├── .gitattributes ├── ECGDeepLearningWithCWT_cn.mlx ├── ECGWaveletScatteringWithLSTMs_cn.mlx ├── PrepareSignalData_cn.mlx ├── README.md ├── SECURITY.md ├── license.txt └── training.mp4 /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /ECGDeepLearningWithCWT_cn.mlx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mathworks/deep-learning-for-time-series-data/9305eb6828f7a53437a0a03e83171c62910da417/ECGDeepLearningWithCWT_cn.mlx -------------------------------------------------------------------------------- /ECGWaveletScatteringWithLSTMs_cn.mlx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mathworks/deep-learning-for-time-series-data/9305eb6828f7a53437a0a03e83171c62910da417/ECGWaveletScatteringWithLSTMs_cn.mlx -------------------------------------------------------------------------------- /PrepareSignalData_cn.mlx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mathworks/deep-learning-for-time-series-data/9305eb6828f7a53437a0a03e83171c62910da417/PrepareSignalData_cn.mlx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # deep-learning-for-time-series-data 2 | The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used data set can be download on：https://github.com/mathworks/physionet_ECG_data/ 3 | 4 | # Folder structure: 5 | 6 | ECGDeepLearningWithCWT_cn.mlx: ECG Classification with Continuous Wavelet Transform; 7 | 8 | ECGWaveletScatteringWithLSTMs_cn.mlx: ECG Classification using Wavelet Scattering and LSTMs; 9 | 10 | PrepareSignalData_cn.mlx: Prepare Signal Data for ECG Classification with Continuous Wavelet Transform; 11 | 12 | training.mp4: the video shows the training process of ECG Classification with Continuous Wavelet Transform. 13 | 14 | # The video series (in Chinese) on this topic can be found as follows: 15 | https://www.mathworks.com/videos/series/deep-learning-for-time-series-data.html 16 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | SECURITY.md · master · Ned Gulley / Contribution Template · GitLab 21 | 22 | 23 | 24 | 25 | 26 | 27 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 78 | 79 | 80 |
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751 | 752 | 753 | 773 | 774 | 775 | 776 | 777 | -------------------------------------------------------------------------------- /license.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2020, The MathWorks, Inc. 2 | All rights reserved. 3 | Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 4 | 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 5 | 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 6 | 3. In all cases, the software is, and all modifications and derivatives of the software shall be, licensed to you solely for use in conjunction with MathWorks products and service offerings. 7 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 8 | 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /training.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mathworks/deep-learning-for-time-series-data/9305eb6828f7a53437a0a03e83171c62910da417/training.mp4 --------------------------------------------------------------------------------