A Novel Statistical Model for Predicting the Efficacy of Vagal Nerve Stimulation in Patients With Epilepsy (Pre-X-Stim) Is Applicable to Different EEG Systems

Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). It remains to be seen, however, if this model can be applied in different clinical settings. Objective: To validate our model using EEG data acquired with a different recording system. Methods: We identified a validation cohort of eight patients implanted with VNS, whose preimplantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting). Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments. Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use

On the Time Course of Synchronization Patterns of Neuronal Discharges in the Human Brain during Cognitive Tasks

Using intracerebral EEG recordings in a large cohort of human subjects, we investigate the time course of neural cross-talk during a simple cognitive task. Our results show that human brain dynamics undergo a characteristic sequence of synchronization patterns across different frequency bands following a visual oddball stimulus. In particular, an initial global reorganization in the delta and theta bands (2–8 Hz) is followed by gamma (20–95 Hz) and then beta band (12–20 Hz) synchrony

Left Ventricular Myocardial Septal Pacing in Close Proximity to LBB Does Not Prolong the Duration of the Left Ventricular Lateral Wall Depolarization Compared to LBB Pacing

Background: Three different ventricular capture types are observed during left bundle branch pacing (LBBp). They are selective LBB pacing (sLBBp), non-selective LBB pacing (nsLBBp), and myocardial left septal pacing transiting from nsLBBp while decreasing the pacing output (LVSP). Study aimed to compare differences in ventricular depolarization between these captures using ultra-high-frequency electrocardiography (UHF-ECG). Methods: Using decremental pacing voltage output, we identified and studied nsLBBp, sLBBp, and LVSP in patients with bradycardia. Timing of ventricular activations in precordial leads was displayed using UHF-ECGs, and electrical dyssynchrony (e-DYS) was calculated as the difference between the first and last activation. The durations of local depolarizations (Vd) were determined as the width of the UHF-QRS complex at 50% of its amplitude. Results: In 57 consecutive patients, data were collected during nsLBBp (n = 57), LVSP (n = 34), and sLBBp (n = 23). Interventricular dyssynchrony (e-DYS) was significantly lower during LVSP 16 ms (21; 11), than nsLBBp 24 ms (28; 20) and sLBBp 31 ms (36; 25). LVSP had the same V1d-V8d as nsLBBp and sLBBp except for V3d, which during LVSP was shorter than sLBBp; the mean difference 9 ms (16; 1), p = 0.01. LVSP caused less interventricular dyssynchrony and the same or better local depolarization durations than nsLBBp and sLBBp irrespective of QRS morphology during spontaneous rhythm or paced QRS axis. Conclusions: In patients with bradycardia, LVSP in close proximity to LBB resulted in better interventricular synchrony than nsLBBp and sLBBp and did not significantly prolong depolarization of the left ventricular lateral wall

SMV-2018-19: Systém for ECG analysis

A subject of this contracional research is development of algorithms for automated ECG processing from 1-lead ECG holters. Namely, it consists of:\n- development of algorithm for QRS recognition with focus on robustness against noise\n- development of classification algorithm to recognize arrhythmias, the algorithm is based on analysis of RR intervals and other ECG descriptors. The algorithm implements machine learning (neural networks). The input is Information related to QRS complexes and other descriptors extracted from ECG. The output is category of classified ECG block (atrial fibrillation, AB-block, non-quality signal, premature atrial contractions, premature ventricular contractions, sinus rhythm, supraventricular tachycardia and ventricular tachycardia)\n- Implemetation of these algorithms as software for .NET platform in C# language. It is optimized for multi-thread computers (computing server of the customer).\

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

BACKGROUND Nonselective His-bundle pacing (nsHBp), nonselective left bundle branch pacing (nsLBBp), and left ventricular septal myocardial pacing (LVSP) are recognized as physiological pacing tech niques. OBJECTIVE The purpose of this study was to compare differences in ventricular depolarization between these techniques using ultrahigh-frequency electrocardiography (UHF-ECG). METHODS In patients with bradycardia, nsHBp, nsLBBp (confirmed concomitant left bundle branch [LBB] and myocardial capture), and LVSP (pacing in left ventricular [LV] septal position without proven LBB capture) were performed. Timings of ventricular activations in precordial leads were displayed using UHF-ECG, and electrical dyssynchrony (e-DYS) was calculated as the difference between the first and last activation. Duration of local depolarization (Vd) was determined as width of the UHF-QRS complex at 50% of its amplitude. RESULTS In 68 patients, data were collected during nsLBBp (35), LVSP (96), and nsHBp (55). nsLBBp resulted in larger e-DYS than did LVSP and nsH Bp [- 24 ms (-28;-19) vs -12 ms (-16;-9) vs 10 ms (7;14), respectively; P <.001]. nsLBBp produced similar values of Vd in leads V-5 -V-8 (36-43 ms vs 38-43 ms; P = NS in all leads) but longer Vd in leads V-1 -V-4 (47-59 ms vs 41-44 ms; P <.05) as nsH Bp. LVSP caused prolonged Vd in leads V-1 -V-8 compared to nsH Bp and longer Vd in leads V-5 -V-8 compared to nsLBBp (44-51 ms vs 36-43 ms; P <.05) regardless of R-wave peak time in lead V-5 or QRS morphology in lead V-1 present during LVSP. CONCLUSION nslbbp preserves physiological LV depolarization but increases interventricular electrical dyssynchrony. LV lateral wall depolarization during LVSP is prolonged, but interventricular synchrony is preserved

An example of spatial representation of post-stimulus interactions after targets between all investigated brain sites in one subject (No.7).

<p>Correlation results are arranged in the triangular matrices into groups according to brain structures (delimited by black lines)(D) and in graphic form of “glass brains” with linked pairs of investigated electrode contacts (A – Coronal, B – Sagittal, C – Axial). Matrix values and links are colored according to the percentage of duration of the increase (red) or decrease (blue) in cross-correlations within time window 250–750 ms after stimulation. Three selected frequency bands – δ (left panel), β (middle panel), and upper γ (right panel).</p

Relative incidences of significant post-stimulus changes in inter-areal cross-correlations (A – monopolar; B – bipolar) and post-stimulus power changes (C – monopolar; D – bipolar) across all investigated subjects.

<p>Statistical significance of target/frequent differences is indicated by asterisks.</p