TSFE DL: : A python library for time series spatio-temporal feature extraction and prediction using deep learning
Authors: 
Ignacio Aguilera-Martos, Ángel M. García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco HerreraAuthors Info & Claims
References
[1]
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016.
[2]
J.H. Tan, Y. Hagiwara, W. Pang, I. Lim, S.L. Oh, M. Adam, R.S. Tan, M. Chen, U.R. Acharya, Application of stacked convolutional and long short-term memory network for accurate identification of cad ecg signals, Comput. Biol. Med. 94 (2018) 19–26.
[3]
H. Lu, Z. Ge, Y. Song, D. Jiang, T. Zhou, J. Qin, A temporal-aware lstm enhanced by loss-switch mechanism for traffic flow forecasting, Neurocomputing 427 (2021) 169–178.
[4]
M.O. Alassafi, M. Jarrah, R. Alotaibi, Time series predicting of covid-19 based on deep learning, Neurocomputing 468 (2022) 335–344.
[5]
P. Zhang, Y. Hang, X. Ye, P. Guan, J. Jiang, J. Tan, W. Hu, A united cnn-lstm algorithm combining rr wave signals to detect arrhythmia in the 5g-enabled medical internet of things, IEEE Internet Things J. 9 (16) (2022) 14563–14571.
[6]
S.L. Oh, E.Y.K. Ng, R. San Tan, U.R. Acharya, Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Comput. Biol. Med. 102 (2018) 278–287.
[7]
J. Gao, H. Zhang, P. Lu, Z. Wang, An effective LSTM recurrent network to detect arrhythmia on imbalanced ECG dataset, J. Healthcare Eng. 2019 (2019) 6320651.
[8]
C. Chen, Z. Hua, R. Zhang, G. Liu, W. Wen, Automated arrhythmia classification based on a combination network of CNN and LSTM, Biomed. Signal Process. Control 57 (2020).
[9]
X. Wei, L. Zhou, Z. Zhang, Z. Chen, Y. Zhou, Early prediction of epileptic seizures using a long-term recurrent convolutional network, J. Neurosci. Methods 327 (2019).
[10]
M. Sajjad, Z.A. Khan, A. Ullah, T. Hussain, W. Ullah, M.Y. Lee, S.W. Baik, A novel CNN-GRU-based hybrid approach for short-term residential load forecasting, IEEE Access 8 (2020) 143759–143768.
[11]
T.-Y. Kim, S.-B. Cho, Predicting residential energy consumption using CNN-LSTM neural networks, Energy 182 (2019) 72–81.
[12]
Z. Kong, Y. Cui, Z. Xia, H. Lv, Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics, Appl. Sci. 9 (19) (2019) 4156.
[13]
F. Chollet, Others, Keras, https://keras.io (2015).
[14]
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, Pytorch: An imperative style, high-performance deep learning library, Advances in Neural Information Processing Systems, 32, 2019, pp. 8024–8035.
[15]
W. Cai, Y. Chen, J. Guo, B. Han, Y. Shi, L. Ji, J. Wang, G. Zhang, J. Luo, Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network, Comput. Biol. Med. 116 (2020).
[16]
J. Fu, C. Sun, Z. Yu, L. Liu, A hybrid CNN-LSTM model based actuator fault diagnosis for six-rotor UAVs, 2019 Chinese Control And Decision Conference (CCDC), IEEE (2019) 410–414.
[17]
M. Geng, W. Zhou, G. Liu, C. Li, Y. Zhang, Epileptic seizure detection based on stockwell transform and bidirectional long short-term memory, IEEE Trans. Neural Syst. Rehabil. Eng. 28 (3) (2020) 573–580.
[18]
J.H. Tan, Y. Hagiwara, W. Pang, I. Lim, S.L. Oh, M. Adam, R.S. Tan, M. Chen, U.R. Acharya, Application of stacked convolutional and long short-term memory network for accurate identification of cad ecg signals, Comput. Biol. Med. 94 (2018) 19–26.
[19]
H.M. Lynn, S.B. Pan, P. Kim, A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks, IEEE Access 7 (2019) 145395–145405.
[20]
M.-L. Huang, Y.-S. Wu, Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network, Biomed. Eng. Lett. 10.
[21]
O.S. Lih, V. Jahmunah, T.R. San, E.J. Ciaccio, T. Yamakawa, M. Tanabe, M. Kobayashi, O. Faust, U.R. Acharya, Comprehensive electrocardiographic diagnosis based on deep learning, Artif. Intell. Med. 103 (2020).
[22]
H. Shi, C. Qin, D. Xiao, L. Zhao, C. Liu, Automated heartbeat classification based on deep neural network with multiple input layers, Knowl.-Based Syst. 188 (2020).
[23]
K. Wang, X. Qi, H. Liu, Photovoltaic power forecasting based LSTM-Convolutional Network, Energy 189 (2019).
[24]
Q. Yao, R. Wang, X. Fan, J. Liu, Y. Li, Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network, Inf. Fusion 53 (2020) 174–182.
[25]
Y. Gao, H. Wang, Z. Liu, An end-to-end atrial fibrillation detection by a novel residual-based temporal attention convolutional neural network with exponential nonlinearity loss, Knowl.-Based Syst. 212 (2021).
[26]
O. Yildirim, U.B. Baloglu, R.-S. Tan, E.J. Ciaccio, U.R. Acharya, A new approach for arrhythmia classification using deep coded features and LSTM networks, Comput. Methods Programs Biomed. 176 (2019) 121–133.
[27]
J. Zhang, A. Liu, M. Gao, X. Chen, X. Zhang, X. Chen, ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network, Artif. Intell. Med. 106 (2020).
[28]
Z. Zheng, Z. Chen, F. Hu, J. Zhu, Q. Tang, Y. Liang, An automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology, Electronics 9 (1) (2020) 121.
[29]
G. Sharma, A. Parashar, A.M. Joshi, Dephnn: A novel hybrid neural network for electroencephalogram (eeg)-based screening of depression, Biomed. Signal Process. Control 66 (2021).
[30]
J. Dai, X. Xi, G. Li, T. Wang, Eeg-based emotion classification using improved cross-connected convolutional neural network, Brain Sci. 12(8).
[31]
International Federation of Digital Seismograph Networks, Spanish Digital Seismic Network. Dataset/Seismic Network.
[32]
G.B. Moody, R.G. Mark, The impact of the MIT-BIH arrhythmia database, IEEE Eng. Med. Biol. Mag. 20 (3) (2001) 45–50.
[33]
S. Hettich, S.D. Bay, The uci kdd archive, Irvine, CA: University of California, Department of Information and Computer Science. URL: http://kdd.ics.uci.edu.
Index Terms
- TSFE DL: A python library for time series spatio-temporal feature extraction and prediction using deep learning
Index terms have been assigned to the content through auto-classification.
Recommendations
Ore Image Classification Based on Improved CNN
AbstractThe identification of ore deposits is an important technical task in mining and excavation. However, conventional techniques are time-consuming and tedious. Therefore, data augmentation and transfer learning were used in this topic to ...
Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Networks
Proceedings of the 23rd International Conference on Artificial Neural Networks and Machine Learning ICANN 2013 - Volume 8131Artificial neural networks have proved to be good at time-series forecasting problems, being widely studied at literature. Traditionally, shallow architectures were used due to convergence problems when dealing with deep models. Recent research findings ...
Composition of Feature Selection for Time-Series Prediction with Deep Learning
AbstractThe concept of predictions has gained much attention over the last few years. Research on prediction based on experience is error-prone. Usually, a lot of data has been available with multiple variables, but not all features are relevant. This ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
The Authors.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 14 January 2023
Author Tags
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Feb 2025