MAGSleepNet: Adaptively multi-scale temporal focused sleep staging model for multi-age groups
MAGSleepNet: Adaptively multi-scale temporal focused sleep staging model for multi-age groups
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DOI：
10.1016/j.eswa.2023.122549
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发表时间：
2023-11-20
影响因子：
8.5
通讯作者：
Chen，Wei
中科院分区：
文献类型：
--
作者：
Zhu，Hangyu;Guo，Yao;Chen，Wei
基于深度学习的自动睡眠分期方法已广泛应用于睡眠评分和睡眠诊断。然而，大多数方法在处理睡眠信号时只考虑单一的时间尺度。此外，这些方法仅限于针对单一年龄组或单一数据集。在本文中，我们提出了一个多尺度的时间聚焦睡眠分期模型MAGSleepNet，它可以同时用于多个年龄组。MAGSleepNet包括：(1)群体年龄分类（GAC）模块，可以对多年龄的流行病估计进行初步筛选；(2)维度扩展模块（DEM），可以扩展输入信号的维度；(3)提取多尺度特征和短时时间信息的顺序多尺度卷积神经网络（SMCNN）；(4)提取顺序时间信息的顺序时间编码器（STE）。此外，利用两个辅助任务分别补充短时时间信息和重新分配不同年龄组的概率，以增强模型的鲁棒性。MAGSleepNet在成人、儿童和婴儿中进行了评估，在MASS、CHAT和CHFU数据集上进行了测试，其累积率分别为86.7%、80.1%和66.5%，优于最先进的方法。基于该方法的优异性能，有望为具有较强通用性的多年龄组自动睡眠分期方法铺平道路。
Deep learning-based automatic sleep staging methods have been widely applied for sleep scoring and sleep diagnosis. However, most methods consider only a single temporal scale when dealing with sleep signals. Furthermore, these methods are limited to target only a single-age group or single dataset. In this paper, we propose a multi-scale temporally focused sleep staging model, MAGSleepNet, which can be used for multi-age groups simultaneously. MAGSleepNet consists of (1) a group age classification (GAC) module that can offer a preliminary screening on epidemic estimation of multiple age, (2) a dimensional expansion module (DEM) that can expand the dimension of the input signals, (3) a sequential multi-scale convolutional neural network (SMCNN) that extracts multi-scale features and short-time temporal information, and (4) sequence temporal encoder (STE) that extracts sequential temporal information. In addition, two auxiliary tasks are used to complement the short-time temporal information and to reassign the probabilities of different age groups to enhance the model robustness, respectively. The MAGSleepNet is evaluated in adult, child and infant, tested on MASS, CHAT, and CHFU datasets with accruacy of 86.7%, 80.1% and 66.5%, outperforming the state-of-the-art methods. Based on the excellent performance of the proposed method, it is expected to pave the way for automatic sleep staging methods with strong generalizability for multiple age groups.