Fraiwan Luay, Alkhodari Mohanad
Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan.
Med Biol Eng Comput. 2020 Jun;58(6):1383-1391. doi: 10.1007/s11517-020-02169-x. Epub 2020 Apr 12.
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.
新生儿重症监护病房（NICU）的新生儿睡眠分析对于诊断生命早期阶段的任何脑发育风险至关重要。本文对长短期记忆（LSTM）学习系统在新生儿睡眠阶段自动评分中的应用进行了研究。所开发的算法基于单通道脑电图记录的输入自动对睡眠阶段进行分类。截至目前，仅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睡眠阶段自动评分的方法。本研究使用了从匹兹堡大学脑电图记录中获取的总共5095个睡眠阶段信号。睡眠阶段由凯斯西储大学儿科神经科的一名医生进行标注，针对三个新生儿睡眠阶段，即清醒（W）、活跃睡眠（AS）和安静睡眠（QS）阶段，每60秒时段进行标注。信号通过归一化和滤波进行预处理。所得信号按照4折、6折和10折交叉验证方案进行划分。训练和分类过程使用基于预定义训练参数构建的双向LSTM网络分类器完成。最后，对所开发的算法进行评估，并给出一个完整的汇总表，报告本研究及其他前沿研究的结果。当前研究分别以91.37%、96.81%和94.43%的水平实现了较高的科恩卡帕（κ）系数、准确率和F1分数。基于混淆矩阵，总体真阳性率达到95.21%。所开发的算法在新生儿睡眠信号的睡眠阶段自动评分方面取得了有前景的结果。未来的工作包括改进LSTM架构和训练参数，以提高分类器的整体准确率。
