{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:21:30Z","timestamp":1763886090262,"version":"3.45.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>Accurate prognosis of coma emergence is difficult because bedside behavioral scales can fail to detect residual consciousness. Auditory oddball event-related potentials (ERPs) offer a physiological readout, but single-component markers (e.g., MMN or P3) have limited sensitivity and generalizability. We present CORE-Coma, a deep learning framework for full-waveform ERP analysis, trained exclusively on healthy controls and evaluated zero-shot in coma patients. We analyzed ERPs from 39 healthy controls and 8 coma patients in the intensive care unit (ICU), segmenting EEG recordings into ~5-minute sub-blocks to capture temporal fluctuations. We define two complementary, model-derived metrics: a time-resolved ERP Separability Score (ESS) and a subject-level Global ERP Separability Index (GESI). Controls showed near-ceiling standard\u2013deviant separability (ROC AUC=0.99), while separability was reduced in coma (ROC AUC=0.68). CORE-Coma identified all patients who emerged from coma (3\/3; sensitivity 100%) and 4\/5 patients who did not emerge (specificity 80%), yielding accuracy=87.5% (7\/8). ESS revealed temporal fluctuations (waxing\u2013waning) of responsiveness in coma at ~5-minute resolution, absent in controls. SHAP explanations localized influential features, including frontocentral electrodes and time windows consistent with canonical oddball components: 100\u2013150 ms (N1\/MMN) and 270\u2013370 ms (P3a\/P3b). By combining bedside-feasible scalp EEG with time-resolved and subject-level metrics, CORE-Coma offers an etiology-agnostic approach to coma prognosis. Prospective multicenter studies are needed to validate performance and support clinical deployment.<\/jats:p>","DOI":"10.1609\/aaaiss.v7i1.36918","type":"journal-article","created":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:18:50Z","timestamp":1763885930000},"page":"456-465","source":"Crossref","is-referenced-by-count":0,"title":["CORE-Coma: Deep Learning Framework for Coma Prognosis from\nAuditory Event-Related Potentials"],"prefix":"10.1609","volume":"7","author":[{"given":"Elham","family":"Bagheri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paniz","family":"Tavakoli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adianes","family":"Herrera-Diaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rober","family":"Boshra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Kolesar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alison","family":"Fox-Robichaud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John F.","family":"Connolly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Reilly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,11,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36918\/39056","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36918\/39056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:18:51Z","timestamp":1763885931000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/36918"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,11,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v7i1.36918","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]}}}