ECG Vertex-Time Activation Representation for IoT-XR Heart Monitoring.

Electrocardiogram (ECG) monitoring via wearable devices generates rich physiological data, yet conventional time-domain representations offer limited support for spatial interpretation and immersive visualization. We introduce a novel vertex-time representation of the ECG signal, introducing the Ideal Vertex Time (IVT) and Eigen Vertex Time (EVT) vectors which map temporal activation intervals and eigenbeat amplitudes onto a three-dimensional heart point cloud. A key novelty is that this approach serves a dual purpose: it enables graph-spectral ECG feature extraction for classification tasks, and it drives immersive rendering within an Internet of Things pipeline, based on MQTT, cloud processing, and Extended Reality visualization. This work is presented as a proof of concept. The mapping from ECG landmarks to anatomical regions is model-driven and has not been independently validated against patient-specific electrophysiological measurements. Herein, the framework is evaluated for classification purposes on an affective-state dataset and integrated into an Internet-of-Things-enabled e-health monitoring pipeline, whose prototype replays previously recorded ECG signals rather than acquiring them from a wearable device. Overall, this work extends conventional time-domain electrocardiographic analysis toward a spatially structured and immersive monitoring framework. No comparison with conventional ECG descriptors was performed; thus, no advantage over such features is claimed. Its clinical utility remains to be assessed within a patient-specific monitoring pipeline, computing the vertex-time features alongside conventional ECG descriptors and weighting them jointly to identify the best representation for each patient.