小児集中治療室におけるリアルタイム発作検出のためのマルチ特徴量法

Continuous electroencephalogram (cEEG) monitoring is an important technique used in detecting electroclinical seizures in the pediatric intensive care unit (PICU). This study developed an artificial intelligence method for the real-time automatic detection of seizures in the PICU.

We designed an artificial intelligence method to analyze EEG, electromyography (EMG), and electrocardiography (ECG) signals, detecting features from multiple dimensions, extracting candidate signal fragments in real time, and analyzing the relevant indicators of these fragments to determine whether they indicate electroclinical seizures. We tested the sensitivity and specificity of the detection system on patients with seizures who were hospitalized in the PICU of Peking University First Hospital and received cEEG monitoring.

A total of 28 PICU patients were collected, including 17 boys and 11 girls, with a median age of 4.30 (1.02-7.00) years. Sixteen patients had convulsive status epilepticus, of which eight were generalized and eight were focal. Twelve patients had cluster seizures, of which seven were generalized and five were focal. A total of 218.73 h of EEG data were collected from all 28 EEG records. The electroencephalography physician annotated a total of 1561 seizures, whereas the algorithm detected a total of 1095 seizures. The overall detection sensitivity was 94%, and the overall false detection rate was 0.18 (0.03-0.28)/h. There was no statistical difference in the sensitivity and false detection rate between focal and generalized seizures.

The detection system has high sensitivity and specificity, suggesting great potential for future real-time automatic detection of electroclinical seizures in the PICU.

We developed a computer program that helps doctors quickly detect seizures in critically ill children by analyzing brain activity, muscle movements, and heart rate signals. The system was designed specifically for the challenging environment of pediatric intensive care units, where timely seizure detection is particularly important but often difficult. The program reliably identified seizures that showed both brain activity and physical signs, matching doctors' diagnoses. This technology marks an important step toward better monitoring and care for critically ill children with seizures.