基于改进YOLO模型的轻量化脑电图肌电伪影检测方法

doi:10.3969/j.issn.0258-8021.2025.01.012

Abstract
Figure/Table
References
Related Citation (15)

[1] Thomas J, Thangavel P, Peh WY, et al. Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: A six-center study[J]. International Journal of Neural Systems, 2021, 31(5): 2050074.
[2] Rasheed K, Qayyum A, Qadir J, et al. Machine learning for predicting epileptic seizures using EEG signals: a review[J]. IEEE Reviews in Biomedical Engineering, 2020, 14: 139-155.
[3] 于淑月, 李想, 于功敬, 等. 脑机接口技术的发展与展望[J]. 计算机测量与控制, 2019, 27(10): 5-12.
[4] Guerrero-Mosquera C, Navia-Vázquez A. Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data[J]. IET Signal Processing, 2012, 6(2): 99-106.
[5] Di Mauro M, Longo M, Postiglione F, et al. Service function chaining deployed in an NFV environment: an availability modeling[C]//2017 IEEE Conference on Standards for Communications and Networking (CSCN). Helsinki: IEEE, 2017: 42-47.
[6] Tejaswini V, Susitra D. Hybrid PSO-WOA for solving ORPD problem under unbalanced conditions[J]. Journal of Computational Mechanics, Power System and Control, 2019, 2(2): 10-20.
[7] Bartunov S, Santoro A, Richards B, et al. Assessing the scalability of biologically-motivated deep learning algorithms and architectures[J]. Advances in Neural Information Processing Systems, 2018, 31: 9368-9378.
[8] Dora M, Holcman D. Adaptive single-channel EEG artifact removal with applications to clinical monitoring[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 286-295.
[9] Narmada A, Shukla MK. A novel adaptive artifacts wavelet Denoising for EEG artifacts removal using deep learning with Meta-heuristic approach[J]. Multimedia Tools and Applications, 2023, 82(26): 40403-40441.
[10] Dhull SK, Singh KK. EEG artifact removal using canonical correlation analysis and EMD-DFA based hybrid denoising approach[J]. Procedia Computer Science, 2023, 218: 2081-2090.
[11] Ghosh R, Phadikar S, Deb N, et al. Automatic eyeblink and muscular artifact detection and removal from EEG signals using k-nearest neighbor classifier and long short-term memory networks[J]. IEEE Sensors Journal, 2023, 23(5): 5422-5436.
[12] Ingolfsson T M, Benatti S, Wang X, et al. Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers[J]. Scientific Reports, 2024, 14(1): 2980.
[13] Nayak A B, Shah A, Maheshwari S, et al. An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals[J]. Decision Analytics Journal, 2024, 10: 100420.
[14] Zhao Shuo, Gao Han, Li Xuran, et al. An outlier detection based two-stage EEG artifact removal method using empirical wavelet transform and canonical correlation analysis[J]. Biomedical Signal Processing and Control, 2024, 92: 106022.
[15] Redmon J. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016: 779-788.
[16] Acharya UR, Oh SL, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals[J]. Computers in Biology and Medicine, 2018, 100: 270-278.
[17] Gorjan D, Gramann K, De Pauw K, et al. Removal of movement-induced EEG artifacts: current state of the art and guidelines[J]. Journal of neural engineering, 2022, 19(1): 011004.