OBJECTIVE:The upper frequency limit of human brain activity remains unknown. Using ultrahigh sampling rate (≥20 kHz) intracranial microelectroencephalography, this study aimed to systematically explore and quantitatively characterize brain field oscillations beyond the established high-frequency oscillation range (>2 kHz), and to determine their relationship to epileptogenic tissue. METHODS:We analyzed intracranial electroencephalographic recordings from 15 patients with drug-resistant epilepsy, comprising 466 microcontacts and 434 macrocontacts implanted in mesiotemporal structures across four international epilepsy centers. A custom spectrogram-based detector optimized for ultrahigh frequencies was developed to identify ultrafast oscillations (UFOs; >2 kHz). UFO rates were assessed in epileptic (successfully resected) and nonepileptic hippocampi and compared with established electrophysiological biomarkers, including interictal epileptiform discharges, ripples, and fast ripples. Statistical comparisons were performed using mixed-effects models to account for intersubject and interelectrode variability. RESULTS:We identified a previously undescribed class of short-duration oscillatory events spanning 2-8 kHz. UFOs occurred at significantly higher rates in epileptic compared with nonepileptic mesiotemporal regions, with the strongest differentiation observed in the 2-3-kHz band. Two distinct UFO phenotypes were consistently observed: (1) spindlelike, narrow-band oscillations and (2) sharp-onset, rapidly decaying bursts. Both forms were prevalent within epileptic hippocampi but were exceedingly rare in nonepileptic structures. UFOs exhibited pronounced temporal intermittency and spatial focality and frequently arose independently of ripples, fast ripples, and very high-frequency oscillations, indicating that they represent a distinct electrophysiological phenomenon. Macrocontacts detected UFOs only exceptionally, highlighting the necessity of microelectrode recordings to capture activity in this ultrahigh-frequency regime. SIGNIFICANCE:These findings substantially extend the known frequency range of human brain field activity and identify ultrafast oscillations as a novel biomarker of neuronal hyperexcitability. UFOs likely reflect pathological microcircuit dynamics inaccessible to conventional clinical recordings and provide new insights into the organization and pathophysiology of epileptogenic networks.

BackgroundPhysiological pacing targeting the cardiac conduction system is increasingly being adopted as an alternative to conventional right ventricular (RV) pacing for the treatment of bradyarrhythmias, although its effects on ventricular repolarization remain underexplored.ObjectiveThis study evaluates depolarization and repolarization responses to different pacing techniques using ultra-high-frequency electrocardiograms (UHF-ECGs).MethodsTemporary pacing was performed at different cardiac areas in 178 patients with bradycardia. Depolarization was assessed via QRS duration (QRSd), QRS area (QRSa), ventricular dyssynchrony (e-DYS), and activation time dispersion (dAT computed from leads V1-V6 and dAT4-6 from leads V1-V6). Repolarization was analyzed using the corrected QT interval (QTc), T-wave area (Ta), Periodic Repolarization Dynamics (PRD), and repolarization time dispersion (dRTc and dRTc4-6).ResultsHis bundle pacing (HBP) preserved ventricular activation patterns similar to spontaneous rhythm. Left bundle branch pacing (LBBP) induced moderate depolarization changes, primarily due to delayed right ventricular activation, while maintaining left ventricular synchrony. In particular, dAT showed no significant differences between HBP and spontaneous rhythm, while differences between LBBAP and spontaneous rhythm were significant but below 7 ms in median. When restricted to the left ventricle (LV), no significant differences in dAT4-6 were found between LBBAP and spontaneous rhythm. Importantly, e-DYS for HBP showed similar values to spontaneous rhythm, LBBP led to a significant reduction (median differences approximately 20 ms), and RVP was associated with a significant increase (above 15 ms in median). In line with these results, QRSd and QRSa showed the largest values for RVP. In terms of ventricular repolarization, median differences in the QTc interval between pacing modes and spontaneous rhythm were below 3 ms for HBP, above 1 ms for LBBP, and above 20 ms for RVP. All pacing modes led to a reduction in PRD, with the most marked reductions observed for LBBP, particularly for selective LBBP, with median changes with respect to spontaneous rhythm of 4.6 degrees. RT and RTc showed similar trends for all pacing techniques. Ta, however, showed median differences with respect to spontaneous rhythm above 100 and 34 [Formula: see text] Vs when pacing the RV at the apex and the septum, respectively, whereas such median differences were below 16 [Formula: see text] Vs for HBP and below 2 [Formula: see text] Vs for LBBP.ConclusionPhysiological stimulation via HBP or LBBAP generates ventricular depolarization and repolarization responses that more closely resembles that of spontaneous rhythm, in high contrast to the largely different response induced by RV pacing. HBP and LBBAP have distinct technical characteristics, including differences in capture thresholds, lead stability, and procedural aspects. These techniques serve as alternatives to conventional RV pacing.

BACKGROUND:Biventricular pacing improves morbidity and mortality in heart failure patients with electrical dyssynchrony. Yet, approximately one-third fail to respond when selection is based on QRS duration and morphology. OBJECTIVES:This study sought to determine if ultra-high-frequency electrocardiography (UHF-ECG) assesses ventricular dyssynchrony and may better predict cardiac resynchronization therapy (CRT) response. METHODS:In this prospective single-center study, 114 patients with heart failure, sinus rhythm, nonright bundle branch block morphology, LVEF ≤35%, and QRS duration >130 milliseconds were enrolled and followed for 6 months. Ventricular electrical dyssynchrony (e-DYS) was derived from UHF-ECG as the interval between earliest and latest activation in any of V1 to V7 leads, and its predictive performance was compared with QRS duration, QRS area, negative derivatve action time (NDAT) V8, and QRS morphology. CRT response was defined as a >15% reduction in left ventricular end-systolic volume. RESULTS:A total of 73 patients (64%) were responders. Responders more often had nonischemic cardiomyopathy, larger QRS area, longer QRS duration, and greater e-DYS. Optimal cutoffs for CRT response prediction were e-DYS >55 ms, QRS area >81 μVs, NDAT V8 >117 ms, and QRS duration >155 ms. In multivariable analysis, only e-DYS (OR: 1.02 per ms; P = 0.04) and nonischemic cardiomyopathy (OR: 2.8; P = 0.03) independently predicted response. In ischemic cardiomyopathy, e-DYS remained the sole independent predictor. Postimplant Δe-DYS (-37 ms) predicted response more accurately (AUC = 0.73, 95% CI: 0.63-0.83) than ΔQRS area or ΔQRS duration. CONCLUSIONS:UHF-ECG-derived ventricular dyssynchrony predicts response to CRT more accurately than conventional ECG parameters. Both baseline e-DYS and its postimplant reduction identify patients most likely to benefit from biventricular pacing.

Electrical cardioversion presents one of the treatment options for atrial fibrillation (AF). However, the early recurrence rate is high, reaching ~40% three months after the procedure. Features based on vectorcardiographic signals were explored to find association with early recurrence of AF. Eighty-four patients with non-paroxysmal AF referred to electrical cardioversion were prospectively studied; early AF recurrence was present in 40 (47.6%). Patients underwent 24-h Holter ECG monitoring three months after the procedure to assess AF recurrence. Pre-procedural 12-lead ECGs (10 s, 1 kHz) were recorded and automatically analyzed. We explored associations of VCG-based features with early AF recurrence. Two features were strongly associated with AF recurrence: (1) a mean VCG (y-axis) signal slope in a window starting 145 ms before QRS center, lasting for 190 ms (AUC 0.778, p < 0.001), and (2) a mean VCG (z-axis) signal slope in a window starting 60 ms after QRS center, lasting for 465 ms (AUC 0.744, p < 0.001). These features showed higher association to the outcome than eighteen baseline clinical features. Our approach revealed features based on a slope of vectorcardiographic signals. This work also suggests that state of ventricles strongly affects the AF recurrence after electrical cardioversion.

INTRODUCTION:Electrical cardioversion (ECV) remains a treatment option for atrial fibrillation (AF). The study aimed to find predictors of SR maintenance after ECV using spectral and vector cardiographic (VCG) analysis of ECGs. METHODS:Consecutive patients with AF referred for elective ECV were prospectively enrolled. A digital ECG recording was obtained before the ECV and was analyzed using spectral and VCG analysis. AF activity was analyzed using spectral analysis to determine the dominant frequency (DF), RI (regularity index), and OI (organizational index). QRS complexes were analyzed using vectorcardiography to determine the dXmean, dYmean, and dZmean (derivation of VCG signals). We used Lasso Logistic Regression (LLR) in five-fold cross-validation for feature selection and to build combined predictive models of SR maintenance. For model training and evaluation, data were split in a 60%-40% ratio for training and testing, respectively. RESULTS:A total of 80 patients were enrolled (age 70.2 ± 10.6 years, 49 (61%) were men, BMI 29.7 kg/m2). At the 3-month follow-up, AF recurrence was present in 36 patients (45%). The best single VCG parameter to predict SR maintenance was dZMean (OR 0.18, 95% CI 0.06-0.51, p < 0.001). VCG-domain parameters combined into the LLR model showed an area under the curve (AUC) of 0.78. From the spectral analysis domain, the best predictor was DF (OR 3.54, 95% CI 1.28-10.25), p = 0.006; spectral features led to an AUC of 0.76 when combined in the LLR model. Clinical features did not form a model since no features passed feature selection. Combining VCG and spectral analysis features led to an LLR model with an AUC of 0.79. CONCLUSION:The combination of spectral analysis of AF activity and VCG analysis of ventricular activity provided more accurate predictive information than either analysis alone.

Background Maternal perinatal mental health is essential for optimal brain development and mental health of the offspring. We evaluated whether maternal depression during the perinatal period and early life of the offspring might be selectively associated with altered brain function during emotion regulation and whether those may further correlate with physiological responses and the typical use of emotion regulation strategies.Methods Participants included 163 young adults (49% female, 28-30 years) from the ELSPAC prenatal birth cohort who took part in its neuroimaging follow-up and had complete mental health data from the perinatal period and early life. Maternal depressive symptoms were measured mid-pregnancy, 2 weeks, 6 months, and 18 months after birth. Regulation of negative affect was studied using functional magnetic resonance imaging, concurrent skin conductance response (SCR) and heart rate variability (HRV), and assessment of typical emotion regulation strategy.Results Maternal depression 2 weeks after birth interacted with sex and showed a relationship with greater brain response during emotion regulation in a right frontal cluster in women. Moreover, this brain response mediated the relationship between greater maternal depression 2 weeks after birth and greater suppression of emotions in young adult women (ab = 0.11, SE = 0.05, 95% CI [0.016; 0.226]). The altered brain response during emotion regulation and the typical emotion regulation strategy were also as sociated with SCR and HRV.Conclusions These findings suggest that maternal depression 2 weeks after birth predisposes female offspring to maladaptive emotion regulation skills and particularly to emotion suppression in young adulthood.

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.

Identifying electrical dyssynchrony is crucial for cardiac pacing and cardiac resynchronization therapy (CRT). The ultra-high-frequency electrocardiography (UHF-ECG) technique allows instantaneous dyssynchrony analyses with real-time visualization. This review explores the physiological background of higher frequencies in ventricular conduction and the translational evolution of UHF-ECG in cardiac pacing and CRT. Although high-frequency components were studied half a century ago, their exploration in the dyssynchrony context is rare. UHF-ECG records ECG signals from eight precordial leads over multiple beats in time. After initial conceptual studies, the implementation of an instant visualization of ventricular activation led to clinical implementation with minimal patient burden. UHF-ECG aids patient selection in biventricular CRT and evaluates ventricular activation during various forms of conduction system pacing (CSP). UHF-ECG ventricular electrical dyssynchrony has been associated with clinical outcomes in a large retrospective CRT cohort and has been used to study the electrophysiological differences between CSP methods, including His bundle pacing, left bundle branch (area) pacing, left ventricular septal pacing and conventional biventricular pacing. UHF-ECG can potentially be used to determine a tailored resynchronization approach (CRT through biventricular pacing or CSP) based on the electrical substrate (true LBBB vs. non-specified intraventricular conduction delay with more distal left ventricular conduction disease), for the optimization of CRT and holds promise beyond CRT for the risk stratification of ventricular arrhythmias.

Abstract Background An ultra-high-frequency ECG (UHF-ECG) technique, recently introduced, provides ventricular activation patterns and identifies electrical dyssynchrony. UHF-ECG interpretation assumes that the ECG amplitude decreases rapidly at higher frequencies with increasing distance from the source. Based on this property, nearby sources can be distinguished. Unfortunately, this assumption has never been experimentally proven. Purpose The study aims to show whether there is a decrease in ECG amplitude with distance from the source and whether this decrease is frequency-dependent. Methods UHF-ECG resting recordings were acquired in 106 subjects. We used a standard electrode setup for a 12-lead ECG. In addition, we recorded UHF-ECG from six extended precordial electrodes. With the help of magnetic resonance images, we measured the distance of all precordial leads to the ventricular geometrical center. For each lead, we determined the amplitude of frequency components by computing the area of the averaged amplitude envelopes in the region of the QRS complex. We analyzed five frequency bands: LF (0.2-20 Hz), MF (20-80 Hz), HF (80-300 Hz), UHF1 (300-500 Hz), and UHF2 (800-1000 Hz). Results The left panel in the figure shows the relative amplitude decrease in five frequency bands with relative distance from the depolarization source. The decay coefficient in the LF band is only 0.56 [0.84, 0.29] median [percentile 25,75] and gradually grows up to 1.23 [1.40, 0.98] in the UHF2 band, p&lt;0.005. It indicates that the amplitude of the ECG signal from distant areas of the ventricles in the LF band drops approximately 50 percent of the original value, and the amplitude in the UHF2 band drops to only 20 percent of the original value. The right panel demonstrates an amplitude decrease with distance and frequency in the background of heart geometry. The circle's area corresponds to the relative signal intensity recorded in a particular body surface electrode V1, V4, and V6 from RV, LV, and septal ventricular segments. The circle marked in red has a size of 1 and is at the smallest distance from the electrode. The right panel includes the LF band (regular ECG) and the UHF2 band of 800-1000 Hz. Conclusion The results show significant differences in ECG signal decay between frequency bands and the ability to localize nearby sources using UHF-ECG. Verifying the principle of frequency dependence of ECG properties opens possibilities for a more accurate description of activation patterns. Combining multiple frequency bands (broadband ECG) has the potential to distinguish the activation of near and distant sources.Different decrease in ECG amplitude

Abstract Introduction Electrical cardioversion (ECV) still presents a treatment option for atrial fibrillation (AF). Despite the great success rate in the acute setting, its long-term success rate is low. The aim of the study was to find predictors of SR maintenance after ECV using spectral and vectorcardiographic (VCG) analysis of the ECG curve. Methods Consecutive patients with AF referred for elective ECV were prospectively enrolled. The digital ECG recording obtained before the ECV was analyzed using spectral and VCG analysis. All patients had Holter monitoring and clinical follow-up three months after cardioversion to assess SR maintenance. Atrial fibrillation activity was analyzed by spectral analysis, determining the dominant frequency (DF), RI (regularity index) and OI (organization index). QRS complexes were analyzed by vectorcardiography and dXmean, dYmean and dZmean (derivation of VCG signals) parameters were determined. Using logistic regression (LR) with backward parameter reduction, separate and combined predictive models of SR maintenance were calculated based on both the analysis from the VCG data and the spectral analysis data. Additional models with a forward selection of clinical variables were also calculated. The efficiency of the models was evaluated on a separate test subset of the data (N=32). Results A total of 80 patients were enrolled (age 70+10 years, 48 (64%) men, CHADSVASc 3.17+1.5, BMI 29.75kg/m2). At the 3-month follow-up, AF recurrence was present in 36 patients (45%). Using only vectorcardiographic parameters, dXmean (OR 3.07, 95 %CI 1.14- 8.63), p=0.001) and dYmean (OR 0.26, 95% CI 0.09-0.72), p <0.001) best predicted the SR maintenance (AUC of 0.72). Using only spectral analysis parameters, the best predictor was DF (OR 3.07, 95%CI 1.14-8.63), p=0.012) with AUC of 0.66. By combining the results of spectral analysis and vectorcardiography, an AUC of 0.78 was achieved. Among the clinical parameters, the most significant predictors were insufficiently controlled arterial hypertension and previous history of stroke. Conclusion Digital analysis of ECG curves using deeper mathematical models provides a better predictive model than classical clinical and echocardiographic models. The combination of spectral analysis of fibrillation atrial activity and vectorocardiographic analysis of QRS complexes revealed more accurate predictive information compared to both analyses alone.