Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential for automating ECG classification, many existing models rely on large, computationally intensive architectures that hinder practical deployment. In this paper, we present an empirical study of convolutional neural network (CNN) architectures, exploring tradeoffs between diagnostic accuracy and computational efficiency. We benchmark two established baselines: AttiaNet, a compact model composed of sequential temporal and spatial blocks, and DeepResidualCNN, the winning architecture of the 2021 PhysioNet/Computing in Cardiology Challenge. Building on these, we propose three lightweight models: (i) ParallelCNN, which employs dual temporal and spatial branches for parallel pattern extraction; (ii) ParallelCNNew, a variant with symmetric weight initialization for balanced feature learning; and (iii) SimpleNet, a streamlined architecture that jointly processes temporal and spatial dimensions. Our experiments span three publicly available 12-lead ECG datasets from Germany, China, and the United States, covering binary, multiclass, and multilabel classification tasks across diverse patient populations. We further evaluate the impact of integrating low-cost demographic metadata (age and sex) to improve performance with minimal overhead. To ensure fair comparison, we introduce a unified Efficiency Score that integrates model size, inference speed, memory usage, and AUC performance. By balancing diagnostic performance and efficiency, our models offer a scalable and viable foundation for next-generation AI systems in cardiovascular care.

At the beginning of the COVID-19 pandemic, HealthLink BC (HLBC) rapidly integrated physicians into the triage process of their virtual healthcare service to improve patient outcomes and satisfaction with this service and preserve health care system capacity. We present the design and implementation of a visual analytics tool, VIVA (Virtual healthcare Interactions using Visual Analytics), to support HLBC in analysing various forms of usage data from the service. We abstract HLBC's data and data analysis tasks, which we use to inform our design of VIVA. We also present the interactive workflow abstraction of Scan, Act, Adapt. We validate VIVA's design through three case studies with stakeholder domain experts. We also propose the Controllability Through Configuration model to conduct and analyze design studies, and discuss architectural evolution of VIVA through that lens. It articulates configuration, both that specified by a developer or technical power user and that constructed automatically through log data from previous interactive sessions, as a bridge between the rigidity of hardwired programming and the time-consuming implementation of full end-user interactivity.

Remote photoplethysmography (rPPG) allows to optically measure vital signs, such as heart rate, without physical contact. Signal quality impacts the reliability of derived measurements, but the effects of influential factors in rPPG are not well understood. This research specifically examines five factors that are hypothesized to be important: camera type, skin tone, age, gender, and body mass index (BMI). We investigated these factors using a purposely collected dataset from a comparatively large and diverse population (n=126). For each participant, two simultaneous video streams were recorded using different quality hardware to allow to study the effect of choice of camera type. Statistical analysis based on two quality metrics (signal-to-noise ratio and mean absolute error in heart rate measurements) shows that the choice of camera type is important. Generalized linear mixed models provide evidence of a compounded effect between low quality camera and young age with respect to both signal quality decrease and an increase in measurement error. Analysis of the models coefficients brings evidences that darker skin tone also appears to reduce signal quality, but the results are statistically inconclusive in what concerns heart rate measurements. We observe no significant effect of gender or BMI on rPPG in this study. We believe that comprehensive understanding of the influential factors in rPPG will lead to more reliable and inclusive technologies.