Atrial fibrillation can be treated using low voltage (LV) (amplitude of intracardiac electrogram < 0.5mV) targeted ablation. However, catheter characteristics can alter the voltage leading to changes in identified LV areas. This study evaluates the impact electrode size has on the voltage in healthy and diseased tissue. A realistic setup was generated of tissue, bath and two high conductivity electrodes, with centre to centre spacing of 2mm, placed in contact to the tissue and perpendicular to the planar wavefront. Simulations were performed varying the dimensions of the cubic electrodes from 0.2 to 1.6 mm in healthy tissue and including fibrosis in different locations. An inverse relationship was found between the electrode size and the voltage. When including epicardial fibrosis, a voltage decrease of 1 mV was found in electrodes. When fibrosis was placed closer to the electrodes, a morphological signal change was seen and a 9 mV drop in voltage for small electrodes. Large electrodes deliver smaller voltages. A fibrotic area on the epicardial side has a small influence on the voltage, which was not amplified by increasing electrode size. Endocardial fibrosis delivers significantly smaller voltages than healthy tissue. Little difference in the voltage was seen between large electrodes (> 1 mm) in diseased tissue. Electrode size needs to be accounted for when determining LV areas using different catheters.
Due to the cost-efficiency of the ECG, the interest in noninvasive techniques to assess atrial fibrillation (AF) electrophysiological complexity is increasingly high. Still, ECG-based methods to measure AF complexity are limited in clinical practice and need estimation of the atrial activity (AA) signal from sufficiently long ECG recordings. The present work proposes an algorithm for tensor decomposition called constrained alternating group lasso (CAGL) as a noninvasive tool to quantify AF complexity. Experiments with a database of 59 ECG recordings from 20 patients suffering from persistent AF show that CAGL is able to both extract the AA and quantify its complexity from very short ECG recordings (1.06 ± 0.20 s). All the patients had undergone step-wise catheter ablation (CA) that ended in procedural AF termination. CAGL is applied on the ECG recording before CA and at each step procedure, measuring the rank of the tensor that provides the AA signal. It is observed that such rank decreases at each step of the CA procedure, showing a less complex AA signal as the ablation is performed. A statistical correlation between AA complexity measured by the new index and AF recurrence after CA is observed. The proposed index is a potential tool to guide CA procedures in real time.
We recently proposed a Sympathetic Activity Index (SAI) through an orthonormal Laguerre expansion of linear RRinterval autoregressive kernels.The resulting heart rate variability (HRV) instantaneous indices may be used for the effective estimation of cardiac sympathetic outflow because the model is independent from the overlapping dynamics of the sympathetic and vagus nerves in the low frequency (LF) band (0.04-0.15Hz).In this study, we perform a preliminary validation of the SAI performance through concurrent estimates from efferent muscle sympathetic nerve activity (MSNA) recordings.ECG and MSNA were simultaneously recorded in 12 hypertensive patients during a 10min resting state in the supine position and up to 30min sodium nitroprusside (SNP, 0.4 µg/kg per minute) administration.Results show a characteristic increase of the MSNA during SNP intake with respect to the resting state (p< 0.001).While SAI was associated with a significant increase during SNP (p<0.001),LF power did not show significant changes between sessions (p>0.05).Spearman analysis highlighted a significant correlation between SAI and MSNA (r=0.47;p<0.02) and a nonsignificant correlation between LF power and MSNA (r=-0.09;p>0.05).This study provides a further validation step of the SAI and supports the use of HRV parameters as a reliable proxy of cardiac sympathetic outflow dynamics.
Cardiac abnormalities are a leading cause of death and their early diagnosis are of importance for providing timely interventions. The goal of 2020 PhysioNetlCinC challenge was to develop algorithms to diagnose multiple cardiac abnormalities using 12-lead ECG data. In this work, we develop a wide and deep transformer neural network to classify each 12-lead ECG sequence into 27 cardiac abnormality classes. Our approach combines handcrafted ECG features, which were determined to be important by a random forest model, and discriminative feature representations that are automatically learned from a transformer neural network. Our entry to the 2020 Phys-ioN etlCinC challenge placed 1st out of 41 official ranking teams (team name = prna). Using the official generalized weighted accuracy metric for evaluation, we achieved a validation score of 0.587 and top score of 0.533 on the full held-out test set.
Electrocardiogram (ECG) analysis is the standard of care for the diagnosis of irregular heartbeat patterns, known as arrhythmias. This paper presents a deep learning system for the automatic detection and multilabel classification of arrhythmias in ECG recordings. Our system composes three differentiable operators: a scattering transform (ST), a depthwise separable convolutional network (DSC), and a bidirectional long short-term memory network (BiLSTM). The originality of our approach is that all three operators are implemented in Python. This is in contrast to previous publications, which precomputed ST coefficients in MATLAB. The implementation of ST on Python was made possible by using a new software library for scattering transform named Kymatio. This paper presents the first successful application of Ky-matio to the analysis of biomedical signals. As part of the PhysioNet/Computing in Cardiology Challenge 2020, we trained our hybrid Scattering-LSTM model to classify 27 cardiac arrhythmias from two databases of 12-lead ECGs: CPSC2018 and PTB-XL, comprising 32k recordings in total. Our team “Bitscattered” achieved a Challenge metric of 0.536±0.012 over ten folds of cross-validation but this result may be over-optimistic since we were not able to rank and score on the hidden test set.
