赵地;卜刚;

• 1:中国科学院计算技术研究所
• 2:南京航空航天大学

摘要(Abstract)：

目前,神经电刺激领域发展较为迅速,主要应用领域是对脑部核团进行电刺激,用于治疗或缓解患者相关病症,主要调整参数方式为主观调节,医生根据患者情况进行参数调节,让患者感受相关刺激后决定是否采用该参数,存在调整不精准、不适应、不及时等情况。随着市场需求增加,通信技术发展,远程医疗逐渐能够解决程控调整的及时性问题,但更本质的解决问题的技术为闭环程控技术。

关键词(KeyWords)： 一维卷积神经网络;低功耗芯片;脑机接口;闭环刺激;信号采集与处理

Abstract:

Keywords:

基金项目(Foundation):

作者(Authors): 赵地;卜刚;

DOI: 10.16453/j.cnki.ISSN2096-5036.2021.06.003

参考文献(References)：

• [1]陈弘扬,高敬阳*,赵地*,等.深度学习与生物医学图像分析2020年综述[J].中国图象图形学报,2021,26(3):475-486.
• [2]吕鸿蒙,赵地*,迟学斌.基于增强Alex Net的深度学习的阿尔茨海默病的早期诊断[J].计算机科学,2017,44(S1):50-60.
• [3]张巧丽,迟学斌,赵地.基于深度学习的帕金森病症早期诊断[J].计算机系统应用,2018,27(9):1-9.
• [4]Yang Yang,Lin-Feng Yan,Xin Zhang,et al.Glioma Grading on Conventional MR Images:A Deep Learning Study With Transfer Learning[J]Frontiers in Neuroscience,2018.
• [5]Jiahui Zhang,Xiong Han*,Di Zhao,et al.SVM-based personalized prediction model for seizure-free epilepsy with levetiracetam therapy[J].British Journal of Pharmacology,2018.
• [6]Gunnar Gudnason,Erik Bruun,Morten Hauglan.An implantable mixed analog/digital neural stimulator circuit[C]//1999 IEEEInternational Symposium on Circuits and Systems (ISCAS).IEEE,1999.
• [7]Andy Zhou,Samantha R.Santacruz,Benjamin C.Johnson,et al.A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates[J].Nature Biomedical Engineering,2019,3:15-26.
• [8]Y.Wang,Q.Sun,H.Luo,et al.A Closed-Loop Neuromodulation Chipset with 2-Level Classification Achieving 1.5Vpp CMInterference Tolerance,35d B Stimulation Artifact Rejection in 0.5ms and 97.8%Sensitivity Seizure Detection[C]//2020 IEEEInternational Solid-State Circuits Conference-(ISSCC).IEEE,2020.
• [9]Hemmings Wu,Hartwin Ghekiere,Dorien Beeckmans,et al.Conceptualization and validation of an open-source closed-loop deep brain stimulation system in rat[J].Scientific Reports,2015 4(1):9921.
• [10]Hyo-Gyuem Rhew,Jaehun Jeong,Jeffrey A.Fredenburg,et al.A Wirelessly Powered Log-based Closed-loop Deep Brain Stimulation So C with Two-way Wireless Telemetry for Treatment of Neurological Disorders[C]//2012 Symposium on VLSICircuits (VLSIC).IEEE,2012.
• [11]Lei Cai,Jingyang Gao*,Di Zhao*.A review of the application of deep learning in medical image classification and segmentation[J].Annals of Translational Medicine,2020,8(11).
• [12]张巧丽,赵地*,迟学斌.基于深度学习的医学影像诊断综述[J].计算机科学,2017,44(S2):1-7.
• [13]Di Zhao*.Fast Filter Bank Convolution for Three-dimensional Wavelet Transform by Shared Memory on Mobile GPUComputing[J].Journal of Supercomputing,2015,71(9):3440-3455.
• [14]Tiechui Yao,Li Xiao,Di Zhao,et al.GPU Computing based fast discrete wavelet transform for l1-regularized SPIRi Treconstruction[J].The Imaging Science Journal,2018,66(7-8):393-408..
• [15]Morgan Stuart,Chathurika S.Wickramasinghe,Daniel L.Marino,et al.Machine Learning for Deep Brain Stimulation Efficacy using Dense Array EEG[C]//2019 12th International Conference on Human System Interaction (HSI).IEEE,2019.
• [16]Wan KR,Maszczyk T,See AAQ,et al.A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson's disease[J].Clinical Neurophysiology,2019,130(1):145-154.
• [17]Sabato Santaniello,John T.Gale,Sridevi V.Sarma.Systems approaches to optimizing deep brain stimulation therapies in Parkinson’s disease[J].Wiley Interdiscip Rev Syst Biol Med,2018:e1421.
• [18]Feng XJ,Greenwald B,Rabitz H,et al.Toward closed-loop optimization of deep brain stimulation for Parkinson's disease:concepts and lessons from a computational model[J].Journal of Neural Engineering,2007,4(2):L14-21.
• [19]Marco Wiering,Martijn van Otterlo.强化学习[M].赵地,刘莹,邓仰东,欧阳建权,苏统华,译.北京:机械工业出版社,2018.
• [20]Poomipat Boonyakitanont,Apiwat Lek-uthai,Krisnachai Chomtho,et al.A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals[J].bio Rxiv,2019.
• [21]?zal Y?ld?r?m,Ulas Baran Baloglu,U.Rajendra Acharya.A deep convolutional neural network model for automated identification of abnormal EEG signals[J].Neural Computing and Applications,2020,32:15857-15868.
• [22]Rajamanickam Yuvaraj,John Thomas,Tilmann Kluge,et al.A deep Learning Scheme for Automatic Seizure Detection from Long-Term Scalp EEG[C]//2018 52nd Asilomar Conference on Signals,Systems,and Computers,2018.
• [23]Jiuwen Zhang,Haobo Wu,Wei Su,et al.A new approach for classification of epilepsy eeg signals based on temporal convolutional neural networks[C]//2018 International Symposium on Computational Intelligence and Design(ISCID),2018.
• [24]Xinghua Yao,Qiang Cheng,Guo-Qiang Zhang.A Novel Independent RNN Approach to Classification of Seizures against Nonseizures[J].ar Xiv:1903.09326v1[cs.LG].
• [25]Ihsan Ullah,Muhammad Hussain,Emad-ul-Haq Qazi,et al.An automated system for epilepsy detection using EEG brain signals based on deep learning approach[J].Expert Systems with Applications,2018,107:61-71.
• [26]Zuochen Wei,Junzhong Zou,Jian Zhang,et al.Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain[J].Biomedical Signal Processing and Control,2019,53:101551.
• [27]Hisham G.Daoud,Ahmed M.Abdelhameed,Magdy Bayoumi.Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network[C]//2018 IEEE 14th International Colloquium on Signal Processing&Its Applications (CSPA).IEEE,2018.
• [28]Xuhui Chen,Jinlong Ji,Tianxi Ji,et al.Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection[C]//ACMConference on Bioinformatics,Computational Biology,and Health Informatics 2018(ACM-BCB).Washington,DC:ACM,2018.
• [29]U.Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara.Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals[J].Computers in Biology and Medicine,2018:270-278.
• [30]Subhrajit Roy,Isabell Kiral-Kornek,Stefan Harrer.Deep Learning Enabled Automatic Abnormal EEG Identification[C]//2018Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE,2018.
• [31]Christian Meisel,Rima El Atrache,Michele Jackson,et al.Deep learning from wristband sensor data:towards wearable,non-invasive seizure forecasting[J].ar Xiv:1906.00511v2[q-bio.NC].
• [32]Maurice Abou Jaoude,Jin Jing,Haoqi Sun,et al.Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning[J]Clinical Neurophysiology,2020,131(1):133-141.
• [33]John Thomas,Luca Comoretto,Jing Jin.EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection[C]//2018 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE,2018.
• [34]Alexander Rosenberg Johansen,Jing Jin,Tomasz Maszczyk,et al.Epileptiform spike detection via convolutional neural networks[C]//2016 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2016.
• [35]Kosuke Fukumori,Hoang Thien Thu Nguyen,Noboru Yoshida,et al.Fully Data-driven Convolutional Filters with Deep Learning Models for Epileptic Spike Detection[C]//2019 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2019.
• [36]Jeff Craley,Emily Johnson,Archana Venkataraman.Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG[C]//Information Processing in Medical Imaging(IPMI 2019).Springer,2019.
• [37]Alison O’Shea,Gordon Lightbody,Geraldine Boylan,et al.Neonatal seizure detection using convolutional neural networks[C]//2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).IEEE,2017.
• [38]Diyuan Lu,Jochen Triesch.Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy[J].ar Xiv:1903.08100v1[cs.LG].
• [39]Paschalis Bizopoulos,George I.Lambrou,Dimitrios Koutsouris.Signal2Image Modules in Deep Neural Networks for EEGClassification[C]//2019 41st Annual International Conference of the[21]IEEE Engineering in Medicine and Biology Society(EMBC).IEEE,2019.