Reducing electrophysiological (EP) signal noise is essential for diagnosis, mapping, and ablation procedures in patients with arrhythmias or conditions such as cardiomyopathies. However, traditional approaches have been suboptimal due to the varied sources of noise. We hypothesized that variational autoencoders (VAEs) can learn key components of ’clean’ electrophysiological signals by creating robust internal representations, thereby enabling automatic denoising of diverse noise in clinical recordings. We set out to apply a β-VAE model to a dataset of 5706 intra-ventricular monophasic action potential (MAP) signals, selected because their morphology is verifiable and measurable against a reference, from 42 patients with ischemic cardiomyopathy at risk for sudden death. We designed a noise library, and implemented baselines based on state-of-the-art clinical filtering techniques. The proposed β-VAE model was assessed for various noise types, including challenging non-stationary real EP noise. Comprehensive evaluation using general metrics and clinical action potential duration labels by domain experts revealed that our β-VAE outperformed current state-of-the-art filters in denoising efficacy, with key physiological information encoded in the reconstruction. We performed a sensitivity analysis that confirmed the robustness of the β-VAE model to increasing noise levels. These results demonstrate the ability of our model to denoise various sources, including those of time-varying nature. The application to well-studied MAPs verifies that clinically meaningful features were reconstructed in the EP context. This work enhances traditional signal processing approaches to ensure ’clean’ electrical signals, and may have promising applications for diagnosis, tracking therapy and prognostication in patients with EP disorders in real-world clinical environments.

Samuel Ruipérez-Campillo, Alain Ryser, Thomas M Sutter, Brototo Deb, Ruibin Feng, Prasanth Ganesan, Kelly A Brennan, Albert J Rogers, Maarten ZH Kolk, Fleur VY Tjong, Sanjiv M Narayan^†, Julia E Vogt^†
† denotes shared last authorship

Expert Systems with Applications

01.03.2026

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BACKGROUND: Mapping of heart rhythms is influenced by the size and configuration of the mapping electrodes. Whether a recorded electrogram represents near (local) or remote activity influences diagnosis and treatment, yet is affected by mapping characteristics that are often undefined. METHODS: We developed biophysical computational models to predict interactions between the recording tool and cardiac tissue in coherent and disorganized rhythms, which we validated in clinical recordings. RESULTS: Biophysical computational models demonstrated the ability to quantify and visualize the recording antennae for different electrode configurations. Our results show that unipolar electrograms reflected a recording antenna within 3-dimensional ellipsoids of radius 8 mm across-tissue and 2.7 mm transmurally. Bipolar electrogram antennae align with propagation direction in ellipsoids of long axis radius 1.7, 5.7, and 8.3 mm for 2, 5, and 10 mm spacing, respectively, and often extend beyond the physical extent of electrodes. Notably, omnipolar electrograms, constructed from orthogonal bipoles in a triangular configuration, retained some directional preferences of bipolar electrograms, with a complex relationship between electrode orientation and wave direction. When tested clinically on high-resolution, narrow field (grid) catheters and moderate-to-low resolution, global (basket) catheters, antennae varied more with electrode type (correlation coefficient of 0.43 unipolar, 0.05 bipolar, and 0.26 omnipolar; P<0.001) and spacing (correlation coefficient of 0.36 versus 0.42; P=0.002) than the precise electrode size. CONCLUSIONS: This novel computational-clinical system approach enabled us to systematically compare electrode configurations. This work may help interpret signals in complex biological rhythms, such as atrial fibrillation, and may influence the design of novel catheter configurations and signal processing approaches to identify local tissue signals.

Miguel Rodrigo, Samuel Ruipérez-Campillo, Prasanth Ganesan, Ruibin Feng, Sanjiv M. Narayan

Circulation: Arrhythmia and Electrophysiology

26.02.2026

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Recent advances in vision–language models (VLMs) have improved Chest X-ray (CXR) interpretation in multiple aspects. However, many medical VLMs rely solely on supervised fine-tuning (SFT), which optimizes next-token prediction without evaluating answer quality. In contrast, reinforcement learning (RL) can incorporate task-specific feedback, and its combination with explicit intermediate reasoning (``thinking'') has demonstrated substantial gains on verifiable math and coding tasks. To investigate the effects of RL and thinking in a CXR VLM, we perform large-scale SFT on CXR data to build an updated RadVLM based on Qwen3-VL, followed by a cold-start SFT stage that equips the model with basic thinking ability. We then apply Group Relative Policy Optimization (GRPO) with clinically grounded, task-specific rewards for report generation and visual grounding, and run matched RL experiments on both domain-specific and general-domain Qwen3-VL variants, with and without thinking. Across these settings, we find that while strong SFT remains crucial for high base performance, RL provides additional gains on both tasks, whereas explicit thinking does not appear to further improve results. Under a unified evaluation pipeline, the RL-optimized RadVLM models outperform their baseline counterparts and reach state-of-the-art performance on both report generation and grounding, highlighting clinically aligned RL as a powerful complement to SFT for medical VLMs.

Benjamin Gundersen, Nicolas Deperrois, Samuel Ruipérez-Campillo, Thomas M. Sutter, Julia E. Vogt, Michael Moor, Farhad Nooralahzadeh, Michael Krauthammer

MIDL 2026

14.02.2026

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Cardiac MRI encodes detailed geometric information, but standard deep learning models rely on grid-based encoders that emphasize texture rather than structure. Neural fields of- fer a continuous alternative, yet Conditional Neural Fields (CNFs) compress each subject into a single global latent, discarding spatial organization. We evaluate Equivariant Neural Fields (ENFs) for cardiac MRI, which replace the global latent with a geometry-aware la- tent point cloud. ENFs achieve competitive reconstruction quality with far fewer decoder parameters and produce latents that are local, anatomically meaningful, and robust to geometric transformations. For downstream prediction tasks, ENF latents perform com- petitively with ResNet50 and global CNF latents across several clinical endpoints. These results position ENFs as a compact, interpretable, and geometry-aware alternative for car- diac MRI representation learning

Jesse Wiers, David R Wessels, Lukas Arts, Samuel Ruipérez-Campillo, Maarten Kolk, Fleur Tjong, Erik J Bekkers

MIDL 2026

14.02.2026

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Omnipolar technology has improved the characterisation of complex arrhythmias by enabling more accurate, direction-independent electrogram analysis. Omnipolar EGMs are reconstructed assuming locally planar and homogeneous propagation. However, anatomical complexity, electrode spacing, tissue heterogeneity, or poor catheter contact may violate this assumption. This study aims to quantify the extent to which key assumptions are fulfilled in clinical settings by analysing amplitude and morphology variability, as well as wavefront non-planarity. Additionally, we evaluate how such deviations affect the accuracy of omnipolar EGM reconstruction and propagation direction estimation. To enhance performance, we introduce the concept of Ultra-High Resolution—defined as the resolution required to meet these assumptions—and propose electrical field interpolation as a method to bridge this target with feasible interelectrode spacing in practical devices. Results are validated using in silico simulations and clinical recordings acquired with the Advisor™ HD Grid Mapping Catheter. The results show that at 4 mm interelectrode spacing—common in current catheters—deviations from the underlying assumptions are frequent, leading to inaccurate omnipolar signal computation and derived parameters. The proposed Ultra-High Resolution proves reliable, as evidenced by low normalised root mean square error and high Pearson correlation of the interpolated unipolar signals. By effectively reducing interelectrode spacing through interpolation, compliance with the underlying assumptions improves significantly. Accurate omnipolar mapping requires tighter interelectrode spacing than current catheters provide. We show that interpolation reliably enhances spatial resolution, enabling assumption compliance without hardware changes. A spacing of 0.5 mm defines Ultra-High Resolution, beyond which further gains are negligible. We establish a practical benchmark for future catheter design and signal processing.

Elisa Ramírez, Raul Alós, Johanna Tonko, Samuel Ruipérez-Campillo, Matthias AF Gsell, Gernot Plank, Pier Lambiase, José Millet, Francisco Castells

Computers in Biology and Medicine

15.01.2026

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