Research on Deep Learning Analysis Model of Sleep ElectroEncephalography Signals for Anxiety Improvement

HUANG Chen; MA Yaolong; ZHANG Yan; WANG Shihui; YANG Chao; SONG Jianhua; CHEN Kansong; YANG Weiping

doi:10.11999/JEIT241123

Volume 47 Issue 8

Aug. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2935-2944

HUANG Chen, MA Yaolong, ZHANG Yan, WANG Shihui, YANG Chao, SONG Jianhua, CHEN Kansong, YANG Weiping. Research on Deep Learning Analysis Model of Sleep ElectroEncephalography Signals for Anxiety Improvement[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2935-2944. doi: 10.11999/JEIT241123

Citation:

HUANG Chen, MA Yaolong, ZHANG Yan, WANG Shihui, YANG Chao, SONG Jianhua, CHEN Kansong, YANG Weiping. Research on Deep Learning Analysis Model of Sleep ElectroEncephalography Signals for Anxiety Improvement[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2935-2944. doi: 10.11999/JEIT241123

Citation:

HUANG Chen, MA Yaolong, ZHANG Yan, WANG Shihui, YANG Chao, SONG Jianhua, CHEN Kansong, YANG Weiping. Research on Deep Learning Analysis Model of Sleep ElectroEncephalography Signals for Anxiety Improvement[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2935-2944. doi: 10.11999/JEIT241123

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Research on Deep Learning Analysis Model of Sleep ElectroEncephalography Signals for Anxiety Improvement

doi: 10.11999/JEIT241123 cstr: 32379.14.JEIT241123

HUANG Chen^{1, 2, 3, 4},
MA Yaolong^{1, 2, 3, 4},
ZHANG Yan^{1, 2, 3, 4
,
,},
WANG Shihui^{1, 2, 3, 4},
YANG Chao^{1, 2, 3, 4},
SONG Jianhua^{2, 3, 4, 5},
CHEN Kansong^{1, 2, 3, 4},
YANG Weiping⁶

1.
School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
2.
Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Wuhan 430062, China
3.
Hubei Key Laboratory of Big Data Intelligent Analysis and Application, Wuhan 430062, China
4.
Hubei Province Project of Key Research Institute of Humanities and Social Sciences at Universities, Wuhan 430062, China
5.
School of Cyber Science and Technology, Hubei University, Wuhan 430062, China
6.
Faculty of Education, Hubei University, Wuhan 430062, China

Funds: Wuhan Knowledge Innovation Special Project(202311901251001), Hubei Province Science and Technology Plan Major Science and Technology Project (2024BAA008), The Major Research Projects in Hubei Province(2023BAA018), The Key Projects of Science and Technology in Shenzhen (2020N061)

Received Date: 2024-12-23
Rev Recd Date: 2025-07-02

Available Online: 2025-07-25

Publish Date: 2025-08-27

Abstract

Abstract

Objective Anxiety is a common emotional disorder, characterized by excessive worry and fear, which negatively affects mental, physical, and social well-being. A bidirectional relationship exists between anxiety and sleep, poor sleep quality worsens anxiety symptoms, and anxiety disrupts normal sleep patterns. ElectroEncephaloGraphy (EEG) signals provide a non-invasive and informative means to investigate brain activity, making them useful for studying the neurophysiological underlying this association. However, conventional EEG analysis methods often fail to capture the complex, multiscale features needed to assess anxiety modulation during sleep. This study proposes an Improved Feature Pyramid Network (IFPN) model to enhance EEG analysis in sleep settings, with the aim of improving the detection and interpretation of anxiety-related brain activity. Methods The IFPN model comprises a preprocessing module, feature extraction module, and classification module, each being optimized for analyzing EEG signals related to anxiety during sleep. The preprocessing module applies Z-score normalization to EEG signals from individuals with anxiety to standardize signal amplitude across channels. Noise artifacts are reduced using a denoising process based on a feature pyramid network. Preprocessed signals are then converted into brain entropy topographies using Singular Spectral Entropy (SSE), which quantifies signal complexity. These entropy maps are processed by the IFPN backbone, which incorporates convolutional layers, SSE-guided upsampling, and lateral connections to enable multiscale feature fusion. The resulting features are input to a modified ResNet-50 network for classification, with SSE-based regularization applied to enhance model robustness and accuracy. The model is evaluated using two independent EEG datasets: a sleep deprivation dataset and a cognitive-state EEG dataset, both comprising participants with levels of anxiety. Results and Discussions The experimental results demonstrate that the IFPN model improves the detection of anxiety-related features in EEG signals during sleep. Spectral power analysis shows a significant reduction in β-band power after sleep, reflecting decreased hyperarousal commonly associated with anxiety. In Dataset 1, β-band power declines from 16% to 13% (p < 0.01), and in Dataset 2, from 19.5% to 15% (p < 0.05). This is accompanied by an increase in the θ/β power ratio, suggesting a shift toward a more relaxed neural state post-sleep. The IFPN model achieves 85% accuracy in identifying severe anxiety, outperforming baseline methods, which reach 78%. This improvement results from the model’s capacity to integrate multiscale features and selectively emphasize anxiety-related patterns, supporting more accurate classification of elevated anxiety states. Conclusions This study proposes an IFPN model for EEG analysis during sleep, with a focus on detecting anxiety-related neural activity. Unlike traditional approaches that rely on shallow architectures or frequency- limited metrics, the IFPN model addresses the multiscale and spatially heterogeneous nature of brain activity associated with anxiety. By incorporating SSE as a nonlinear dynamic feature, the model captures subtle regional and frequency-specific variations in EEG complexity. SSE functions as both a signal complexity metric and a functional biomarker of neural disorganization linked to anxiety. Integrated with the multiscale fusion capability of the feature pyramid network, SSE enhances the model’s ability to extract salient spatiotemporal features relevant to anxiety states. Experimental results show that the IFPN model outperforms existing methods in both accuracy and robustness, particularly in identifying severe anxiety, where conventional models often struggle due to noise and reduced discriminative performance. These findings highlight the model’s potential utility in clinical assessment of anxiety during sleep.
- Sleep,
- Anxiety,
- ElectroEncephaloGraphy (EEG),
- Improve Feature Pyramid Network (IFPN),
- Singular Spectral Entropy(SSE)

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References(22)

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