Neonatal sleep analysis at the neonatal intensive care units (NICU) is critical for the diagnosis of any brain growth risks during the early stages of life. In this paper, an investigation is carried out on the use of a long short-term memory (LSTM) learning system in automatic sleep stage scoring in neonates. The developed algorithm automatically classifies sleep stages based on inputs from a single channel EEG recording. Up to this date, only a single study have developed an approach for automatic sleep stage scoring in neonatal sleep signals using deep neural network (DNN). A total of 5095 sleep stages signals acquired from EEG recordings of the University of Pittsburgh are used in this study. The sleep stages are annotated by a medical doctor from the Pediatric Neurology Department of Case Western Reserve University for three neonatal sleep stages including the awake (W), active sleep (AS), and quiet sleep (QS) stages on every 60-s epoch. The signals are pre-processed through normalization and filtering. The resulted signals are divided following 4-, 6-, and 10-fold cross-validation schemes. The training and classification process is done using a bi-directional LSTM network classifier built with pre-defined training parameters. At the end, the developed algorithm is evaluated along with a complete summary table that reports the results of this study and other state-of-the-art studies. The current study achieved high levels of Cohen's kappa (κ), accuracy, and F1 score with 91.37%, 96.81%, and 94.43%, respectively. Based on the confusion matrix, the overall true positives percentage reached 95.21%. The developed algorithm gave promising results in automatic sleep stage scoring in neonatal sleep signals. Future work include LSTM architecture and training parameters improvements to enhance the overall accuracy of the classifier.

中文翻译：

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使用长期短期记忆学习系统进行新生儿睡眠阶段识别。

新生儿重症监护病房（NICU）的新生儿睡眠分析对于生命早期诊断任何脑部生长风险至关重要。本文研究了在新生儿自动睡眠阶段评分中使用长短期记忆（LSTM）学习系统的情况。所开发的算法根据单通道EEG记录的输入自动对睡眠阶段进行分类。迄今为止，只有一项研究开发了一种使用深度神经网络（DNN）对新生儿睡眠信号进行自动睡眠阶段评分的方法。这项研究使用了从匹兹堡大学的脑电图记录中获得的总共5095个睡眠阶段信号。凯斯西储大学儿科神经病学系的一名医生对睡眠阶段进行了注释，该阶段包括每60秒一次的清醒（W），主动睡眠（AS）和安静睡眠（QS）阶段的三个新生儿睡眠阶段。 。通过归一化和滤波对信号进行预处理。所得信号按照4倍，6倍和10倍交叉验证方案进行划分。训练和分类过程是通过使用预先定义的训练参数构建的双向LSTM网络分类器完成的。最后，对开发的算法以及完整的汇总表进行评估，该汇总表报告了本研究和其他最新研究的结果。当前的研究获得了高水平的Cohenκ（κ），准确性和F1分数，分别为91.37％，96.81％和94.43％。基于混淆矩阵，总真实阳性百分比达到95.21％。所开发的算法在新生儿睡眠信号的自动睡眠阶段评分中给出了令人鼓舞的结果。未来的工作包括LSTM体系结构和训练参数的改进，以提高分类器的整体准确性。