癲癇偵測演算法應用於癲癇輔助診斷系統

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姓名

黃思齊(Szu-Chi Huang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

癲癇偵測演算法應用於癲癇輔助診斷系統
(Seizure Detection Algorithm for Auxiliary Epilepsy Diagnosis System)

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至系統瀏覽論文 (2028-7-23以後開放)

摘要(中)

本論文提出基於深度學習的癲癇偵測演算法應用於長時EEG分析，並基於機器學習提出穿戴式癲癇偵測演算法應用於非醫院環境之癲癇偵測。本論文使用CHB-MIT資料集與波恩資料集對演算法效能進行評估，實驗結果顯示本論文深度學習演算法具備最佳的分類效能。本論文深度學習模型在CHB-MIT資料集受試者相依測試平均f1-score為69.34%，跨受試者測試平均f1-score為37.31%。在波恩資料集本論文深度學習模型平均準確度為98.91%，穿戴式演算法平均準確度為97.13%。本論文將穿戴式演算法實現於超低功耗嵌入式系統計算演算法執行時間，實驗結果顯示本論文所提出熵之估算法能夠減少81.58%的計算時間，與近幾年演算法相比本論文穿戴式演算法提供相當的分類效能並具備最少計算時間。

摘要(英)

This thesis proposed a deep learning-based seizure detection algorithm for long-term EEG analysis and a machine learning-based wearable seizure detection algorithm for non-hospital seizure detection. We conducted an experiment on the CHB-MIT and Bonn datasets to evaluate the algorithm′s performance. The experimental results show that our deep learning algorithm has the best classification performance. For the CHB-MIT dataset, our deep learning model achieved an average f1-score of 69.34% in the subject dependence experiment and an average f1-score of 37.31% in the cross-subject experiment. In the Bonn dataset, the average accuracy of the deep learning model and the wearable algorithm is 98.91% and 97.13%, respectively. Our wearable algorithm is implemented on the ultra-low power embedded system and analysis the calculation time of the algorithm. The experimental results show that the proposed entropy estimation method can reduce the calculation time by 81.58%. Compared with the previous algorithms, our wearable algorithm provides a comparable classification performance and has the fastest inference speed.

關鍵字(中)

★ 深度學習
★ 機器學習
★ 腦電圖
★ 癲癇偵測
★ 端對端模型
★ 熵

關鍵字(英)

★ Deep learning
★ machine learning
★ EEG
★ seizure detection
★ end-to-end models
★ entropy

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
一、緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究動機與目的 4
1-4 內容大綱 5
二、穿戴式癲癇偵測演算法 6
2-1 穿戴式癲癇偵測演算法架構 6
2-2 LBPMAD特徵提取法 7
2-3 熵 8
2-4 邏輯回歸 9
三、深度學習癲癇偵測演算法 11
3-1 深度學習癲癇偵測模型架構 11
3-1-1 Inception 模組 12
3-1-2 Residual模組 12
3-1-3 分類器 13
3-2 網路訓練策略 13
四、實驗設計 15
4-1 癲癇資料集 15
4-1-1 波恩資料集 15
4-1-2 CHB-MIT資料集 16
4-2 長時EEG癲癇偵測實驗設計 19
4-2-1 CHB-MIT資料集受試者相依測試 19
4-2-2 CHB-MIT資料集跨受試者測試 20
4-2-3 波恩資料集癲癇腦波分類 20
4-3 長時EEG癲癇偵測之基線方法 21
4-3-1 FBCSP+SVM： 21
4-3-2 EEGNet-8,2： 22
4-3-3 Stacked 1D-CNN： 23
4-3-4 EEGWaveNet： 24
4-4 效能評估指標 25
五、實驗結果與討論 27
5-1 長時EEG癲癇偵測結果 27
5-2 深度學習模型錯誤輸出分析 30
5-2-1 雜訊 30
5-2-2 極端地類別非平衡 31
5-2-3 異質性 31
5-3 穿戴式癲癇演算法特徵無效性分析 32
5-4 波恩資料集癲癇腦波分類結果 34
5-5 穿戴式裝置演算法計算時間分析 38
六、結論與未來研究方向 41
6-1 結論 41
6-2 未來研究方向 42
論文發表列 44
參考文獻 45

參考文獻

[1] W. H. Organization, "Epilepsy: a public health imperative: summary," World Health Organization, 2019.
[2] M. Rashed-Al-Mahfuz et al., "A Deep Convolutional Neural Network Method to Detect Seizures and Characteristic Frequencies Using Epileptic Electroencephalogram (EEG) Data," IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-12, 2021, doi: 10.1109/JTEHM.2021.3050925.
[3] M. Yazid et al., "Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram," IEEE Access, vol. 9, pp. 150252-150267, 2021, doi: 10.1109/access.2021.3126065.
[4] J. A. French, "Refractory epilepsy: clinical overview," Epilepsia, vol. 48, pp. 3-7, 2007.
[5] Y. Kumar et al., "Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network," Signal, Image and Video Processing, vol. 8, no. 7, pp. 1323-1334, 2014.
[6] T. Zhang and W. Chen, "LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 8, pp. 1100-1108, 2017, doi: 10.1109/TNSRE.2016.2611601.
[7] S. Chen et al., "Automatic Diagnosis of Epileptic Seizure in Electroencephalography Signals Using Nonlinear Dynamics Features," IEEE Access, vol. 7, pp. 61046-61056, 2019, doi: 10.1109/access.2019.2915610.
[8] J. Na et al., "An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis," IEEE Access, vol. 9, pp. 73910-73923, 2021, doi: 10.1109/ACCESS.2021.3081767.
[9] M. R. Khan et al., "A low complexity patient-specific threshold based accelerator for the Grand-mal seizure disorder," in 2017 IEEE Biomedical Circuits and Systems Conference, 2017: IEEE, pp. 1-4.
[10] D. Sopic et al., "e-glass: A wearable system for real-time detection of epileptic seizures," in 2018 IEEE International Symposium on Circuits and Systems, 2018: IEEE, pp. 1-5.
[11] I. L. Olokodana et al., "EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals," IEEE Transactions on Consumer Electronics, vol. 67, no. 2, pp. 166-175, 2021, doi: 10.1109/tce.2021.3079399.
[12] A. F. Hussein et al., "Focal and Non-Focal Epilepsy Localization: A Review," IEEE Access, vol. 6, pp. 49306-49324, 2018, doi: 10.1109/access.2018.2867078.
[13] N. Lopac et al., "Detection of Non-Stationary GW Signals in High Noise From Cohen’s Class of Time–Frequency Representations Using Deep Learning," IEEE Access, vol. 10, pp. 2408-2428, 2022, doi: 10.1109/ACCESS.2021.3139850.
[14] T. Arias-Vergara et al., "Multi-channel spectrograms for speech processing applications using deep learning methods," Pattern Analysis and Applications, vol. 24, no. 2, pp. 423-431, 2021/05/01 2021, doi: 10.1007/s10044-020-00921-5.
[15] S. Sakib et al., "A Proof-of-Concept of Ultra-Edge Smart IoT Sensor: A Continuous and Lightweight Arrhythmia Monitoring Approach," IEEE Access, vol. 9, pp. 26093-26106, 2021, doi: 10.1109/ACCESS.2021.3056509.
[16] W. Liang et al., "Scalp EEG epileptogenic zone recognition and localization based on long-term recurrent convolutional network," Neurocomputing, vol. 396, pp. 569-576, 2020.
[17] Y. Zhang et al., "Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network," IEEE Trans Neural Syst Rehabil Eng, vol. 30, pp. 135-145, 2022, doi: 10.1109/TNSRE.2022.3143540.
[18] D. Lai et al., "Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks," IEEE Access, vol. 7, pp. 82501-82511, 2019, doi: 10.1109/ACCESS.2019.2923281.
[19] Y. Li et al., "Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, pp. 782-794, 2020, doi: 10.1109/TNSRE.2020.2973434.
[20] U. R. Acharya et al., "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals," Computers in Biology and Medicine, vol. 100, pp. 270-278, Sep 1 2018, doi: 10.1016/j.compbiomed.2017.09.017.
[21] X. Wang et al., "One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG," Neurocomputing, vol. 459, pp. 212-222, 2021.
[22] P. Thuwajit et al., "EEGWaveNet: Multiscale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection," IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5547-5557, 2022, doi: 10.1109/tii.2021.3133307.
[23] L. Duan et al., "An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2147-2157, 2022, doi: 10.1109/JBHI.2021.3138852.
[24] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
[25] K. He et al., "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[26] A. K. Tiwari et al., "Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals," IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 4, pp. 888-896, Jul 2017, doi: 10.1109/JBHI.2016.2589971.
[27] B. K.S et al., "Ictal EEG classification based on amplitude and frequency contours of IMFs," Biocybernetics and Biomedical Engineering, vol. 37, no. 1, pp. 172-183, 2017, doi: 10.1016/j.bbe.2016.12.005.
[28] H. He et al., "A progressive deep wavelet cascade classification model for epilepsy detection," Artificial Intelligence in Medicine, vol. 118, p. 102117, Aug 2021, doi: 10.1016/j.artmed.2021.102117.
[29] S. Pincus, "Approximate entropy (ApEn) as a complexity measure," Chaos, vol. 5, no. 1, pp. 110-117, Mar 1995, doi: 10.1063/1.166092.
[30] M. Costa et al., "Multiscale entropy analysis of biological signals," Physical Review E, vol. 71, no. 2 Pt 1, p. 021906, Feb 2005, doi: 10.1103/PhysRevE.71.021906.
[31] M. Zanin et al., "Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review," Entropy, vol. 14, no. 8, pp. 1553-1577, 2012, doi: 10.3390/e14081553.
[32] M. Thilagaraj et al., "Tsallis entropy: as a new single feature with the least computation time for classification of epileptic seizures," Cluster Computing, vol. 22, no. S6, pp. 15213-15221, 2018, doi: 10.1007/s10586-018-2549-5.
[33] N. Arunkumar et al., "Automatic detection of epileptic seizures using new entropy measures," Journal of Medical Imaging and Health Informatics, vol. 6, no. 3, pp. 724-730, 2016.
[34] M. Jia et al., "A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling," IEEE Access, vol. 8, pp. 101153-101162, 2020, doi: 10.1109/access.2020.2998608.
[35] P. D. Prasad et al., "Single-trial EEG classification using logistic regression based on ensemble synchronization," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 1074-80, May 2014, doi: 10.1109/JBHI.2013.2289741.
[36] M. Schmidt et al., "Minimizing finite sums with the stochastic average gradient," Mathematical Programming, vol. 162, no. 1, pp. 83-112, 2017.
[37] H. You et al., "MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network," Knowledge-Based Systems, vol. 231, p. 107456, 2021/11/14/ 2021, doi: 10.1016/j.knosys.2021.107456.
[38] X. Jiang et al., "MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging," in 2021 International Joint Conference on Neural Networks, 18-22 July 2021 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9534133.
[39] I. Loshchilov and F. Hutter, "SGDR: Stochastic gradient descent with warm restarts," arXiv preprint arXiv:1608.03983, 2016.
[40] R. G. Andrzejak et al., "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state," Physical Review E, vol. 64, no. 6 Pt 1, p. 061907, Dec 2001, doi: 10.1103/PhysRevE.64.061907.
[41] X. Tian et al., "Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 10, pp. 1962-1972, 2019, doi: 10.1109/TNSRE.2019.2940485.
[42] A. Bhattacharyya and R. B. Pachori, "A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform," IEEE Transactions on Biomedical Engineering, vol. 64, no. 9, pp. 2003-2015, 2017, doi: 10.1109/TBME.2017.2650259.
[43] Z. Y. Chin et al., "Multiclass voluntary facial expression classification based on Filter Bank Common Spatial Pattern," in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 20-25 Aug. 2008 2008, pp. 1005-1008, doi: 10.1109/IEMBS.2008.4649325.
[44] A. D. Bigirimana et al., "Emotion-Inducing Imagery Versus Motor Imagery for a Brain-Computer Interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, pp. 850-859, 2020, doi: 10.1109/TNSRE.2020.2978951.
[45] A. Kai Keng et al., "Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface," in 2008 IEEE International Joint Conference on Neural Networks 1-8 June 2008 2008, pp. 2390-2397, doi: 10.1109/IJCNN.2008.4634130.
[46] S. H. Park et al., "Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 498-505, 2018, doi: 10.1109/TNSRE.2017.2757519.
[47] C. Li et al., "Seizure Onset Detection Using Empirical Mode Decomposition and Common Spatial Pattern," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 458-467, 2021, doi: 10.1109/TNSRE.2021.3055276.
[48] T. N. Alotaiby et al., "Seizure detection with common spatial pattern and Support Vector Machines," in 2015 International Conference on Information and Communication Technology Research, 17-19 May 2015 2015, pp. 152-155, doi: 10.1109/ICTRC.2015.7156444.
[49] V. J. Lawhern et al., "EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces," Journal of Neural Engineering, vol. 15, no. 5, p. 056013, 2018.
[50] J. M. Johnson and T. M. Khoshgoftaar, "Survey on deep learning with class imbalance," Journal of Big Data, vol. 6, no. 1, p. 27, 2019/03/19 2019, doi: 10.1186/s40537-019-0192-5.
[51] L. Van der Maaten and G. Hinton, "Visualizing data using t-SNE," Journal of machine learning research, vol. 9, no. 11, 2008.
[52] A. Gupta et al., "A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 5, pp. 925-935, May 2018, doi: 10.1109/TNSRE.2018.2818123.
[53] S. Mamli and H. Kalbkhani, "Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection," Biocybernetics and Biomedical Engineering, vol. 39, no. 1, pp. 87-99, 2019, doi: 10.1016/j.bbe.2018.10.006.
[54] W. Zhao et al., "A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals," Computational and Mathematical Methods in Medicine, vol. 2020, p. 9689821, 2020, doi: 10.1155/2020/9689821.
[55] Y. Jiang et al., "Symplectic geometry decomposition-based features for automatic epileptic seizure detection," Computers in Biology and Medicine, vol. 116, p. 103549, Jan 2020, doi: 10.1016/j.compbiomed.2019.103549.
[56] X. Yan et al., "Significant Low-Dimensional Spectral-Temporal Features for Seizure Detection," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 668-677, 2022, doi: 10.1109/TNSRE.2022.3156931.
[57] S. Raghu et al., "Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier," Computers in Biology and Medicine, vol. 110, pp. 127-143, Jul 2019, doi: 10.1016/j.compbiomed.2019.05.016.
[58] I. L. Olokodana et al., "Ordinary-kriging based real-time seizure detection in an edge computing paradigm," in 2020 IEEE International Conference on Consumer Electronics, 2020: IEEE, pp. 1-6.
[59] M. Zeng et al., "Detecting Seizures From EEG Signals Using the Entropy of Visibility Heights of Hierarchical Neighbors," IEEE Access, vol. 7, pp. 7889-7896, 2019, doi: 10.1109/access.2019.2890895.
[60] Y. Qiu et al., "Denoising Sparse Autoencoder-Based Ictal EEG Classification," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 9, pp. 1717-1726, 2018, doi: 10.1109/TNSRE.2018.2864306.
[61] A. S. Tarawneh et al., "Stop Oversampling for Class Imbalance Learning: A Review," IEEE Access, vol. 10, pp. 47643-47660, 2022, doi: 10.1109/ACCESS.2022.3169512.
[62] Y. Ganin et al., "Domain-adversarial training of neural networks," The journal of machine learning research, vol. 17, no. 1, pp. 2096-2030, 2016.
[63] S. Qiu et al., "LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection," IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 1845-1856, 2023, doi: 10.1109/JBHI.2022.3223970.
[64] A. Gholami et al., "A survey of quantization methods for efficient neural network inference," arXiv preprint arXiv:2103.13630, 2021.
[65] G. Hinton et al., "Distilling the knowledge in a neural network," arXiv preprint arXiv:1503.02531, 2015.

指導教授

徐國鎧(Kuo-Kai Shyu)

審核日期

2023-7-18

推文