Key result
AI-based depth sensing and cameras accurately localize ECG electrodes within ~6 mm of electromagnetic tracking.
Why the study?
BSPMs enable detection of electrophysiological phenomena beyond standard 12-lead ECG, motivating automatic AI-based methods to detect electrode locations.
Do AI-based 3D and 2D camera methods accurately localize ECG electrodes for body surface potential maps compared to traditional imaging techniques?
Observational (n=7)
No
Do AI-based 3D and 2D camera methods accurately localize ECG electrodes for body surface potential maps compared to traditional imaging techniques?
AI-based 3D and 2D camera methods provide highly accurate and practical alternatives to traditional imaging for ECG electrode localization in body surface potential mapping.
Supports technical feasibility for BSPM electrode localization; leaves open clinical validation in patients.
Body surface potential maps (BSPMs) derived from multi-channel ECG recordings enable the detection and diagnosis of electrophysiological phenomena beyond the standard 12-lead ECG. In this work, we developed two AI-based methods for the automatic detection of location of the electrodes used for BSPM: a rapid method using a specialized 3D Depth Sensing (DS) camera and a slower method that can use any 2D camera. Both methods were validated on a phantom model and in 7 healthy volunteers. With the phantom model, both 3D DS camera and 2D camera method achieved an average localization error less than 2 mm when compared to CT-scan or an Electromagnetic Tracking System (ETS). With healthy volunteers, the 3D camera yielded average 3D Euclidean distances ranging from 2.61 ± 1.2 mm to 5.78 ± 3.09 mm depending on the patient, similar to that seen with 2D camera (ranging from 2.45 ± 1.32 mm to 5.88 ± 2.73 mm). These results demonstrate high accuracy and provide practical alternatives to traditional imaging techniques, potentially enhancing the interest of BSPMs in a clinical setting.
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Ghebouli et al. (2025) conducted an observational in Healthy volunteers (n=7). 3D depth sensing camera and 2D camera AI-based methods vs. Electromagnetic Tracking System (ETS) and CT-scan was evaluated on 3D Euclidean distance (localization error) compared to reference methods. AI-based 3D depth sensing and 2D camera methods accurately localized ECG electrodes in healthy volunteers, yielding average 3D Euclidean distance errors of 2.45 to 5.88 mm compared to an electromagnetic tracking system.
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