MLS-Net:利用小鼠多模态生理信号的自动睡眠阶段分类器

IF 4.9 3区 工程技术 Q1 CHEMISTRY, ANALYTICAL Biosensors-Basel Pub Date : 2024-08-22 DOI:10.3390/bios14080406
Chengyong Jiang, Wenbin Xie, Jiadong Zheng, Biao Yan, Junwen Luo, Jiayi Zhang
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引用次数: 0

摘要

过去几十年来,基于特征的统计机器学习和深度神经网络已被广泛用于自动睡眠阶段分类(ASSC)。基于特征的方法能清楚地洞察睡眠特征,所需计算能力低,但往往无法捕捉数据的时空背景。相比之下,深度神经网络可以直接处理原始睡眠信号,并提供卓越的性能。然而,它们的过度拟合、不一致的准确性和计算成本是限制其被最终用户接受的主要缺点。为了应对这些挑战,我们开发了一种新型神经网络模型--MLS-Net,它整合了神经网络和特征提取的优势,可用于小鼠的自动睡眠分期。MLS-Net 利用脑电图(EEG)、肌电图(EMG)和眼动(EMs)等多模态信号的时间和频谱特征作为输入,并结合双向长短期记忆(bi-LSTM)来有效捕捉睡眠信号固有的时空非线性特征。我们的研究表明,MLS-Net 在小鼠中的总体分类准确率达到 90.4%,快速眼动状态准确率达到 91.1%,灵敏度达到 84.7%,F1-分数达到 87.5%,在多模态数据集中的表现优于其他神经网络和基于特征的算法。
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MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial-temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial-temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.

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来源期刊
Biosensors-Basel
Biosensors-Basel Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
6.60
自引率
14.80%
发文量
983
审稿时长
11 weeks
期刊介绍: Biosensors (ISSN 2079-6374) provides an advanced forum for studies related to the science and technology of biosensors and biosensing. It publishes original research papers, comprehensive reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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