An automated artifact detection and rejection system for body surface gastric mapping

Background Body surface gastric mapping (BSGM) is a new clinical tool for gastric motility diagnostics, providing high‐resolution data on gastric myoelectrical activity. Artifact contamination was a key challenge to reliable test interpretation in traditional electrogastrography. This study aimed to introduce and validate an automated artifact detection and rejection system for clinical BSGM applications. Methods Ten patients with chronic gastric symptoms generated a variety of artifacts according to a standardized protocol (176 recordings) using a commercial BSGM system (Alimetry, New Zealand). An automated artifact detection and rejection algorithm was developed, and its performance was compared with a reference standard comprising consensus labeling by 3 analysis experts, followed by comparison with 6 clinicians (3 untrained and 3 trained in artifact detection). Inter‐rater reliability was calculated using Fleiss' kappa. Key Results Inter‐rater reliability was 0.84 (95% CI:0.77–0.90) among experts, 0.76 (95% CI:0.68–0.83) among untrained clinicians, and 0.71 (95% CI:0.62–0.79) among trained clinicians. The sensitivity and specificity of the algorithm against experts was 96% (95% CI:91%–100%) and 95% (95% CI:90%–99%), respectively, vs 77% (95% CI:68%–85%) and 99% (95% CI:96%–100%) against untrained clinicians, and 97% (95% CI:92%–100%) and 88% (95% CI:82%–94%) against trained clinicians. Conclusions & Inferences An automated artifact detection and rejection algorithm was developed showing >95% sensitivity and specificity vs expert markers. This algorithm overcomes an important challenge in the clinical translation of BSGM and is now being routinely implemented in patient test interpretations.