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.