.png){kind=icon}
返回
STDP-based adaptive graph convolutional networks for automatic sleep staging
.png){kind=icon}
OA
AI
DOI：10.3389/fnins.2023.1158246.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.
Keyword:
sleep stage classification
graph convolutional network (GCN)
spike-timing-dependent plasticity (STDP)
domain adaptation
Polysomnography (PSG)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
1.6W
被引数:
5.3W
机构
引用论文
Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events睡眠呼吸事件评分规则: 更新2007 AASM睡眠及相关事件评分手册
OAAI
OAAI分享收藏Knowledge, attitude, and practice of breast self-examination among female nurses in Aminu Kano teaching hospital, Kano, Nigeria尼日利亚卡诺Aminu Kano教学医院女护士乳房自我检查的知识，态度和实践
PREAI
分享收藏A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals用于从EEG信号检测睡眠阶段的图-时间融合双输入卷积神经网络OAAI
OAAI分享收藏Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring使用人工智能对睡眠进行评分可以量化睡眠阶段的模糊性: 基于多个专家评分器和自动评分的催眠.png)
SLEEPOAAI
SLEEPIF4.9
OAAI分享收藏
查看更多内容