Neonatal sleep stage identification using long short-term memory learning system
Neonatal sleep stage identification using long short-term memory learning system
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DOI：
10.1007/s11517-020-02169-x
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发表时间：
2020-04-12
影响因子：
3.2
通讯作者：
Alkhodari, Mohanad
中科院分区：
文献类型：
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作者：
Fraiwan, Luay;Alkhodari, Mohanad
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大脑生长风险至关重要。本文对长短期记忆（LSTM）学习系统在新生儿自动睡眠阶段评分中的应用进行了调查。开发的算法根据单通道脑电图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。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小儿神经病学系的医生注释，每 60 秒的三个新生儿睡眠阶段包括清醒 (W)、主动睡眠 (AS) 和安静睡眠 (QS) 阶段。信号通过归一化和滤波进行预处理。所得信号按照 4 倍、6 倍和 10 倍交叉验证方案进行划分。训练和分类过程是使用使用预定义训练参数构建的双向 LSTM 网络分类器完成的。最后，对开发的算法进行评估，并提供完整的汇总表，报告本研究和其他最先进研究的结果。目前的研究取得了高水平的Cohen kappa（kappa）、准确率和F1分数，分别为91.37%、96.81%和94.43%。基于混淆矩阵，总体真阳性率达到95.21%。开发的算法在新生儿睡眠信号的自动睡眠阶段评分方面给出了有希望的结果。未来的工作包括 LSTM 架构和训练参数改进，以提高分类器的整体准确性。
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 (kappa), 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.