脑电图
睡眠阶段
睡眠(系统调用)
公制(单位)
计算机科学
频道(广播)
相似性(几何)
人工智能
模式识别(心理学)
多导睡眠图
心理学
神经科学
图像(数学)
工程类
电信
运营管理
操作系统
作者
Yuyang You,Xiaoyu Guo,Yang Zhihong,Wenjing Shan
出处
期刊:Biomedicines
[MDPI AG]
日期:2023-01-24
卷期号:11 (2): 327-327
被引量:4
标识
DOI:10.3390/biomedicines11020327
摘要
Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen’s kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging.
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