PDF(Pubmed)
Abstract：
An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients\' vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly.
摘要：
重症监护病房（ICU）是医院中需要重症监护的患者的特殊病房。它配备了许多监测患者生命体征的仪器，并由医务人员支持。然而,持续的监测需要大量的医疗工作量。为了减轻负担，我们的目标是开发一种自动检测模型，以监测何时发生脑部异常。在这项研究中,我们专注于脑电图（EEG），持续监测患者的脑电活动。主要用于脑功能障碍的诊断。我们提出了基于门控循环单元（基于GRU）的模型，用于检测大脑异常；它可以预测尖峰或锐波是否在短时间窗口内发生。根据香蕉蒙太奇的设置，所提出的模型同时利用多个通道的特征来检测异常。它受过训练，已验证，并在分离的脑电图数据上进行测试，灵敏度测试性能达到90%以上，特异性，平衡的准确性。所提出的异常检测模型精确地检测到尖峰或锐波的存在；它将通知ICU医务人员，谁可以提供立即的后续治疗。因此,它可以显著减少ICU的医疗工作量。
