1. |
Mcleod G, Shum K, Gupta T, et al. Echocardiography in congenital heart disease. Prog Cardiovasc Dis, 2018, 61(5-6): 468-475.
|
2. |
Aquaro G D, Habtemicael Y G, Camastra G, et al. Prognostic value of repeating cardiac magnetic resonance in patients with acute myocarditis. J Am Coll Cardiol, 2019, 74(20): 2439-2448.
|
3. |
Rosmini S, Treibel T A, Bandula S, et al. Cardiac computed tomography for the detection of cardiac amlyoidosis. J Cardiovasc Comput, 2017, 11: 155-156.
|
4. |
Patidar S, Pachori R B. Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform. Biomed Signal Proces, 2013, 8: 559-567.
|
5. |
Cheng X, Ma Y, Liu C, et al. Research on heart sound identification technology. Sci China Inf Sci, 2012, 55(2): 281-292.
|
6. |
Lim K H, Shin Y D, Park S H, et al. Correlation of blood pressure and the ratio of S1 to S2 as measured by esophageal stethoscope and wireless bluetooth transmission. Pak J Med Sci, 2013, 29(4): 1023-1027.
|
7. |
成谢锋, 马勇, 刘陈, 等. 心音身份识别技术的研究. 中国科学: 信息科学, 2012, 42(2): 237-251.
|
8. |
侯雷静, 郭婷婷, 孙燕, 等. 面向心音分割的个性化高斯混合建模方法. 声学学报, 2019, 44(1): 20-27.
|
9. |
Papadaniil C D, Hadjileontiadis L J. Efficient heart sound segmentation and Extraction using ensemble empirical mode decomposition and kurtosis features. IEEE J Biomed Health, 2014, 18(4): 1138-1152.
|
10. |
谢稳, 姚泽阳, 邱海龙, 等. 人工智能在先天性心脏病学中的应用. 中国胸心血管外科临床杂志, 2020, 27(3): 343-353.
|
11. |
Moukadem A, Dieterlen A, Hueber N, et al. A robust heart sounds segmentation module based on S-transform. Biomed Signal Proces, 2013, 8: 273-281.
|
12. |
Kumar D, Carvalho P, Antunes M, et al. Wavelet transform and simplicity based heart murmur segmentation. Comput Cardiol, 2006, 33: 173-176.
|
13. |
周润景. 模式识别与人工智能(基于MATLAB). 北京: 清华大学出版社, 2018.
|
14. |
侯雷静. 心音信号的周期分析与状态分割. 北京: 中国科学院大学硕士学位论文, 2018.
|
15. |
Chen T E, Yang S I, Ho L T, et al. S1 and S2 heart sound recognition using deep neural networks. IEEE T Bio-Med Eng, 2017, 64(2): 372-380.
|
16. |
Tsao Y, Lin T H, Chen F, et al. Robust S1 and S2 heart sound recognition based on spectral restoration and multi-style training. Biomed Signal Proces, 2019, 49: 173-180.
|
17. |
Cui K, Wu W, Huang J, et al. DOA estimation of LFM signals based on STFT and multiple invariance ESPRIT. AEU-Int J Electron C, 2017, 77: 10-17.
|
18. |
Liu K, Ma P, An J, et al. Endpoint detection of distributed fiber sensing systems based on STFT algorithm. Opt Laser Technol, 2019, 114: 122-126.
|
19. |
Scanlan A G. Low power & mobile hardware accelerators for deep convolutional neural network. Integration, 2019, 65: 110-127.
|
20. |
Feng X, Qin B, Liu T. A language-independent neural network for event detection. Sci China Inf Sci, 2018, 61: 092106.
|
21. |
Shaughnessy D O. Recognition and processing of speech signals using neural networks. Circ Syst Signal Process, 2019, 38: 3454-3481.
|
22. |
Brahimi S, Aoun N B, Amar C B. Boosted convolutional neural network for object recognition at large scale. Neurocomputing, 2019, 33: 337-354.
|
23. |
李端, 张洪欣, 刘知青, 等. 基于深度残差卷积神经网络的心电信号心律不齐识别. 生物医学工程学杂志, 2019, 36(2): 189-198.
|
24. |
Acharya U R, Oh S L, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med, 2017, 89: 389-396.
|
25. |
左东奇, 韩霖, 陈科, 等. 基于卷积神经网络提取超声图像甲状腺结点钙化点的研究. 生物医学工程学杂志, 2018, 35(5): 679-687.
|
26. |
朱莉, 张丽英, 韩云涛, 等. 基于卷积神经网络的注意缺陷多动障碍分类研究. 生物医学工程学杂志, 2017, 34(1): 99-105.
|
27. |
Qiao H, Lu C Y. Signal-background discrimination with convolutionan neural network in the PandaX-III experiment using MC simulation. Sci China Phys Mech Astron, 2018, 61(10): 101007.
|
28. |
Zhao Z, Zhang Y, Deng Y, et al. ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation. Comput Biol Med, 2018, 102: 168-179.
|
29. |
He D, Xu K, Wang D. Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels. Image Vision Comput, 2019, 89: 12-20.
|
30. |
Wang S, Chen H. A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl Energ, 2019, 235: 1126-1140.
|
31. |
陈诗慧, 刘维湘, 秦璟, 等. 基于深度学习和医学图像的癌症计算机辅助诊断研究进展. 生物医学工程学杂志, 2017, 34(2): 314-319.
|
32. |
Mitra V, Sivaraman G, Nam H, et al. Hybrid convolutional neural networks for articulatory and acoustic information based speech recognition. Speech Commun, 2017, 89: 103-112.
|
33. |
Lu S, Lu Z, Zhang Y D. Pathological brain detection based on AlexNet and transfer learning. J Comput Sci-Neth, 2019, 30: 41-47.
|
34. |
Teerakawanich N, Leelaruji T, Pichetjamroen A. Short term prediction of sun coverage using optical flow with GoogLeNet. Energy Rep, 2020, 6: 526-531.
|
35. |
Wei J, Ibrahim Y, Qian S, et al. Analyzing the impact of soft errors in VGG networks implemented on GPUs. Microelectr Reliabil, 2020, 110: 113648.
|
36. |
Kawaguchi K, Bengio Y. Depth with nonlinearity creates no bad local minima in ResNets. Neur Netw, 2019, 118: 167-174.
|
37. |
Kocak Y, Siray G U. New activation functions for signal layer feedforward neural network. Expert Syst Appl, 2021, 164: 113977.
|
38. |
Jiang J, Zhang Z, Dong Q, et al. Characterization and identification of asphalt mixtures based on Convolutional Neural Network methods using X-ray scanning images. Constr Build Mater, 2018, 174: 72-80.
|
39. |
Cao J, Pang Y, Li X, et al. Randomly translational activation inspired by the input distributions of ReLU. Neurocomputing, 2018, 275: 859-868.
|
40. |
Wu H, Zhao J. Deep convolutional neural network model based chemical process fault diagnosis. Comput Chem Eng, 2018, 115: 185-197.
|
41. |
Zhang Z, Jaiswal P, Rai R. FeatureNet: Machining feature recognition based on 3D Convolution Neural Network. Comput Aided Design, 2018, 101: 12-22.
|
42. |
Goldberger A L, Amaral L A N, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 2003, 101(23): e215-e220.
|
43. |
谭志向, 张懿, 曾德平, 等. 基于希尔伯特-黄变换的心音包络提取在LabVIEW上的实现. 生物医学工程学杂志, 2015, 32(2): 263-268.
|
44. |
卢誉声. 移动平台深度神经网络实战: 原理、架构与优化. 北京: 机械工业出版社, 2019.
|