Automated detection of scar-related ventricular tachycardia origins from implanted device electrograms: a combined computational-AI platform

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): EPSRC Background Existing strategies that identify ventricular tachycardia (VT) ablation targets either employ invasive and time-consuming electrophysiological (EP) mapping, or non-invasive modalities that utilise standard electrocardiogram (ECG) signals. Success of these pre-procedure ablation approaches in localising re-entrant VTs often relies on VT induction, which could be avoided by utilising recordings of clinical VT episodes stored as electrograms (EGMs) in implanted devices. Such a non-invasive approach that localises VT substrates from EGMs may aid ablation planning, enhancing safety and speed. Purpose Our goal is to automate scar-related VT localisation by utilising EGM recordings of VT episodes from implanted devices. To achieve this, deep-learning algorithms will be trained on computational data to return VT sites of origin from implanted device EGMs. Ultimately, we intend to utilise this computational-artificial intelligence (AI) framework to detect ablation targets of clinical VT episodes and guide pre-procedure ablation planning non-invasively. Methods A comprehensive library of ECGs and EGMs from simulated paced beats (~15000) and scar-related VTs (500) was generated across five detailed torso models within a fast EP computational environment, combining reaction-eikonal and lead field methods. ECG (or EGM) traces from simulated paced beats were used to initially pre-train two convolutional neural network (CNN) long short-term (LSTM) attention-based architectures. Subsequently, signals of the in-silico, re-entrant VTs were used to re-train the networks to output the sites of origin of these episodes in a standardised ventricular coordinate space. Finally, the retrained CNN architectures were tested on re-entrant VTs of unseen models, and median localisation errors (LEs) were estimated against known VT origins from simulations. Results The performance of the networks to localise scar-related VT episodes was asserted for each torso model. When a torso model was only seen during initial training on simulated paced beats, implanted device EGMs and ECGs successfully localised VT sources with LEs 10.04 – 16.36 mm and 10.05 – 12.79 mm, respectively. When a torso model was not seen during pacing or VT training, recreating potential clinical application settings where ECGs or EGMs of clinical VTs would be the only inputs to the networks, LEs ranged 12.42 - 22.79 mm and 12.41 - 19.68 mm for EGM and ECG-based testing, respectively. Conclusions Our study successfully detected VT ablation substrates with accuracy that could be beneficial in clinical ablation settings. The proposed computational-AI framework may be used to automate the localisation of scar-related VTs from clinical ECGs or EGM recordings from implanted devices, ultimately aiding ablation planning.