We analyzed 1340 adult POC EEGs acquired with the Ceribell EEG system.  Two or more experts marked epochs of seizure and other pathological activity; from these an EEG label was assigned per file per reviewer.  EEGs were labeled as SE if the seizures’ cumulative duration over any 5-min window was ≥4.5min or if seizures were present for ≥20% of any 1-hour window.  The Clarity_v8 AI maximum estimated seizure burden (SzB) was extracted per file.  Detection of SE was evaluated at SzB ≥50% and ≥90%, including if any 1-hour window had seizures for ≥12-min. We also evaluated detection of seizures/SE at ≥1% and ≥10% SzB.

Of 1340 EEGs, expert consensus identified 25 EEGs meeting criteria for SE and 51 with non-SE seizures. For SE detection, Clarity_v8 AI at SzB ≥90% correctly identified 24 cases for a 96.0% sensitivity, with 94.8% specificity and 99.9% negative predictive value (NPV). For SzB ≥50%, the sensitivity, specificity, and NPV were 96%, 93.3%, and 99.9%, respectively.  In the missed SE case, Clarity’s SzB was 10%. For seizures/SE detection, with SzB ≥10%, the sensitivity, specificity, and NPV were 88.2%, 86.1%, and 99.2% respectively. And with SzB ≥1%, the sensitivity was 96.1% with a specificity of 74.0% and a NPV of 99.7%.

In a large real-world dataset, Clarity_v8 AI demonstrated high performance for SE and seizure detection. Its high NPV offers a clinically useful tool for immediate rule out of seizures and SE during neuroemergencies, underscoring the value of AI algorithms as flexible tools to triage patients and potentially reducing empiric treatments.