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.
View Article and Find Full Text PDFFrom 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.
View Article and Find Full Text PDFThe current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data.We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification.
View Article and Find Full Text PDFBiventricular pacing (Biv) and left bundle branch area pacing (LBBAP) are methods of cardiac resynchronization therapy (CRT). Currently, little is known about how they differ in terms of ventricular activation. This study compared ventricular activation patterns in left bundle branch block (LBBB) heart failure patients using an ultra-high-frequency electrocardiography (UHF-ECG).
View Article and Find Full Text PDFBackground: Left bundle branch pacing (LBBP) produces delayed, unphysiological activation of the right ventricle. Using ultra-high-frequency electrocardiography (UHF-ECG), we explored how bipolar anodal septal pacing with direct LBB capture (aLBBP) affects the resultant ventricular depolarization pattern.
Methods: In patients with bradycardia, His bundle pacing (HBP), unipolar nonselective LBBP (nsLBBP), aLBBP, and right ventricular septal pacing (RVSP) were performed.
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.
View Article and Find Full Text PDFTelemedicine can be defined as a health care service that, specifically in the field of diagnostics, employs remote transfer of a large volume of data from a large number of subjects at the same time. This data is subsequently processed on a central basis and returned to a large number of health care providers by whom the service was ordered on national or international level. In arrhythmology, telemedicine is used particularly in long-term ECG monitoring to diagnose arrhythmias and check out treatment outcome via external recorders, smart watch, and implantable devices.
View Article and Find Full Text PDFWhile various QRS detection and classification methods were developed in the past, the Holter ECG data acquired during daily activities by wearable devices represent new challenges such as increased noise and artefacts due to patient movements. Here, we present a deep-learning model to detect and classify QRS complexes in single-lead Holter ECG. We introduce a novel approach, delivering QRS detection and classification in one inference step.
View Article and Find Full Text PDFDuring 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.
View Article and Find Full Text PDFThree 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).
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFIdentifying 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).
View Article and Find Full Text PDFBackground: Nonselective His-bundle pacing (nsHBp), nonselective left bundle branch pacing (nsLBBp), and left ventricular septal myocardial pacing (LVSP) are recognized as physiological pacing techniques.
Objective: The purpose of this study was to compare differences in ventricular depolarization between these techniques using ultra-high-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.
Background: Right ventricular (RV) pacing causes delayed activation of remote ventricular segments. We used the ultra-high-frequency ECG (UHF-ECG) to describe ventricular depolarization when pacing different RV locations.
Methods: In 51 patients, temporary pacing was performed at the RV septum (mSp); further subclassified as right ventricular inflow tract (RVIT) and right ventricular outflow tract (RVOT) for septal inflow and outflow positions (below or above the plane of His bundle in right anterior oblique), apex, anterior lateral wall, and at the basal RV septum with nonselective His bundle or RBB capture (nsHBorRBBp).
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 (∆QRS area) after CRT with the outcome. We hypothesize that a larger ∆QRS area is associated with a better survival and echocardiographic response.
View Article and Find Full Text PDFEEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable.
View Article and Find Full Text PDFBackground: Right ventricular myocardial pacing leads to nonphysiological activation of heart ventricles. Contrary to this, His bundle pacing preserves their fast activation. Ultra-high-frequency electrocardiography (UHF-ECG) is a novel tool for ventricular depolarization assessment.
View Article and Find Full Text PDFIntroduction: 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).
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.
View Article and Find Full Text PDFIntroduction: Cardiac 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.
View Article and Find Full Text PDFManual 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.
View Article and Find Full Text PDFUnlabelled: 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.
View Article and Find Full Text PDF