A new algorithm to diagnose atrial ectopic origin from multi lead ECG systems - insights from 3D virtual human atria and torso

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms

Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study.

Atrial tachy-arrhytmias, such as atrial fibrillation (AF), are characterised by irregular electrical activity in the atria, generally associated with erratic excitation underlain by re-entrant scroll waves, fibrillatory conduction of multiple wavelets or rapid focal activity. Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options. Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. The standard 12-lead electrocardiogram (ECG) provides a method for non-invasive identification of the presence of arrhythmia, due to irregularity in the ECG signal associated with atrial activation compared to sinus rhythm, but has limitations in providing specific spatial information. There is therefore a pressing need to develop novel methods to identify and locate the origin of arrhythmic excitation. Invasive methods provide direct information on atrial activity, but may induce clinical complications. Non-invasive methods avoid such complications, but their development presents a greater challenge due to the non-direct nature of monitoring. Algorithms based on the ECG signals in multiple leads (e.g. a 64-lead vest) may provide a viable approach. In this study, we used a biophysically detailed model of the human atria and torso to investigate the correlation between the morphology of the ECG signals from a 64-lead vest and the location of the origin of rapid atrial excitation arising from rapid focal activity and/or re-entrant scroll waves. A focus-location algorithm was then constructed from this correlation. The algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation with a spatial resolution of 40 mm, respectively. The general approach allows its application to any multi-lead ECG system. This represents a significant extension to our previously developed algorithms to predict the AF origins in association with focal activities

A Cognitive Grammatological Analysis of Joyo-kanji: Types and Distribution of Cognitive Information Units

Myocardial ventricular ischemia arises from the lack of blood supply to the heart, which may cause abnormal excitation wave conduction and repolarization patterns in the tissue, leading to cardiac arrhythmias and even sudden death. Current diagnosis of cardiac ischemia by the 12-lead electrocardiogram (ECG) has limitations as they are insensitive in many cases and may showunnoticeable differences compared to normal patterns. As the magnetic field provides extra information of cardiac excitation and is more sensitive to tangential currents to the surface of the chest, whereas the electric field is more sensitive to radial currents, it has been hypothesized that the magnetocardiogram (MCG) may provide a complementary methodto the ECG in ischemic diagnosis. However, it is unclear yet about the differences in the sensitivity of the ECG and MCG signals to ischemic conditions. The aim of this study was to investigate such differences by using multi-scale biophysically detailed computational models of the human ventricles and torso model, to simulate normal and ischemic conditions.CPCI-S(ISTP)[email protected]

Normalized power spectral density for ectopic focal and re-entrant activation.

<p>Normalized power spectral density for ectopic focal (blue) and re-entrant (red) activity located in (A) SAN; (B) PV and (C) RAA. The darker shadow corresponds to the area between 0–2 x DF. (D): a scatter plot of (AFFTr_2DF) against the DF, the magenta area is the overlapping area where both activities can occur. AFFTr_2DF is the ratio of the area under the power spectrum density in the ranges 0 –(2 x DF) Hz and (2 x DF)– 50 Hz: AFFTr_2DF = Area_0-2DF/Area_0-50Hz</p

Illustration of the correlation used by the algorithm for activation from three atrial sites.

<p>(A): Dipole sum (green line) (i), lead V1 (black line) (ii) and Lead 47 (Grey line)(iii), (i) were used to identify the time interval (section between dotted lines) of re-entrant patterns where the tip was located in the sino-atrial node (SAN), right atria (RA) and pulmonary veins (PV). The amplitude in all cases has been normalized. (B): Atrial-wave polarity map in the anterior (i) and posterior (ii) part of the torso for atrial activation initiated at different locations of the atria (SAN, RA and PV). A red sign represents a positive polarity in the atrial-wave, the blue sign is a negative polarity and a purple sign represents a biphasic atrial-wave. The black square represents the electrode position of lead V1, and the grey circle represents the electrode position of lead 47. (C): Rotor tip (red dot) identified by the algorithm in each simulation. The anterior (ii) and posterior (i) parts of the atria and torso (B-i,ii) are shown for each case. In each case the algorithm correctly identifies the correct quadrant: SAN the tip is located in the quadrant Qa5 (i), for RA the tip is located in the quadrant Qa2 (ii), and for PV the tip is located in the quadrant Qa7 (iii).</p

Models and procedure used to develop the algorithm.

<p>Illustration of atria (A) and torso (B) models used in the study to simulate re-entry and ectopic activity in the atria. (C) Electrode positions used to simulate the 64-lead ECG. (D) Simulated anterior (i) and posterior (ii) polarity map, as compared to experimental data, validating the 3D atria-torso models.</p

Dipole and atrial activation evolution in different atrial activations located in the pulmonary veins (PV).

<p>(A) Slow ectopic atrial activation focus in the PV (non fibrillatory waves observed). (B) Fast focal activation focus in the PV (F-waves observed). (C) Re-entrant activation around the PV (F-waves observed). Red line: Positive dipole. Blue line: Negative dipole. Green line: Dipole sum. Black line: lead V1 (ECG). The Magenta regions represent the time interval of the main atrial wave, selected from the peaks in the dipole pattern. (i)-(ii) Snapshots of the atria activation at the beginning and end of the time interval selected.</p

Schematic illustration of the algorithm to identify the location of atrial focal origin or re-entry from atrial wave polarity maps.

<p>Schematic illustration of the algorithm to identify the location of atrial focal origin or re-entry from atrial wave polarity maps.</p