The neonatal period is the most vulnerable time for the development of
seizures. Seizures in the immature brain lead to detrimental consequences,
therefore require early diagnosis. The gold-standard for neonatal seizure
detection currently relies on continuous video-EEG monitoring; which involves
recording multi-channel electroencephalogram (EEG) alongside real-time video
monitoring within a neonatal intensive care unit (NICU). However, video-EEG
monitoring technology requires clinical expertise and is often limited to
technologically advanced and resourceful settings. Cost-effective new
techniques could help the medical fraternity make an accurate diagnosis and
advocate treatment without delay. In this work, a novel explainable deep
learning model to automate the neonatal seizure detection process with a
reduced EEG montage is proposed, which employs convolutional nets, graph
attention layers, and fully connected layers. Beyond its ability to detect
seizures in real-time with a reduced montage, this model offers the unique
advantage of real-time interpretability. By evaluating the performance on the
Zenodo dataset with 10-fold cross-validation, the presented model achieves an
absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall,
respectively.

基于可解释深度学习模型的新生儿癫痫自动检测方法，具有实时可解释性，对于 Zenodo 数据集的性能评估中，该模型在曲线下面积（AUC）和召回率方面分别实现了 8.31% 和 42.86% 的绝对改进。

应用可解释人工智能进行基于脑电图缩减画面的新生儿癫痫检测

Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure  Detection

Automated seizure detection and classification from electroencephalography
(EEG) can greatly improve seizure diagnosis and treatment. However, several
modeling challenges remain unaddressed in prior automated seizure detection and
classification studies: (1) representing non-Euclidean data structure in EEGs,
(2) accurately classifying rare seizure types, and (3) lacking a quantitative
interpretability approach to measure model ability to localize seizures. In
this study, we address these challenges by (1) representing the spatiotemporal
dependencies in EEGs using a graph neural network (GNN) and proposing two EEG
graph structures that capture the electrode geometry or dynamic brain
connectivity, (2) proposing a self-supervised pre-training method that predicts
preprocessed signals for the next time period to further improve model
performance, particularly on rare seizure types, and (3) proposing a
quantitative model interpretability approach to assess a model's ability to
localize seizures within EEGs. When evaluating our approach on seizure
detection and classification on a large public dataset, we find that our GNN
with self-supervised pre-training achieves 0.875 Area Under the Receiver
Operating Characteristic Curve on seizure detection and 0.749 weighted F1-score
on seizure classification, outperforming previous methods for both seizure
detection and classification. Moreover, our self-supervised pre-training
strategy significantly improves classification of rare seizure types.
Furthermore, quantitative interpretability analysis shows that our GNN with
self-supervised pre-training precisely localizes 25.4% focal seizures, a 21.9
point improvement over existing CNNs. Finally, by superimposing the identified
seizure locations on both raw EEG signals and EEG graphs, our approach could
provide clinicians with an intuitive visualization of localized seizure
regions.

本研究提出了一种基于 GNN 的自监督预训练模型，能够捕捉电极几何或动态脑连接的脑电图图形结构，提高罕见癫痫类型的检测和分类精度。此外，提出了一种定量的模型可解释性方法来评估模型在 EEGs 内定位癫痫的能力。实验证明，GNN 的自监督预训练方法在癫痫检测和分类上都优于以往的方法，能够精确定位约 25.4% 的局部癫痫，可以为临床医生提供直观的癫痫局部化信息。

自监督图神经网络提高脑电图癫痫分析

Self-Supervised Graph Neural Networks for Improved  Electroencephalographic Seizure Analysis

Automated seizure detection using clinical electroencephalograms is a
challenging machine learning problem because the multichannel signal often has
an extremely low signal to noise ratio. Events of interest such as seizures are
easily confused with signal artifacts (e.g, eye movements) or benign variants
(e.g., slowing). Commercially available systems suffer from unacceptably high
false alarm rates. Deep learning algorithms that employ high dimensional models
have not previously been effective due to the lack of big data resources. In
this paper, we use the TUH EEG Seizure Corpus to evaluate a variety of hybrid
deep structures including Convolutional Neural Networks and Long Short-Term
Memory Networks. We introduce a novel recurrent convolutional architecture that
delivers 30% sensitivity at 7 false alarms per 24 hours. We have also evaluated
our system on a held-out evaluation set based on the Duke University Seizure
Corpus and demonstrate that performance trends are similar to the TUH EEG
Seizure Corpus. This is a significant finding because the Duke corpus was
collected with different instrumentation and at different hospitals. Our work
shows that deep learning architectures that integrate spatial and temporal
contexts are critical to achieving state of the art performance and will enable
a new generation of clinically-acceptable technology.

本研究通过使用 TUH EEG Seizure Corpus 数据集，评估了一种新型的循环卷积神经网络，该网络在每 24 小时内的假警报率为 7 次，灵敏度达到 30%。研究表明，深度学习结构集成空间和时间信息对于达到最先进的表现至关重要，并将推动出一代临床可接受的技术。