ABSTRACT
The rapid development of wearable device technology provides an efficient way of data acquisition for remote ECG monitoring and identification. However, existing iteration based signal recovery methods have high latency, while the deep learning based method have a shortcoming that large increase in parameters makes training more difficult as the signal length increases. In this paper, we combine compressed sensing and generative adversarial networks to propose a signal recovery method based on dilated convolution. The proposed model can accept more prior information from compressed long signal without increasing parameters and achieve feature domain self-adaptation by fitting the distribution of reconstructed and original signals. Experiments result on MIT-BIH and PTB datasets demonstrate that the proposed method achieves comparable or better results in reconstruction accuracy and reconstruction time when compared to some existing iteration-based methods and some deep learning based methods. For example, reconstructing a 2s signal takes only 0.013s, which is a 50% improvement over other deep learning methods.
- Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML’17). JMLR.org, Sydney, NSW, Australia, 214–223.Google Scholar
- Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G. Dimakis. 2017. Compressed Sensing Using Generative Models. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML’17). JMLR.org, Sydney, NSW, Australia, 537–546.Google Scholar
- Emmanuel J. Candes and Michael B. Wakin. 2008. An Introduction To Compressive Sampling. IEEE Signal Processing Magazine 25, 2 (2008), 21–30. https://doi.org/10.1109/MSP.2007.914731Google ScholarCross Ref
- Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS’14). MIT Press, Cambridge, MA, USA, 2672–2680.Google ScholarDigital Library
- Khalid Hasan, Kamanashis Biswas, Khandakar Ahmed, Nazmus S. Nafi, and Md Saiful Islam. 2019. A comprehensive review of wireless body area network. Journal of Network and Computer Applications 143 (2019), 178–198. https://doi.org/10.1016/j.jnca.2019.06.016Google ScholarDigital Library
- K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 770–778. https://doi.org/10.1109/CVPR.2016.90Google Scholar
- Zehao Huang, Lingfeng Wang, Gaofeng Meng, and Chunhong Pan. 2017. Image super-resolution via deep dilated convolutional networks. In 2017 IEEE International Conference on Image Processing (ICIP). 953–957. https://doi.org/10.1109/ICIP.2017.8296422Google ScholarDigital Library
- Maya Kabkab, Pouya Samangouei, and Rama Chellappa. 2018. Task-aware Compressed Sensing with Generative Adversarial Networks. In AAAI Conference on Artificial Intelligence.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations,ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980Google Scholar
- H.J. Landau.1967. Sampling, data transmission, and the Nyquist rate. Proc. IEEE 55, 10 (1967), 1701–1706. https://doi.org/10.1109/PROC.1967.5962Google ScholarCross Ref
- Benyuan Liu, Zhilin Zhang, Gary Xu, Hongqi Fan, and Qiang Fu. 2014. Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation. Biomedical Signal Processing and Control 11 (2014), 80–88.Google ScholarCross Ref
- G.B. Moody and R.G. Mark. 2001. The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine 20, 3 (2001), 45–50. https://doi.org/10.1109/51.932724Google ScholarCross Ref
- Ali Mousavi, Ankit B. Patel, and Richard G. Baraniuk. 2015. A deep learning approach to structured signal recovery. 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) (Sep 2015). https://doi.org/10.1109/allerton.2015.7447163Google ScholarDigital Library
- Priya Ranjan Muduli, Rakesh Reddy Gunukula, and Anirban Mukherjee. 2016. A deep learning approach to fetal-ECG signal reconstruction. In 2016 Twenty Second National Conference on Communication (NCC). 1–6. https://doi.org/10.1109/NCC.2016.7561206Google ScholarCross Ref
- Haydar Ozkan, Orhan Ozhan, Yasemin Karadana, Muhammed Gulcu, Samet Macit, and Fasahath Husain. 2020. A Portable Wearable Tele-ECG Monitoring System. IEEE Transactions on Instrumentation and Measurement 69, 1 (2020), 173–182. https://doi.org/10.1109/TIM.2019.2895484Google ScholarCross Ref
- Yubao Sun, Jiwei Chen, Qingshan Liu, and Guangcan Liu. 2020. Learning image compressed sensing with sub-pixel convolutional generative adversarial network. Pattern Recognition 98 (2020), 107051. https://doi.org/10.1016/j.patcog.2019.107051Google ScholarDigital Library
- Fenghua Tong, Lixiang Li, Haipeng Peng, and Yixian Yang. 2020. An effective algorithm for the spark of sparse binary measurement matrices. Appl.Math. Comput. 371 (2020), 124965. https://doi.org/10.1016/j.amc.2019.124965Google ScholarDigital Library
- Joel A. Tropp and Anna C. Gilbert. 2007. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit. IEEE Transactions on Information Theory 53, 12 (2007), 4655–4666. https://doi.org/10.1109/TIT.2007.909108Google ScholarDigital Library
- P Wagner, N Strodthoff, and R Bousseljot. 2020. PTB-XL, a large publicly available electrocardiography dataset. Sci Data 7 (2020), 154.Google ScholarCross Ref
- Jun Zhang, Zhenghui Gu, Zhu Liang Yu, and Yuanqing Li. 2015. Energy-Efficient ECG Compression on Wireless Biosensors via Minimal Coherence Sensing and Weighted Minimization Reconstruction. IEEE Journal of Biomedical and Health Informatics 19, 2 (2015), 520–528. https://doi.org/10. 1109/JBHI.2014.2312374Google ScholarCross Ref
- Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, and Bhaskar D. Rao. 2013. Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning. IEEE Transactions on Biomedical Engineering 60, 2 (Feb 2013), 300–309. https://doi.org/10.1109/tbme.2012.2226175Google ScholarCross Ref
- Zhilin Zhang and Bhaskar D. Rao. 2013. Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation. IEEE Transactions on Signal Processing 61, 8 (Apr 2013), 2009–2015. https://doi.org/10.1109/tsp.2013.2241055Google ScholarDigital Library
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