Tracking Rotors With Minimal Electrodes

Balasundaram, Krishnanand; Umapathy, Karthikeyan; Jeyaratnam, Joyce; Niri, Ahmed; Massé, Stéphane; Nair, Krishnakumar; Asta, John; Cusimano, Robert J.; Vigmond, Edward J.; Nanthakumar, Kumaraswamy

doi:10.1161/circep.114.002306

Cited by 8 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These rotors are believed to be centres where the ventricular arrhythmia originates [30][31][32][33]. Rotors are of special clinical interests as detecting them and tracking them could lead to termination of VA through ablation [2,34]. A conference proceeding by our group revealed that there exists different distributions of a few repeating electrogram signal patterns during an arrhythmic episode [35].…”

Section: Current Researchmentioning

confidence: 99%

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Balasundaram¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Ventricular fibrillation (VF) is one of the major causes for sudden cardiac deaths (SCD). The duration from the onset of VF to SCD is a few minutes, making it difficult to study VF. This dissertation proposes methods to extract meaningful information from VF electrograms and formulate associations to underlying structural and physiological properties of the cardiac tissue and clinical events of interest during VF. This was achieved by analyzing clues in the electrograms during VF to infer the underlying anatomical and physiological properties of the cardiac tissue and certain clinical events of interest, which is otherwise not easily available. The proposed methods will be of great assistance for the diagnosis and treatment planning of cardiac arrhythmias. The proposed adaptive time-frequency (TF) signal decomposition was separated into two categories based on two purposes: (1) Time-specific event detection and (2) Time-averaged VA characterization. For the time-specific event detection (in this work rotor detection), electrogram signal features related to the rotor event were identified with an adaptive TF decomposition and amodified criterion function. Using the proposed features and a linear discriminant analysis based classifier with leave-one-out cross validation, overall classification accuracies of 80.77% and 79.41% were achieved in detecting rotor events and separating them from similar but non-rotor events. In the time-averaged ventricular arrhythmia characterization, previously established signal features were used to associate electrogram clues to the structural and physiological characteristics of the cardiac tissue. Using label-consistent K-means singular value decomposition dictionary learning process, dictionaries of TF basis functions were generated to capture specific electric structures and physiological characteristics of the underlying cardiac tissue. The association of these characteristics with the extracted electrogram clues were validated using a cross-validation technique. The cross-validated results ranged from 65.58% to 81.80% for the 7 characteristics used in this study. Further to this, to build a decision-support system with non-linear separable capabilities that could automate and infer the heart events and/or characteristics from the identified electrogram signal structures, neural network models were generated. The cross-validated accuracies ranged from 66.99% to 85.90% for each of the developed models for the decision-support system.

show abstract

Section: Current Researchmentioning

confidence: 99%

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Balasundaram¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several methods were introduced to help map rotors using engineering techniques in both time and frequency domain [54][55][56][57][58]. Some of these methods are specifically designed to use conventional MPDCs such as a Lasso catheter, aiming to outperform the limitations of the current EP systems available for identifying rotors.…”

Section: Computer-aided Technologies For Atrial Fibrillationmentioning

confidence: 99%

“…The relevant methods available in the literature for locating rotors involve deterministic, noniterative solutions using time-domain and frequency-domain characteristics [54][55][56][57][58]. The developed method uses a Bayesian filtering approach to search for the rotors by iteratively guiding an MPDC toward the center of a rotor.…”

Section: Computer-aided Technologies -Applications In Engineering Andmentioning

confidence: 99%

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Ganesan¹,

Sterling²,

Ladavich³

et al. 2016

Computer-Aided Technologies - Applications in Engineering and Medicine

View full text Add to dashboard Cite

Clinical decision support systems (clinical DSSs) are widely used today for various clinical applications such as diagnosis, treatment, and recovery. Clinical DSS aims to enhance the end-to-end therapy management for the doctors, and also helps to provide improved experience for patients during each phase of the therapy. The goal of this chapter is to provide an insight into the clinical DSS associated with the highly prevalent heart rhythm disorder, atrial fibrillation (AF). The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient's health during follow-ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment outcome, and localization of AF targets during a treatment procedure.

show abstract

“…In addition, bipolar electrograms are the most commonly used recording modality in clinical electrophysiology, making these signals readily available for entropy analysis whilst also providing a direct and interpretable translation to what is seen clinically. Lastly, high bipolar electrogram Shannon entropy (ShEn) has been demonstrated by our group to be associated with the pivot of rotors [4,18,19], and subsequently entropy-based identification of the rotor pivot has been independently observed by multiple groups using a variety of entropy-based approaches, and in multiple model systems [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Temporal stability and specificity of high bipolar electrogram entropy regions in sustained atrial fibrillation: Implications for mapping

Dharmaprani

McGavigan

Chapman

et al. 2019

Journal of Electrocardiology

View full text Add to dashboard Cite

Background: The potential utility of entropy (En) for atrial fibrillation (AF) mapping has been demonstrated in previous studies by multiple groups, where an association between high bipolar electrogram (EGM) entropy and the pivot of rotors has been shown. Though En is potentially attractive new approach to ablation, no studies have examined its temporal stability and specificity, which are critical to the application of entropy to clinical ablation. In the current study, we sought to objectively measure the temporal stability and specificity of bipolar EGM entropy in medium to long term recordings using three studies: i) a human basket catheter AF study, ii) a tachypaced sheep AF study and iii) a computer simulation study. Objective: To characterize the temporal dynamics and specificity of Approximate, Sample and Shannon entropy (ApEn/SampEn/ShEn) in human (H), sheep (S), and computer simulated AF. Methods: 64-electrode basket bi-atria sustained AF recordings (H:15 min; S:40 min) were separated into 5 s segments. ShEn/ApEn/SampEn were computed, and co-registered with NavX 3D maps. Temporal stability was determined in terms of: (i) global pattern stability of En and (ii) the relative stability the top 10% of En regions. To provide mechanistic insights into underlying mechanisms, stability characteristics were compared to models depicting various propagation patterns. To verify these results, cross-validation was performed across multiple En algorithms, across species, and compared with dominant frequency (DF) temporal characteristics. The specificity of En was also determined by looking at the association of En to rotors and areas of wave cross propagation. Results: Episodes of AF were analysed (H:26 epochs, 6040 s; S:15 epochs, 14,160 s). The global pattern of En was temporally unstable (CV-H:13.42% ± 4.58%; S:14.13% ± 8.13%; Friedman-H: p N 0.001; S: p N 0.001). However, within this dynamic flux, the top 10% of ApEn/SampEn/ShEn regions were relatively temporally stable (Kappa N0.6) whilst the top 10% of DF regions were unstable (Kappa b0.06). In simulated AF scenarios, the experimental data were optimally reproduced in the context of an AF pattern with stable rotating waves surrounded by wavelet breakup (Kappa: 0.610; p b 0.0001). Conclusion: En shows global temporal instability, however within this dynamic flux, the top 10% regions exhibited relative temporal stability. This suggests that high En regions may be an appealing ablation target. Despite this, high En was associated with not just the pivot of rotors but also with areas of cross propagation, which suggests the need for future work before clinical application is possible.

show abstract

Tracking Rotors With Minimal Electrodes

Cited by 8 publications

References 21 publications

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Temporal stability and specificity of high bipolar electrogram entropy regions in sustained atrial fibrillation: Implications for mapping

Contact Info

Product

Resources

About