Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials

Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide ablation procedures, being able to reconstruct the electrophysiological activity on the heart surface by using a non-invasive recording of body surface potentials (BSP). However, the inverse problem of ECGI is ill-posed, and it requires accurate mathematical modeling of both atria and torso, mainly from CT or MR images. Several deep learning-based methods have been proposed to detect AF, but most of the AF-based studies do not include the estimation of ablation targets. In this study, we propose to model the location of AF drivers from BSP as a supervised classification problem using convolutional neural networks (CNN). Accuracy in the test set ranged between 0.75 (SNR = 5 dB) and 0.93 (SNR = 20 dB upward) when assuming time independence, but it worsened to 0.52 or lower when dividing AF models into blocks. Therefore, CNN could be a robust method that could help to non-invasively identify target regions for ablation in AF by using body surface potential mapping, avoiding the use of ECGI.

Local areas of earlier repolarization cause epicardial repolarization heterogeneity in patients with apparently idiopathic ventricular fibrillation

Background Sudden cardiac arrest is often due to ventricular fibrillation (VF). In 5–10% of cases, no cause can be identified despite extensive cardiac examination, hence the designation idiopathic VF. Early repolarization with down sloping ST segments has been previously identified in patients with idiopathic VF. Early repolarization may increase repolarization heterogeneity with steep local repolarization time gradients, and thus form a substrate for idiopathic VF. Purpose To study the presence of local earlier repolarization and increased repolarization dispersion in idiopathic VF patients with noninvasive electrocardiographic imaging (ECGI). Methods A validated, non-commercial, potential-based formulation of ECGI was performed in 17 patients with idiopathic VF and 10 controls with no structural or electrical abnormalities. The ECGI measurement consisted of a body surface potential map with 184–256 electrodes in combination with a CT scan to obtain the torso and heart geometries. ECGI provided local epicardial repolarization times (RT) and RT isochrones. We determined the 1st (RT1%) and 99th percentile (RT99%) of RTs, the total epicardial RT dispersion (ERD: RT99%-RT1%), and the mean RT. Heart-rate corrected QT (QTc), TpTe intervals, and presence of the ER pattern were determined from the 12-lead ECG. All metrics were normalized to the body-surface Q. Results QTc and TpTe did not differ between the two groups (P=0.40 and P=0.83, respectively, Figure 1, panel A). One (10%) control subject and three (17.6%) idiopathic VF patients showed an ER pattern on the 12-lead ECG, with a down sloping ST segment only in 2/4 of the latter. With ECGI, the mean RT was similar between the groups (P=0.31), but the ERD was significantly increased in patients with idiopathic VF (P=0.01, figure 1, panel B). Moreover, RT1% was significantly lower in idiopathic VF patients in comparison to the controls (P=0.002), whereas the RT99% did not differ significantly (P=0.40). Subgroup analysis between ER positive and negative patients did not yield significantly different RT results. Conclusion Noninvasive ECGI, in contrast to the 12-lead ECG, revealed a wider range of epicardial RTs in patients with idiopathic VF, implying increased repolarization heterogeneity. This heterogeneity is caused by areas of earlier repolarization. Our data indicate the value of noninvasively diagnosing these repolarization abnormalities, and suggest promising potential value of the 1st percentile of RT to identify idiopathic VF patients with true early repolarization. FUNDunding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Dutch Heart Foundation Figure 1

Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.

Clinical Utility of Body Surface Potential Mapping in CRT Patients

This paper reviews the current status of the knowledge on body surface potential mapping (BSPM) and ECG imaging (ECGI) methods for patient selection, left ventricular (LV) lead positioning, and optimisation of CRT programming, to indicate the major trends and future perspectives for the application of these methods in CRT patients. A systematic literature review using PubMed, Scopus, and Web of Science was conducted to evaluate the available clinical evidence regarding the usage of BSPM and ECGI methods in CRT patients. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used as a basis for this review. BSPM and ECGI methods applied in CRT patients were assessed, and quantitative parameters of ventricular depolarisation delivered from BSPM and ECGI were extracted and summarised. BSPM and ECGI methods can be used in CRT in several ways, namely in predicting CRT outcome, in individualised optimisation of CRT device programming, and the guiding of LV electrode placement, however, further prospective or randomised trials are necessary to verify the utility of BSPM for routine clinical practice.