{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:29:57Z","timestamp":1783002597189,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61861021"],"award-info":[{"award-number":["61861021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61863027"],"award-info":[{"award-number":["61863027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GJJ190194"],"award-info":[{"award-number":["GJJ190194"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GJJ200426"],"award-info":[{"award-number":["GJJ200426"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Project of the Education Department of Jiangxi Province","award":["61861021"],"award-info":[{"award-number":["61861021"]}]},{"name":"Science and Technology Project of the Education Department of Jiangxi Province","award":["61863027"],"award-info":[{"award-number":["61863027"]}]},{"name":"Science and Technology Project of the Education Department of Jiangxi Province","award":["GJJ190194"],"award-info":[{"award-number":["GJJ190194"]}]},{"name":"Science and Technology Project of the Education Department of Jiangxi Province","award":["GJJ200426"],"award-info":[{"award-number":["GJJ200426"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the data burden and energy costs. Therefore, compressed sensing (CS) which can conduct under-sampling and reconstruction at the same time is adopted in the ECG monitoring application. Recently, deep learning used in CS methods improves the reconstruction performance significantly and can removes of some of the constraints in traditional CS. In this paper, we propose a deep compressive-sensing scheme for ECG signals, based on modified-Inception block and long short-term memory (LSTM). The framework is comprised of four modules: preprocessing; compression; initial; and final reconstruction. We adaptively compressed the normalized ECG signals, sequentially using three convolutional layers, and reconstructed the signals with a modified Inception block and LSTM. We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the highest at all of the sensing rates in our experiments on both of the databases, and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing significant improvement in reconstruction performance compared to the comparative methods. Our method also showed good recovering quality in the noisy data.<\/jats:p>","DOI":"10.3390\/e24081024","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T21:20:59Z","timestamp":1658784059000},"page":"1024","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM"],"prefix":"10.3390","volume":"24","author":[{"given":"Jing","family":"Hua","sequence":"first","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jue","family":"Rao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingqiong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jizhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanchang Key Laboratory of Medical and Technology Research, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1850088","DOI":"10.1142\/S0218126618500883","article-title":"Direct Arrhythmia Classification from Compressive ECG Signals in Wearable Health Monitoring System","volume":"27","author":"Hua","year":"2018","journal-title":"J. Circuit. Syst. Comp."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hua, J., Tang, J., Liu, J., Yang, F., and Zhu, W. (2019, January 14\u201317). A Novel ECG Heartbeat Classification Approach Based on Compressive Sensing in Wearable Health Monitoring System. Proceedings of the 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00115"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mishra, A., Thakkar, F., Modi, C., and Kher, R. (September, January 28). ECG Signal Compression Using Compressive Sensing and Wavelet Transform. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346696"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1002\/mrm.21477","article-title":"Compressed Sensing in Dynamic MRI","volume":"59","author":"Gamper","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/LSP.2014.2364225","article-title":"Sparse Phase Retrieval from Short-Time Fourier Measurements","volume":"22","author":"Eldar","year":"2015","journal-title":"IEEE Signal. Proc. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Donoho, D.L., and Huo, X. (2000, January 30). Beamlet pyramids: A New Form of Multiresolution Analysis Suited for Extracting Lines, Curves, and Objects from Very Noisy Image Data. Proceedings of the Spie the International Symposium on Optical Science and Technology, San Diego, CA, USA.","DOI":"10.1117\/12.408630"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TIP.2002.1014998","article-title":"The Curvelet Transform for Image Denoising","volume":"11","author":"Starck","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","unstructured":"Do, M.N., and Vetterli, M. (2002, January 3\u20136). Contourlets: A New Directional Multiresolution Image Representation. Proceedings of the Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_9","first-page":"97","article-title":"Matching Pursuit: Adaptive Representations of Images and Sounds","volume":"15","author":"Bergeaud","year":"1996","journal-title":"Comput. Appl. Math."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal. Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, C., Pan, P., and Ding, Q. (2022). Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing. Entropy, 24.","DOI":"10.3390\/e24020273"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1109\/TIT.2011.2181819","article-title":"LDPC Codes for Compressed Sensing","volume":"58","author":"Dimakis","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1109\/LSP.2010.2052243","article-title":"Compressive Sensing with Chaotic Sequence","volume":"17","author":"Yu","year":"2010","journal-title":"IEEE Signal. Proc. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TBME.2012.2226175","article-title":"Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning","volume":"60","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s13534-020-00148-7","article-title":"A Compressed-sensing-based Compressor for ECG","volume":"10","author":"Izadi","year":"2020","journal-title":"Biomed. Eng. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.bspc.2018.08.011","article-title":"Biorthogonal Wavelet Filters for Compressed Sensing ECG Reconstruction","volume":"47","author":"Abhishek","year":"2019","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"37228","DOI":"10.1109\/ACCESS.2019.2905000","article-title":"Electrocardiogram Reconstruction Based on Compressed Sensing","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102786","DOI":"10.1016\/j.bspc.2021.102786","article-title":"ECG Compression Using Wavelet-based Compressed Sensing with Prior Support Information","volume":"68","author":"Melek","year":"2021","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102047","DOI":"10.1016\/j.bspc.2020.102047","article-title":"Compressive Sensing Based the Multi-channel ECG Reconstruction in Wireless Body Sensor Networks","volume":"61","author":"Jahanshahi","year":"2020","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109803","DOI":"10.1016\/j.measurement.2021.109803","article-title":"ECG Compressed Sensing Method with High Compression Ratio and Dynamic Model Reconstruction","volume":"183","author":"Michaeli","year":"2021","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.bspc.2017.11.015","article-title":"ECG Signal Compression and Denoising via Optimum Sparsity Order Selection in Compressed Sensing Framework","volume":"41","author":"Rezaii","year":"2018","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1650103","DOI":"10.1142\/S0218126616501036","article-title":"Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System","volume":"25","author":"Hua","year":"2016","journal-title":"J. Circuits Syst. Comp."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Muduli, P.R., Gunukula, R.R., and Mukherjee, A. (2016, January 4\u20136). A Deep Learning Approach to Fetal-ECG Signal Reconstruction. Proceedings of the 2016 Twenty Second National Conference on Communication (NCC), Guwahati, India.","DOI":"10.1109\/NCC.2016.7561206"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.bspc.2018.05.022","article-title":"Compressed Sensing ECG Using Restricted Boltzmann Machines","volume":"45","author":"Plaza","year":"2018","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_25","first-page":"545","article-title":"Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals","volume":"14","author":"Mangia","year":"2020","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCE.2019.2959059","article-title":"A Brain\u2013Computer Interface Framework Based on Compressive Sensing and Deep Learning","volume":"9","author":"Shrivastwa","year":"2020","journal-title":"IEEE Consum. Electr. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mangi, M., Marchioni, A., Prono, L., Pareschi, F., Rovatti, R., and Setti, G. (September, January 31). Low-power ECG Acquisition by Compressed Sensing with Deep Neural Oracles. Proceedings of the 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genova, Italy.","DOI":"10.1109\/AICAS48895.2020.9073945"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shrivastwa, R.R., Pudi, V., and Chattopadhyay, A. (2018, January 8\u201311). An FPGA-Based Brain Computer Interfacing Using Compressive Sensing and Machine Learning. Proceedings of the 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Hong Kong, China.","DOI":"10.1109\/ISVLSI.2018.00137"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/978-3-030-58545-7_14","article-title":"Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing","volume":"Volume 12354","author":"Vedaldi","year":"2020","journal-title":"Computer Vision-ECCV 2020"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The Impact of the MIT-BIH Arrhythmia Database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_34","unstructured":"Khosravy, M., Dey, N., and Duque, C.A. (2020). Chapter 9\u2014Compressive Sensing of Electrocardiogram. Advances in Ubiquitous Sensing Applications for Healthcare, Compressive Sensing in Healthcare, Elsevier."},{"key":"ref_35","first-page":"407","article-title":"Adaptive Measurement Network for CS Image Reconstruction","volume":"Volume 772","author":"Yang","year":"2017","journal-title":"Computer Vision. CCCV 2017. Communications in Computer and Information Science"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/TSP.2013.2241055","article-title":"Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation","volume":"61","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Signal. Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal Recovery from Random Measurements via Orthogonal Matching Pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1109\/TIT.2009.2016006","article-title":"Subspace Pursuit for Compressive Sensing Signal Reconstruction","volume":"55","author":"Dai","year":"2009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103065","DOI":"10.1016\/j.bspc.2021.103065","article-title":"CSNet: A Deep Learning Approach for ECG Compressed Sensing","volume":"70","author":"Zhang","year":"2021","journal-title":"Biomed. Signal. Process. Control."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Al-Marridi, A.Z., Mohamed, A., and Erbad, A. (2018, January 25\u201329). Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems. Proceedings of the 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC.2018.8450511"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1002\/pd.5412","article-title":"Noninvasive Fetal Electrocardiography for the Detection of Fetal Arrhythmias","volume":"39","author":"Behar","year":"2019","journal-title":"Prenatal. Diag."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1024\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:15Z","timestamp":1760140575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["e24081024"],"URL":"https:\/\/doi.org\/10.3390\/e24081024","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,25]]}}}