Toward a novel semi-invasive activation mapping tool for the diagnosis of supraventricular arrhythmias from the esophagus.

AIMS: Supraventricular arrhythmia diagnosis using the surface electrocardiogram (sECG) is often cumbersome due to limited atrial signal quality. In some instances, use of esophageal electrocardiography (eECG) may facilitate the diagnosis. Here, we present a novel approach to reconstruct cardiac activation maps from eECG recordings. METHODS: eECGs and sECGs were recorded from 19 individuals using standard acquisition tools. From the recordings, algorithms were developed to estimate the esophageal ECG catheter's position and to reconstruct high-resolution mappings of the cardiac electric activity projected in the esophagus over time. RESULTS: Esophageal two-dimensional activation maps were created for five healthy individuals and 14 patients suffering from different arrhythmias. The maps are displayed as time-dependent contour plots, which not only show voltage over time as conventional ECGs, but also the location, direction, and projected propagation speed of the cardiac depolarization wavefront in the esophagus. Representative examples of sinus rhythm, atrial flutter, and ventricular pre-excitation are shown. CONCLUSION: The methodology presented in this report provides a high-resolution view of the cardiac electric field in the esophagus. It is the first step toward a three-dimensional mapping system, which shall be able to reconstruct a three-dimensional view of the cardiac activation from recordings within the esophagus.

Markers for silent atrial fibrillation in esophageal long-term electrocardiography.

PURPOSE: Paroxysmal atrial fibrillation (PAF) often remains undiagnosed. Long-term surface ECG is used for screening, but has limitations. Esophageal ECG (eECG) allows recording high quality atrial signals, which were used to identify markers for PAF. METHODS: In 50 patients (25 patients with PAF; 25 controls) an eECG and surface ECG was recorded simultaneously. Partially A-V blocked atrial runs (PBARs) were quantified, atrial signal duration in eECG was measured. RESULTS: eECG revealed 1.8‰ of atrial premature beats in patients with known PAF to be PBARs with a median duration of 853ms (interquartile range (IQR) 813-1836ms) and a median atrial cycle length of 366ms (IQR 282-432ms). Even during a short recording duration of 2.1h (IQR 1.2-17.2h), PBARs occurred in 20% of PAF patients but not in controls (p=0.05). Left atrial signal duration was predictive for PAF (72% sensitivity, 80% specificity). CONCLUSIONS: eECG reveals partially blocked atrial runs and prolonged left atrial signal duration - two novel surrogate markers for PAF.

Real-Time Feature Extraction From Electrocochleography With Impedance Measurements During Cochlear Implantation Using Linear State-Space Models.

Electrocochleography (ECochG) is increasingly used to monitor the inner ear function of cochlear implant (CI) patients during surgery. Current ECochG-based trauma detection shows low sensitivity and specificity and depends on visual analysis by experts. Trauma detection could be improved by including electric impedance data recorded simultaneously with the ECochG. However, combined recordings are rarely used because the impedance measurements produce artifacts in the ECochG. In this study, we propose a framework for automated real-time analysis of intraoperative ECochG signals using Autonomous Linear State-Space Models (ALSSMs). We developed ALSSM based algorithms for noise reduction, artifact removal, and feature extraction in ECochG. Feature extraction includes local amplitude and phase estimations and a confidence metric over the presence of a physiological response in a recording. We tested the algorithms in a controlled sensitivity analysis using simulations and validated them with real patient data recorded during surgeries. The results from simulation data show that the ALSSM method provides improved accuracy in the amplitude estimation together with a more robust confidence metric of ECochG signals compared to the state-of-the-art methods based on the fast Fourier transform (FFT). Tests with patient data showed promising clinical applicability and consistency with the findings from the simulations. We showed that ALSSMs are a valid tool for real-time analysis of ECochG recordings. Removal of artifacts using ALSSMs enables simultaneous recording of ECochG and impedance data. The proposed feature extraction method provides the means to automate the assessment of ECochG. Further validation of the algorithms in clinical data is needed.

A graphical simulation software for instruction in cardiovascular mechanics physiology.

BACKGROUND: Computer supported, interactive e-learning systems are widely used in the teaching of physiology. However, the currently available complimentary software tools in the field of the physiology of cardiovascular mechanics have not yet been adapted to the latest systems software. Therefore, a simple-to-use replacement for undergraduate and graduate students' education was needed, including an up-to-date graphical software that is validated and field-tested. METHODS: Software compatible to Windows, based on modified versions of existing mathematical algorithms, has been newly developed. Testing was performed during a full term of physiological lecturing to medical and biology students. RESULTS: The newly developed CLabUZH software models a reduced human cardiovascular loop containing all basic compartments: an isolated heart including an artificial electrical stimulator, main vessels and the peripheral resistive components. Students can alter several physiological parameters interactively. The resulting output variables are printed in x-y diagrams and in addition shown in an animated, graphical model. CLabUZH offers insight into the relations of volume, pressure and time dependency in the circulation and their correlation to the electrocardiogram (ECG). Established mechanisms such as the Frank-Starling Law or the Windkessel Effect are considered in this model. The CLabUZH software is self-contained with no extra installation required and runs on most of today's personal computer systems. CONCLUSIONS: CLabUZH is a user-friendly interactive computer programme that has proved to be useful in teaching the basic physiological principles of heart mechanics.

A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography.

Long-term electrocardiogram (ECG) signals might suffer from relevant baseline disturbances during physical activity. Motion artifacts in particular are more pronounced with dry surface or esophageal electrodes which are dedicated to prolonged ECG recording. In this paper we present a method called baseline wander tracking (BWT) that tracks and rejects strong baseline disturbances and avoids concurrent saturation of the analog front-end. The proposed algorithm shifts the baseline level of the ECG signal to the middle of the dynamic input range. Due to the fast offset shifts, that produce much steeper signal portions than the normal ECG waves, the true ECG signal can be reconstructed offline and filtered using computationally intensive algorithms. Based on Monte Carlo simulations we observed reconstruction errors mainly caused by the non-linearity inaccuracies of the DAC. However, the signal to error ratio of the BWT is higher compared to an analog front-end featuring a dynamic input ranges above 15 mV if a synthetic ECG signal was used. The BWT is additionally able to suppress (electrode) offset potentials without introducing long transients. Due to its structural simplicity, memory efficiency and the DC coupling capability, the BWT is dedicated to high integration required in long-term and low-power ECG recording systems.

Graphics-processor-unit-based parallelization of optimized baseline wander filtering algorithms for long-term electrocardiography.

Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here, we present a graphics processor unit (GPU)-based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to autoregressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and four times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a seven-day high-resolution ECG is computed within less than 3 s. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.