论文标题
CNN转换器深度学习模型，用于实时睡眠阶段分类中的无线无线设备
A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device
论文作者
论文摘要
本文提出了一个基于单渠道脑电图数据的自动睡眠阶段分类的深度学习（DL）模型。 DL模型具有卷积神经网络（CNN）和变压器。该模型旨在运行能源和内存约束设备，用于实时操作，并通过本地处理进行实时操作。来自公开可用的睡眠数据集中的FPZ-CZ EEG信号用于训练和测试模型。使用四个卷积过滤器层提取特征并降低数据维度。然后，使用变压器来学习数据的时变特征。为了提高绩效，我们还在推论（即预测）阶段之前实施了特定主题的培训。在特定于主题的训练中，F1得分分别为0.91、0.37、0.84、0.877和0.73。该模型的性能与最先进的计算成本相当。我们在低成本的Arduino Nano 33 BLE板上测试了提出的模型的简化版本，并且功能完全且准确。将来，将开发出具有Edge DL的完全集成的无线EEG传感器，用于临床前和临床实验（例如实时睡眠调制）的睡眠研究。
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The Fpz-Cz EEG signals from a publicly available Sleep-EDF dataset are used to train and test the model. Four convolutional filter layers were used to extract features and reduce the data dimension. Then, transformers were utilized to learn the time-variant features of the data. To improve performance, we also implemented a subject specific training before the inference (i.e., prediction) stage. With the subject specific training, the F1 score was 0.91, 0.37, 0.84, 0.877, and 0.73 for wake, N1-N3, and rapid eye movement (REM) stages, respectively. The performance of the model was comparable to the state-of-the-art works with significantly greater computational costs. We tested a reduced-sized version of the proposed model on a low-cost Arduino Nano 33 BLE board and it was fully functional and accurate. In the future, a fully integrated wireless EEG sensor with edge DL will be developed for sleep research in pre-clinical and clinical experiments, such as real-time sleep modulation.
