Nanyang Technological University, Singapore.
Fortis Hospital Mulund, Mumbai, India.
Int J Neural Syst. 2021 Jun;31(6):2150016. doi: 10.1142/S0129065721500167. Epub 2021 Mar 26.
Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.
脑电图（EEG）中的病理性减慢被广泛研究用于诊断神经障碍。目前，减慢检测的金标准是专家对 EEG 的视觉检查，这种方法既耗时又主观。为了解决这些问题，我们提出了三种自动检测 EEG 中减慢的方法：基于阈值的检测系统（TDS）、基于浅层学习的检测系统（SLDS）和基于深度学习的检测系统（DLDS）。这些系统在通道、片段和 EEG 级别上进行评估。这三个系统通过在单个通道上检测减慢来进行预测，并且这些检测结果在直方图中排列以在片段和 EEG 级别上检测减慢。我们通过在美国、新加坡和印度的四个数据集的留一受试者外（LOSO）交叉验证（CV）和留一机构外（LOIO）CV 来评估系统。DLDS 取得了最佳的总体结果：在通道、片段和 EEG 级别上，LOIO CV 平均平衡准确率（BAC）分别为 71.9%、75.5%和 82.0%，LOSO CV 平均 BAC 分别为 73.6%、77.2%和 81.8%。通道和片段级别的性能与专家的内部评级协议（IRA）的 72.4%和 82%相当。DLDS 可以在 4 秒内处理 30 分钟的 EEG，并可以部署来帮助临床医生解读 EEG。
