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李婷等:基于ECG信号的高精度血糖监测 1223 12-301.http://kns.cnki.net/kcms/detail/51.1705.R.20200722.1640 of diabetes using higher order spectral features extracted from 004.html) heart rate signals.Intell Data Anal,2013,17(2):309 [22]Teng JL,RongZJ,Xu Y,et al.Blood glucose prediction method [27]Porumb M,Griffen C,Hattersley J,et al.Noctumal low glucose based on gru.Comput Appl Softw,2020,37(10):107 detection in healthy elderly from one-lead ECG using (滕建丽,容芷君,许莹,等.基于GU网络的血糖预测方法研究 convolutional denoising autoencoders.Biomed Signal Process 计算机应用与软件,2020,37(10):107) Control,2020,62:102054 [23]Seyd A P T,Joseph P K,Jacob J.Automated diagnosis of diabetes [28]Yannakoulia M,Lykou A,Kastorini C M,et al.Socio-economic using heart rate variability signals./Med Syst,2012,36(3):1935 and lifestyle parameters associated with diet quality of children [24]Acharya U R.Fujita H.Oh S L.et al.Application of deep and adolescents using classification and regression tree analy- convolutional neural network for automated detection of sis:The DIATROFI study.Public Health Nutr,2016,19(2): myocardial infarction using ECG signals.Inf Sci,2017,415-416: 339 190 [29]Yuan C X,Jia D N,Zhou S H.Research and application of [25]Ashiquzzaman A,Tushar A K,Islam M R,et al.Reduction of convolutional neural network in mining area prediction.Chin/ overfitting in diabetes prediction using deep learning neural Eg,2020,42(12):1597 network/lIT Convergence and Security 2017.Singapore,2017:35 (袁传新,贾东宁,周生辉.卷积神经网络在矿区预测中的研究 [26]Swapna G,Acharya R U,Vinithasree S,et al.Automated detection 与应用.工程科学学报,2020,42(12):1597)12-30]. http://kns.cnki.net/kcms/detail/51.1705.R.20200722.1640. 004.html) Teng J L, Rong Z J, Xu Y, et al. Blood glucose prediction method based on gru. Comput Appl Softw, 2020, 37(10): 107 (滕建丽, 容芷君, 许莹, 等. 基于GRU网络的血糖预测方法研究. 计算机应用与软件, 2020, 37(10):107) [22] Seyd A P T, Joseph P K, Jacob J. Automated diagnosis of diabetes using heart rate variability signals. J Med Syst, 2012, 36(3): 1935 [23] Acharya  U  R,  Fujita  H,  Oh  S  L,  et  al.  Application  of  deep convolutional  neural  network  for  automated  detection  of myocardial infarction using ECG signals. Inf Sci, 2017, 415-416: 190 [24] Ashiquzzaman  A,  Tushar  A  K,  Islam  M  R,  et  al.  Reduction  of overfitting  in  diabetes  prediction  using  deep  learning  neural network//IT Convergence and Security 2017. Singapore, 2017: 35 [25] [26] Swapna G, Acharya R U, Vinithasree S, et al. Automated detection of  diabetes  using  higher  order  spectral  features  extracted  from heart rate signals. Intell Data Anal, 2013, 17(2): 309 Porumb  M,  Griffen  C,  Hattersley  J,  et  al.  Nocturnal  low  glucose detection  in  healthy  elderly  from  one-lead  ECG  using convolutional  denoising  autoencoders. Biomed Signal Process Control, 2020, 62: 102054 [27] Yannakoulia  M,  Lykou  A,  Kastorini  C  M,  et  al.  Socio-economic and  lifestyle  parameters  associated  with  diet  quality  of  children and  adolescents  using  classification  and  regression  tree  analy￾sis:  The  DIATROFI  study. Public Health Nutr,  2016,  19(2): 339 [28] Yuan  C  X,  Jia  D  N,  Zhou  S  H.  Research  and  application  of convolutional  neural  network  in  mining  area  prediction. Chin J Eng, 2020, 42(12): 1597 (袁传新, 贾东宁, 周生辉. 卷积神经网络在矿区预测中的研究 与应用. 工程科学学报, 2020, 42(12):1597) [29] 李    婷等: 基于 ECG 信号的高精度血糖监测 · 1223 ·
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