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

Background: Neonatal seizures are a neurological emergency that require
urgent treatment. They are hard to diagnose clinically and can go undetected if
EEG monitoring is unavailable. EEG interpretation requires specialised
expertise which is not widely available. Algorithms to detect EEG seizures can
address this limitation but have yet to reach widespread clinical adoption.
Methods: Retrospective EEG data from 332 neonates was used to develop and
validate a seizure-detection model. The model was trained and tested with a
development dataset ($n=202$) that was annotated with over 12k seizure events
on a per-channel basis. This dataset was used to develop a convolutional neural
network (CNN) using a modern architecture and training methods. The final model
was then validated on two independent multi-reviewer datasets ($n=51$ and
$n=79$).
Results: Increasing dataset and model size improved model performance:
Matthews correlation coefficient (MCC) and Pearson's correlation ($r$)
increased by up to 50% with data scaling and up to 15% with model scaling. Over
50k hours of annotated single-channel EEG was used for training a model with 21
million parameters. State-of-the-art was achieved on an open-access dataset
(MCC=0.764, $r=0.824$, and AUC=0.982). The CNN attains expert-level performance
on both held-out validation sets, with no significant difference in inter-rater
agreement among the experts and among experts and algorithm ($\Delta \kappa <
-0.095$, $p>0.05$).
Conclusion: With orders of magnitude increases in data and model scale we
have produced a new state-of-the-art model for neonatal seizure detection.
Expert-level equivalence on completely unseen data, a first in this field,
provides a strong indication that the model is ready for further clinical
validation.

新生儿癫痫发作的检测模型使用大规模数据和模型规模，达到了国际领先水平，并具有与专家水平的等效性。