Towards a repository of synthetic electrograms for atrial activation detection in atrial fibrillation. (1st October 2018)

• Record Type: Journal Article
• Authors: Valinoti, Maddalena; Masci, Alessandro; Berto, Francesca; Severi, Stefano; Corsi, Cristiana
• Is Part Of: Computers in biology and medicine. Volume 101(2018)
• Page Start: 229
• Page End: 235
• Publication Date: 2018-10-01
• Subjects: Atrial fibrillation -- Biomedical signal processing -- Synthetic atrial electrograms; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.09.001
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Recently, the analysis of the spatio-temporal behavior of atrial fibrillation activation patterns has been widely investigated with the aim to better understand the arrhythmia implications on the heart electrical activity. Most of the proposed techniques are based on atrial activation timing detections. Unfortunately atrial activation timings are not easily recognizable on the electrograms (EGMs) and an approach to support the validation of such techniques is highly desirable. The aim of this study is to provide an effective workflow for the generation of synthetic unipolar atrial electrograms (SEGMs) in atrial fibrillation (AF) condition and with different levels of noise. Method: Real EGMs signals were obtained from a dataset of 6 subjects that underwent ablation. Each SEGM was obtained by modeling the three principal components of an EGM starting from real signals: atrial far-field (Afar ), atrial near-field (Anear ) and the ventricular far-field (Vfar ). Afar was generated using an autoregressive model applied on segments from real EGMs not characterized by ventricular or atrial activations; Anear and Vfar were extracted directly from the real signals. A Gamma distribution and an atrio-ventricular node model were used to locate both Anear and Vfar on Afar, respectively. Three electrophysiologists with different levels of expertise evaluated the realism of the SEGMs on a set of 100 randomly selected signals including 50 EGMs and 50 SEGMs. Analysis was repeated by the three experts on a subset of 21 signals. Results: The time required to generate the synthetic EGMs was less than 1 min once annotated EGMs are available. The cardiologists succeeded in distinguishing real from synthetic EGMs in 45%, 43% and 35% of the signals, respectively. By repeating the evaluation, 28%, 0% and 48% of signals were classified differently, including 67%, 52% and 36% of correct classifications. Conclusion: The proposed approach proved to be effective in producing SEGMs which are difficult to distinguish from real EGMs. This study provides a tool for realistic SEGM generation from real EGMs in AF condition with different levels of noise and at different AF rates. The tool may be easily adopted to obtain SEGMs in different arrhythmic conditions. SEGMs generated in this study are shared with the scientific community as a first step towards a repository of synthetic and real atrial signals supporting the benchmarking of new approaches to investigate AF. Highlights: This is the first study aimed at the generation of synthetic EGMs from real EGM templates in AF. Synthetic EGMs are an effective option for validation of new approaches aimed at AF mechanisms investigation. Synthetic EGM are shared with the scientific community as a first step towards a repository of atrial signals in AF.