 50% of peak voltage amplitude in each lead was defined as the duration of local depolarization (Vd). Proof of principle measurements was performed in seven patients with left (left bundle branch block) and four patients with right bundle branch block (right bundle branch block) before and during CRT using biventricular and His-bundle pacing.Results: The acquired activation maps reflect the activation sequence under the tested conditions. e-DYS decreased considerably more than QRS duration, during both biventricular pacing (−50% vs −8%) and His-bundle pacing (−77% vs −13%). While biventricular pacing slightly increased Vd, His-bundle pacing reduced Vd significantly (+11% vs −36%), indicating the contribution of the fast conduction system. Optimization of biventricular pacing by adjusting VV-interval showed a decrease of e-DYS from 102 to 36 ms with only a small Vd increase and QRS duration decrease. Conclusions: The UHF-ECG technique provides novel information about electrical activation of the ventricles from a standard ECG electrode setup, potentially improving the selection of patients for CRT and application of CRT. K E Y W O R D S biventricular pacing, cardiac resynchronization therapy, His-bundle pacing, ultra-high-frequency ECG, ventricular electrical dyssynchrony

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