Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF

A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins.

BACKGROUND Superimposition of farfield (FF) and nearfield (NF) bipolar voltage electrograms (BVE) complicates the confirmation of pulmonary vein (PV) isolation after catheter ablation of atrial fibrillation. Our aim was to develop an automatic algorithm based on a single-beat analysis to discriminate PV NF from atrial FF BVE from a circular mapping catheter during the cryoballoon PV isolation. METHODS During freezing cycles in cryoablation PVI, local NF and distant FF signals were recorded, identified and labelled. BVEs were classified using four different machine learning algorithms based on four frequency domain (high-frequency power (PHF), low-frequency power (PLF), relative high power band, PHF ratio of neighbouring electrodes) and two time domain features (amplitude (Vmax), slew rate). The algorithm-based classification was compared to the true identification gained during the PVI and to a classification by cardiac electrophysiologists. RESULTS We included 335 BVEs from 57 consecutive patients. Using a single feature, PHF with a cut-off at 150 Hz showed the best overall accuracy for classification (79.4%). By combining PHF with Vmax, overall accuracy was improved to 82.7% with a specificity of 89% and a sensitivity of 77%. The overall accuracy was highest for the right inferior PV (96.6%) and lowest for the left superior PV (76.9%). The algorithm showed comparable accuracy to the classification by the EP specialists. CONCLUSIONS An automated farfield-nearfield discrimination based on two simple features from a single-beat BVE is feasible with a high specificity and comparable accuracy to the assessment by experienced cardiac electrophysiologists

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

Multichannel Analysis of Intracardiac Electrograms - Supporting Diagnosis and Treatment of Cardiac Arrhythmias

Cardiologists diagnose and treat atrial tachycardias using electroanatomical mapping systems. These can be combined with multipolar catheters to record intracardiac electrograms. Within this thesis, various signal processing techniques were implemented and benchmarked to analyze electrograms. They support the physician in diagnosis and treatment of atrial flutter and atrial fibrillation. The developed methods were assessed using simulated data and demonstrated on clinical cases

Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach

INTRODUCTION Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECG’s but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier. METHOD An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms. RESULTS Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naïve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naïve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean κ 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearson’s phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis. CONCLUSIONS Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naïve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment

Simplified Cardiodynamic Tissue Electrophysiology Characterization, Reduced Order Modeling with Therapeutic Perspective

Atrial fibrillation (Afib) is the most common cardiac arrhythmia affecting millions of people around the world. Mapping and analysis of electrical activation patterns such as electric rotors during Afib is crucial in understanding arrhythmic mechanisms and assessment of diagnostic measures. To this end, there exists various mapping studies where textit{'quantitative'} features such as local activation time, dominant frequency, wave direction, and conduction velocity are extracted from recorded intracardiac electrograms (EGMs). However, obtaining quantitative features further adds to multiplicity of the data and henceforth does not help interpretation of measured signals as opposed to using a more compressed diagnostic terms such as linking the measurements to reentry mechanisms. Through some techniques it is possible to construct isopotential and phase mappings by the help of monophasic action potential recordings in higher spatial resolution. In those cases, however, both expensive mapping tools performing multi-site simultaneous recordings which are not available to most of electrophysiologists are required. On the other hand, the most commonly used catheters which provide high resolution but local measurements remain rather rudimentary in mapping a spatially more global arrhythmic behaviors in a simultaneous fashion. Spiral waves are tissue level phenomena observed in both clinical and experimental settings. They are the product of electrical rotors which are associated with reentry mechanisms during Afib. They can be reproduced using computer models of cardiac electrical activity. Current computer models vary in complexity, accuracy, and efficiency. One particular type is called biophysical models which are based on detailed ion channel interactions. Besides being computationally demanding, they are exceedingly complex and intractable preventing their use in a systems approach where multilevel events are generally considered together. Phenomenological models, on the other hand, include summarized details of ionic events yet preserve fundamental biophysical accuracy. A particular one of them, a minimal resistor model (MRM), was shown to reproduce relevant basic electrophysiological behaviors such as (action potential) AP and electrical restitution properties for human ventricular tissue. The objective in present thesis is to 'qualitatively' characterize fibrillatory wavefront propagation dynamics in cardiac tissue using simulated intracardiac EGMs obtained from most commonly used and lower cost catheter types providing high resolution but localized readings. Another purpose connected to the previous is to show adequacy of a phenomenological model, MRM, in reproducing biophysically related behaviors for human atria. In this respect, two category of problems are handled throughout the thesis: (1) parameter estimation of MRM and (2) discrimination of spiral wave behaviors through intracardiac EGMs simulated using MRM. In the first part, representativeness of MRM for human atrial electrophysiology is established through adaptation of it to a biophysically detailed model originated from experimental data. Specifically, a method is proposed for parameter estimation of the simple model, MRM, to match a targeted behavior such as AP and electrical restitutions first generated from a complex model, by using extended Kalman filter (EKF). In the second part, a method that receives intracardiac EGMs and returns corresponding wavefront propagation patterns classified in terms of electric rotor dynamics is introduced. The method incorporates an information theoretical distance which is called normalized compression distance (NCD) used for assessment of distance measure between simulated behaviors. Achieving outstanding performance together with robustness in discrimination through usage of simulated data enables a theoretical validation of the method. Proposed frameworks collectively yield (1) potential usability of a computationally efficient and easier in analysis model for tissue level cardiac events and (2) simplicity and practicality in clinics through a mapping from a multiple, complex EGM signals to electric rotor behaviors, symptoms more relevant to the diagnosis.Ph.D., Electrical Engineering -- Drexel University, 201

Primary prevention implantable cardiac defibrillators: a Townsville district perspective

Background: Despite major advances in treating patients with severe heart failure, deciding who should receive an implantable cardiac defibrillator (ICD) remains challenging. Objective: To study the risk factors and mortality in patients after receiving an ICD (January 2008–December 2015) in a regional hospital in Australia. Methods: Eighty-two primary prevention patients received an ICD for ischemic cardiomyopathy (ICM, n = 41) and non-ischemic cardiomyopathy (NICM, n = 40) with 4.8-yrs follow-up. One patient had mixed ICM/NICM indications. Ventricular arrhythmias were assessed using intracardiac electrograms. Statistical analysis compared the total population and ICM and NICM groups using Kaplan-Meier for survival, Cox regression for mortality predictors, and binary logistic regression for predictors of ventricular arrhythmias (p < 0.05). Results: Major risk factors were hypercholesterolemia (70.7%), hypertension (47.6%), and obesity (41.5%). Severe obstructive sleep apnea (OSA) was found exclusively in NICM patients (23.7%, p = 0.001). Mortality was 30.5% after 4.8-yrs. The majority of patients (n=67) had no sustained ventricular arrhythmias yet 28% received therapy (n = 23), 18.51% were appropriate (n = 15), and 13.9% inappropriate (n = 11). Patients receiving ≥2 incidences of inappropriate shocks were 18-times more likely to die (p =0.013). Three sudden cardiac deaths (SCD) (3.7%) were prevented by the ICD. Conclusion: Patients implanted with an ICD in Townsville had 30.5% all-cause mortality after 4.8-yrs. Only 28% of patients received ICD therapy and 13.9% were inappropriate. OSA may have contributed to the fourfold increase in inappropriate therapy in NICM patients. Our study raises important efficacy, ethical and healthcare cost questions about who should receive an ICD, and possible regional and urban center disparities

Approach to device-detected subclinical atrial fibrillation

Subclinical atrial fibrillation, a commonly encountered entity in patients with implantable devices, has been associated with a number of adverse outcomes – the most important of which is thromboembolism. Through the detection of atrial high rate episodes, implanted devices offer a method to monitor for atrial fibrillation over extended periods of time. Several studies have demonstrated that patients with device-detected atrial tachyarrhythmias have an increased incidence of stroke, especially in the presence of additional risk factors. Yet, there are many uncertainties with limited evidence from randomised clinical studies and no formal guidelines to inform management in this population. This contributes to marked practice heterogeneity, underrecognition and missed opportunities for stroke prevention. We propose a logical approach to management of patients with device-detected atrial high rate episodes pending additional data from ongoing trials

Implantable defibrillator therapy: more than defibrillation...

During the past 25 years, the implantable cardioverter-defibrillator (ICD) has evolved from the treatment of last resort to the gold standard for patients at high risk for life­threatening ventricular tachyarrhythmias. Patients at high risk include those who survived life-threatening ventricular tachyarrhythmias, and patients with cardiac diseases who carry an increased risk for these tachyarrhythmias. We performed a clinical assessment during implantation and follow-up of our patients in Rotterdam. Part I Prognosis and follow-up of patients with an ICD In Chapter 1, the clinical benefit of ICD therapy, survival, and adverse events of patients who received an ICD at the Erasmus MC Rotterdam are described. Our data confirm the benefit from ICD implantation, especially for those patients with a poor left ventricular function. In Chapter 2, defibrillation efficacy testing is investigated. The role of a second defibrillation threshold test after implantation appears questionable. With the advances in ICD technology, defibrillation thresholds are low and stable, which changed the mode of death in ICD patients from instantaneous arrhythmic death to heart failure. Our data demonstrate that despite the advanced ICD technology, a subset of patients may require a second defibrillation efficacy test to confirm a poor prognosis. The feasibility of remote monitoring of ICD therapy is discussed in Chapter 3. The expanding indications for ICD therapy and the complexity of current devices have a high impact on follow-up policy. Currently, the quality of medical supervision only depends on scheduled regular follow-up visits, which is time consuming and expensive. Too long follow-up intervals may have the disadvantage of a delay in the awareness of changes of the clinical course of the underlying disease or in the technical status of the device. TransmiHet concept van de implantable cardioverter-defibrillator (ICD) heeft de afgelopen 25 jaar een grote ontwikkeling doorgemaakt. De eerste generatie defibrillatoren was uitsluitend in staat om ventrikelfibrilleren te herkennen en te onderbreken door middel van een elektrische shock. De volgende generaties defibrillatoren werden uitgerust met functies om verschillende ritmestoornissen te herkennen en te behandelen. Vanuit klinisch oogpunt is er een verschuiving opgetreden van secundaire preventie naar primaire preventie van plotse dood ten gevolge van ventriculaire ritmestoornissen. Dit proefschrift beschrijft zowel de klinische als technische aspecten van defibrillator therapie. Deel 1 Prognose en follow-up van ICD patiënten Hoofdstuk 1 beschrijft zowel het klinische voordeel als de potentiële complicaties van defibrillator therapie bij patiënten, bij wie een ICD in het Erasmus MC werd geïmplanteerd. De rol van het testen van de defibrillatie effectiviteit wordt in Hoofdstuk 2 behandeld. Door de technische vooruitgang kunnen ventriculaire ritmestoornissen effectief met lage energie gedefibrilleerd worden. Vanuit dit technisch oogpunt is een tweede defibrillatie test niet nodig, echter vanuit klinisch oogpunt kan deze test een slechte prognose bij een kleine groep patiënten bevestigen. Veranderingen in de klinische status van de ICD patiënt worden vaak pas vastgesteld bij het volgende poliklinisch bezoek. In Hoofdstuk 3 wordt de mogelijkheid van het op afstand waarnemen van zowel klinische als technische aspecten van defibrillator therapie beschreven. Het verzenden van opgeslagen data in de ICD heeft een potentiële meerwaarde voor de klinische follow-up van de patiënt. Ter illustratie wordt in Hoofdstuk 4 een voorbeeld van het op afstand waarnemen van ICD data gepresenteerd. Bij interpretatie van de ontvangen data werd een malfunctie van de ventriculaire elektrode vastgesteld. Deel 2 Onderscheiden van ritmestoornissen door de ICD Hoofdstuk 5 behandelt een voorbeeld van een opgeslagen registratie van een ventriculaire ritmestoornis, die na nauwkeurige analyse een ander ontstaansmechanisme heeft dan het op eerste oog doet lijken. In Hoofdstuk 6 wordt een nieuw tweekamer detectie algoritme, SMART, geëvalueerd. Na de vergelijking van de atriale en ventriculaire frequentie worden enkelkamer detectie criteria toegepast om een ritmestoornis te classificeren. Ventriculaire ritmestoornissen worden met behulp van dit algoritme betrouwbaar waargenomen. Ondanks een goede discriminatie tussen atriale en ventriculaire ritmestoornissen, worden atriale ritmestoornissen met een stabiele atrioventriculaire geleiding vooral verkeerd geclassificeerd. De ontwikkeling van detectie algoritmen om onterechte therapie ten gevolge van atriale ritmestoornissen te vermijden, wordt in Hoofdstuk 7 gepresenteerd. De classificatie van de ritmestoornis door de ICD is primair gebaseerd op de timing van ventriculaire signalen. De originele eenkamer detectie algoritmen zijn in de tweekamer ICD geïmplementeerd. De toevoeging van atriale informatie heeft tot betere en geavanceerde detectie algoritmen geleidt. Echter, onderzoeken die eenkamer met tweekamer detectie algoritmen vergeleken, lieten geen afname van onterechte therapie zien. Hoofdstuk 8 gaat over klinische variabelen die een verhoogd risico op onterechte therapie kunnen voorspellen. Uit onderzoek blijkt dat de aanwezigheid van atriale ritmestoornissen in het verleden en het voorkomen van trage ventriculaire ritmestoornissen, beide een verhoogd risico op onterechte therapie voorspellen. De beslissing om een tweekamer defibrillator bij patiënten met atriale ritmestoornissen te implanteren is een open vraag. Onterechte therapie werd namelijk in gelijke mate bij zowel eenkamer als tweekamer defibrillatoren waargenomen. Een gerandomiseerd onderzoek tussen eenkamer en tweekamer detectie algoritmen wordt in Hoofdstuk 9 gepresenteerd. De resultaten van dit onderzoek laten geen verschil zien in de discriminatie