Automatic measurement of short-term variability of repolarization to indicate ventricular arrhythmias in a porcine model of cardiac ischaemia

AIMS

An automated method for determination of short-term variability (STV) of repolarization on intracardiac electrograms (STV-ARIauto) has previously been developed for arrhythmic risk monitoring by cardiac implantable devices, and has proved effective in predicting ventricular arrhythmias (VA) and guiding preventive high-rate pacing (HRP) in a canine model. Current study aimed to assess (i) STV-ARIauto in relation to VA occurrence and secondarily (ii-a) to confirm the predictive capacity of STV from the QT interval and (ii-b) explore the effect of HRP on arrhythmic outcomes in a porcine model of acute myocardial infarction (MI).

METHODS AND RESULTS

Myocardial infarction was induced in 15 pigs. In 7/15 pigs, STV-QT was assessed at baseline, occlusion, 1 min before VA, and just before VA. Eight of the 15 pigs were additionally monitored with an electrogram catheter in the right ventricle, underwent echocardiography at baseline and reperfusion, and were randomized to paced or control group. Paced group received atrial pacing at 20 beats per min faster than sinus rhythm 1 min after occlusion. Short-term variability increased prior to VA in both STV modalities. The percentage change in STV from baseline to successive timepoints correlated well between STV-QT and STV-ARIauto. High-rate pacing did not improve arrhythmic outcomes and was accompanied by a stronger decrease in ejection fraction.

CONCLUSION

STV-ARIauto values increase before VA onset, alike STV-QT in a porcine model of MI, indicating imminent arrhythmias. This highlights the potential of automatic monitoring of arrhythmic risk by cardiac devices through STV-ARIauto and subsequently initiates preventive strategies. Continuous HRP during onset of acute MI did not improve arrhythmic outcomes.

Electrophysiological characteristics and possible mechanism of bipolar pacing in left bundle branch pacing

BACKGROUND

Left bundle branch pacing is a physiological pacing modality with a low and stable threshold. The electrophysiological characteristics and mechanisms of bipolar pacing remain unclear.

OBJECTIVES

This study aimed to assess the electrophysiological characteristics of bipolar pacing of left bundle branch pacing and to infer the mechanisms underlying each electrocardiogram and electrogram waveform morphology.

METHODS

A total of 65 patients who strictly met the criteria for left bundle branch capture were enrolled. The changes in the morphology of the electrocardiogram and electrogram during the threshold testing with different outputs on unipolar and bipolar pacing were recorded. The electrophysiological characteristics were then analyzed.

RESULTS

Four distinct morphologies and 3 different types of transitions during bipolar pacing threshold testing were identified; we labeled the 4 types of morphologies as nonselective (NS)-bipolar-left bundle (LB), NS-cathodal-LB, selective (S)-cathodal-LB, and left ventricular septal-cathodal. Except left ventricular septal-cathodal, the other 3 types (NS-bipolar-LB, NS-cathodal-LB, and S-cathodal-L) had a short and constant V₆ R-wave peak time (RWPT) (64.8 ± 7.7 ms vs 65.7 ± 7.8 ms vs 65.7 ± 7.3 ms). The paced QRS (P-QRS) complex was the narrowest in NS-bipolar-LB rather than in NS-cathodal-LB (118.2 ± 14.2 ms vs 133.8 ± 15.8 ms; P < .001). NS-bipolar-LB had a higher threshold than did NS-cathodal-LB (2.5 ± 1.2 V vs 0.8 ± 0.4 V; P < .001).

CONCLUSION

With a higher output on bipolar pacing, NS-bipolar-LB capture had the shortest V₆ RWPT, V₁ RWPT, and P-QRS. S-cathodal-LB capture had the longest V₁ RWPT and P-QRS complex.

Characteristics of intracardiac electrogram of the interventricular septum in the left bundle branch pacing

Background

Left bundle branch pacing (LBBP) has become a hot topic in the field of physiological pacing. However, only a few studies have described the characteristics of the intrinsic intracardiac electrogram (EGM) while placing the left bundle branch (LBB) lead.

Case presentation

Herein, we reported a case with atrial premature contractions to the ventricle during the LBBP procedure. Paced and intrinsic (supraventricular) EGMs were recorded and analyzed.

Conclusions

The myocardium of the interventricular septum could be divided into four regions based on electrophysiology: the right septal area, the left septal area, the endocardium of the left ventricular septum, and the LBB area. This might guide the electrophysiological localization of the LBB lead in the septum.

A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa

We propose a novel convolutional neural network framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying five different ECG types. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed convolutional neural network model represents an efficient, accurate, and superior way to synthesize a 12-lead ECG when compared to previous methods. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for classifying different ECG arrhythmia's on the MIT-BIH arrhythmia database with an average accuracy of 0.98.

Manual Intracardiac Electrogram Method Is Accurate Alternative to Echocardiography for Atrioventricular and Interventricular Optimization in Cardiac Resynchronization Therapy

Some manufacturers do not provide automated intracardiac electrogram method (IEGM) systems for atrioventricular (AV) and interventricular (VV) delay optimization in cardiac resynchronization therapy (CRT). We aimed to evaluate the accuracy of manual IEGM method in 48 patients previously implanted with Medtronic Syncra CRT. All patients underwent standard device interrogation followed by CRT optimization by IEGM method and by echocardiography one month after implantation. The patient mean age was 60.7±11.8 years and there were 33 (68.8%) males. After CRT implantation, the left ventricular ejection fraction increased from 28.0±7.9% to 39.1±11.0% (p<0.001). Optimal aortic flow Velocity Time Integral (aVTI) was obtained when VV was set to 20-50 ms left ventricular pre-activation. There was a strong correlation between VV values determined by echocardiography and IEGM (R=0.823, p<0.001). We found no significant difference in AV, VV and aVTI values between echocardiography and IEGM method. However, IEGM was significantly less time-consuming than echocardiography [20 (10-28) vs. 40 (35-60) minutes, p<0.001]. Manual IEGM method may be good alternative to echocardiography and automated IEGM method. It also emphasizes the need for implementation of automated IEGM systems in as many CRT devices as possible.

An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping

BACKGROUND AND OBJECTIVE

Optimal targets for persistent atrial fibrillation (persAF) ablation are still debated. Atrial regions hosting high dominant frequency (HDF) are believed to participate in the initiation and maintenance of persAF and hence are potential targets for ablation, while rotor ablation has shown promising initial results. Currently, no commercially available system offers the capability to automatically identify both these phenomena. This paper describes an integrated 3D software platform combining the mapping of both frequency spectrum and phase from atrial electrograms (AEGs) to help guide persAF ablation in clinical cardiac electrophysiological studies.

METHODS

30s of 2048 non-contact AEGs (EnSite Array, St. Jude Medical) were collected and analyzed per patient. After QRST removal, the AEGs were divided into 4s windows with a 50% overlap. Fast Fourier transform was used for DF identification. HDF areas were identified as the maximum DF to 0.25Hz below that, and their centers of gravity (CGs) were used to track their spatiotemporal movement. Spectral organization measurements were estimated. Hilbert transform was used to calculate instantaneous phase.

RESULTS

The system was successfully used to guide catheter ablation for 10 persAF patients. The mean processing time was 10.4 ± 1.5min, which is adequate comparing to the normal electrophysiological (EP) procedure time (120∼180min).

CONCLUSIONS

A customized software platform capable of measuring different forms of spatiotemporal AEG analysis was implemented and used in clinical environment to guide persAF ablation. The modular nature of the platform will help electrophysiological studies in understanding of the underlying AF mechanisms.

Analysis and visualization of intracardiac electrograms in diagnosis and research: Concept and application of KaPAVIE

BACKGROUND AND OBJECTIVE

Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias. But although huge amounts of intracardiac electrograms (EGMs) can be acquired during electrophysiological examinations, there is still a lack of software aiding diagnosis. The development of novel algorithms for the automated analysis of EGMs has proven difficult, due to the highly interdisciplinary nature of this task and hampered data access in clinical systems. Thus we developed a software platform, which allows rapid implementation of new algorithms, verification of their functionality and suitable visualization for discussion in the clinical environment.

METHODS

A software for visualization was developed in Qt5 and C++ utilizing the class library of VTK. The algorithms for signal analysis were implemented in MATLAB. Clinical data for analysis was exported from electroanatomical mapping systems.

RESULTS

The visualization software KaPAVIE (Karlsruhe Platform for Analysis and Visualization of Intracardiac Electrograms) was implemented and tested on several clinical datasets. Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias. It proved useful in discussions with clinicians due to its interactive and user-friendly design. Time after export from the clinical mapping system to visualization is below 5min.

CONCLUSION

KaPAVIE(2) is a powerful platform for the development of novel algorithms in the clinical environment. Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation.

Construction of intracardiac vectorcardiogram from implantable cardioverter-defibrillator intracardiac electrograms

We constructed an intracardiac vectorcardiogram from 3 configurations of intracardiac cardiovertor defibrilator (ICD) electrograms (EGMs). Six distinctive 3 lead combinations were selected out of five leads: can to right ventricular coil (RVC); RVC to superior vena cava coil (SVC); atrial lead tip (A-tip) to right ventricular (RV)-ring; can to RV-ring; RV-tip to RVC, in a patient with dual chamber ICD. Surface spatial QRS-T angle (119.8°) was similar to intracardiac spatial QRS-T angle derived from ICD EGMs combination A (101.3°), B (96.1°), C (92.8°), D (95.2), E (99.0), F (96.2) and median (101.5). Future validation of the novel method is needed.

Home monitoring of implantable cardioverter-defibrillators: interpretation reliability of the second-generation "IEGM Online" system

Aims

Intracardiac electrograms (IEGMs) are essential for the assessment of implantable cardioverter-defibrillator (ICD) function. The Biotronik Home Monitoring systems transmit an ‘IEGM Online’ that is shorter than the full-length programmer IEGM due to technical constrains. The aim of this study was to evaluate the accuracy of the physician's classification of the underlying rhythm based on the second-generation IEGM Online.

Methods and results

In total, 1533 patients treated with single- and dual-chamber ICDs and cardiac resynchronization therapy defibrillators were enrolled at 67 investigational sites and followed for 15 months. The investigators classified the rhythm shown in IEGM Online as ventricular tachycardia, ventricular fibrillation, atrial fibrillation, other supraventricular tachyarrhythmia, oversensing due to lead failure, T-wave oversensing, or other rhythm. At the next in-office follow-up, the investigators classified independently the rhythm seen in the corresponding programmer IEGM. The two rhythm classifications were compared thereafter. Both IEGM Online and programmer IEGM were available in 2099 arrhythmic or oversensing events, of which 146 (7.0%) were classified as other rhythm or artefacts and were excluded as inconclusive or atypical. The remaining 1953 events, affecting 352 patients (23.0%), were classified correctly in 1803 cases (92.3%). The accuracy of rough rhythm classification as ventricular, supraventricular, or oversensing was 97.2%.

Conclusion

The Lumax and IEGM Online HD Evaluation study demonstrates that remote IEGM analysis is reasonably accurate in a remote monitoring system that transmits shorter IEGM than the full-length programmer IEGM for the sake of frequent, fully automatic data transmission.