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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2010 - 4

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On ECG Compressed Sensing using Specific Overcomplete Dictionaries

FIRA, M. See more information about FIRA, M. on SCOPUS See more information about FIRA, M. on IEEExplore See more information about FIRA, M. on Web of Science, GORAS, L. See more information about  GORAS, L. on SCOPUS See more information about  GORAS, L. on SCOPUS See more information about GORAS, L. on Web of Science, BARABASA, C. See more information about  BARABASA, C. on SCOPUS See more information about  BARABASA, C. on SCOPUS See more information about BARABASA, C. on Web of Science, CLEJU, N. See more information about CLEJU, N. on SCOPUS See more information about CLEJU, N. on SCOPUS See more information about CLEJU, N. on Web of Science
 
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Download PDF pdficon (1,859 KB) | Citation | Downloads: 2,278 | Views: 6,993

Author keywords
compressed sensing, biomedical signal processing, electrocardiography, pursuit algorithms, signal processing algorithms

References keywords
signal(12), wavelet(6), sensing(6), processing(6), biomed(6), tbme(5), signals(5), sampling(5), classification(5), science(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-11-30
Volume 10, Issue 4, Year 2010, On page(s): 23 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.04004
Web of Science Accession Number: 000284782700004
SCOPUS ID: 78649711600

Abstract
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The paper presents a number of results regarding the construction of specific overcomplete dictionaries for ECG compressed sensing (CS). The dictionaries were built using normal and patological cardiac patterns extracted from 24 recordings of the MIT-BIH Arrhythmia Database. It has been shown that the compression results obtained using the CS concept based on specific dictionaries are better that those using the wavelet overcomplete dictionaries. Starting from the concept of sparse signal with respect to a given overcomplete dictionary the paper present several results regarding the possibility of simple pattern classification as well.


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Cited-By Clarivate Web of Science

Web of Science® Times Cited: 27 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 32
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Cited-By CrossRef

[1] On ECG reconstruction using weighted‐compressive sensing, Zonoobi, Dornoosh, Kassim, Ashraf A., Healthcare Technology Letters, ISSN 2053-3713, Issue 2, Volume 1, 2014.
Digital Object Identifier: 10.1049/htl.2013.0038
[CrossRef]

[2] Development of a Medical Care Terminal for Efficient Monitoring of Bedridden Subjects, Pereira, Filipe, Carvalho, Vítor, Soares, Filomena, Machado, José, Bezerra, Karolina, Silva, Rui, Matos, Demétrio, Journal of Engineering, ISSN 2314-4904, Issue , 2016.
Digital Object Identifier: 10.1155/2016/3591059
[CrossRef]

[3] A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification, Fira, Monica, Costin, Hariton-Nicolae, Goraș, Liviu, Biosensors, ISSN 2079-6374, Issue 3, Volume 12, 2022.
Digital Object Identifier: 10.3390/bios12030146
[CrossRef]

[4] Automatic screening method for atrial fibrillation based on lossy compression of the electrocardiogram signal, Zhang, Hongpo, Dong, Zhongren, Gao, Junli, Lu, Peng, Wang, Zongmin, Physiological Measurement, ISSN 1361-6579, Issue 7, Volume 41, 2020.
Digital Object Identifier: 10.1088/1361-6579/ab979f
[CrossRef]

[5] Combined Sparsifying Transforms for Compressive Image Fusion, WU, C., WANG, H., XU, X., ZHAO, L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 13, 2013.
Digital Object Identifier: 10.4316/AECE.2013.04014
[CrossRef] [Full text]

[6] TP-CNN: A Detection Method for atrial fibrillation based on transposed projection signals with compressed sensed ECG, Zhang, Hongpo, Dong, Zhongren, Sun, Mengya, Gu, Hongzhuang, Wang, Zongmin, Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue , 2021.
Digital Object Identifier: 10.1016/j.cmpb.2021.106358
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[7] Application of Compressed Sensing Based on Adaptive Dynamic Mode Decomposition in Signal Transmission and Fault Extraction of Bearing Signal, Cai, Zhixin, Dang, Zhang, Wen, Ming, Lv, Yong, Duan, Haochun, Machines, ISSN 2075-1702, Issue 5, Volume 10, 2022.
Digital Object Identifier: 10.3390/machines10050353
[CrossRef]

[8] Compressed Sensing Approach for Physiological Signals: A Review, Lal, Bharat, Gravina, Raffaele, Spagnolo, Fanny, Corsonello, Pasquale, IEEE Sensors Journal, ISSN 1530-437X, Issue 6, Volume 23, 2023.
Digital Object Identifier: 10.1109/JSEN.2023.3243390
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[9] A New Method for EEG Compressive Sensing, FIRA, M., GORAS, L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 12, 2012.
Digital Object Identifier: 10.4316/AECE.2012.04011
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[10] An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals, Luengo, David, Meltzer, David, Trigano, Tom, Applied Sciences, ISSN 2076-3417, Issue 12, Volume 8, 2018.
Digital Object Identifier: 10.3390/app8122569
[CrossRef]

[11] ECG compression retaining the best natural basis k-coefficients via sparse decomposition, Adamo, Alessandro, Grossi, Giuliano, Lanzarotti, Raffaella, Lin, Jianyi, Biomedical Signal Processing and Control, ISSN 1746-8094, Issue , 2015.
Digital Object Identifier: 10.1016/j.bspc.2014.09.002
[CrossRef]

[12] DENOISING AND REMOTE MONITORING OF ECG SIGNAL WITH REAL-TIME EXTENDED KALMAN FILTER IN A WEARABLE SYSTEM, Ozkaraca, Osman, Guler, Inan, Biomedical Engineering: Applications, Basis and Communications, ISSN 1016-2372, Issue 01, Volume 27, 2015.
Digital Object Identifier: 10.4015/S101623721550009X
[CrossRef]

[13] Optimization of Discrete Anamorphic Stretch Transform and Phase Recovery for ECG Signal Compression, Thilagavathy, R., Venkataramani, B., IETE Journal of Research, ISSN 0377-2063, Issue 10, Volume 69, 2023.
Digital Object Identifier: 10.1080/03772063.2021.2012281
[CrossRef]

[14] CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals, Tang, Suigu, Deng, Zicong, Physiological Measurement, ISSN 0967-3334, Issue 7, Volume 44, 2023.
Digital Object Identifier: 10.1088/1361-6579/acdfb5
[CrossRef]

[15] CSNet: A deep learning approach for ECG compressed sensing, Zhang, Hongpo, Dong, Zhongren, Wang, Zhen, Guo, Lili, Wang, Zongmin, Biomedical Signal Processing and Control, ISSN 1746-8094, Issue , 2021.
Digital Object Identifier: 10.1016/j.bspc.2021.103065
[CrossRef]

[16] Gabor frames for classification of paroxysmal and persistent atrial fibrillation episodes, Ortigosa, Nuria, Galbis, Antonio, Fernández, Carmen, Cano, Óscar, Medical Engineering & Physics, ISSN 1350-4533, Issue , 2017.
Digital Object Identifier: 10.1016/j.medengphy.2016.10.013
[CrossRef]

[17] A novel ECG signal compression using wavelet and discrete anamorphic stretch transforms, R, Thilagavathy, B, Venkataramani, Biomedical Signal Processing and Control, ISSN 1746-8094, Issue , 2022.
Digital Object Identifier: 10.1016/j.bspc.2021.102773
[CrossRef]

[18] A 1V 9pA analog front end with compressed sensing for electrocardiogram monitoring, Kuo, Liang-Ting, Hou, Chun-Chih, Wu, Meng-Hsuan, Shu, Yun-Shiang, 2015 IEEE Asian Solid-State Circuits Conference (A-SSCC), ISBN 978-1-4673-7191-9, 2015.
Digital Object Identifier: 10.1109/ASSCC.2015.7387484
[CrossRef]

[19] JSM-2 based ECG compression with statistical support prediction, Yu, Sucheng, Liu, Bin, Qiao, Wei, Zhang, Chi, Chen, Chang Wen, Cai, Jian, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), ISBN 978-1-4673-5801-9, 2013.
Digital Object Identifier: 10.1109/HealthCom.2013.6720670
[CrossRef]

[20] Robust reconstruction of compressively sensed ECG signals, Cleju, Nicolae, Fira, Monica, Barabasa, Constantin, Goras, Liviu, ISSCS 2011 - International Symposium on Signals, Circuits and Systems, ISBN 978-1-61284-944-7, 2011.
Digital Object Identifier: 10.1109/ISSCS.2011.5978770
[CrossRef]

[21] JSM-2 based joint ECG compressed sensing with partially known support establishment, Wei Qiao, , Bin Liu, , Chang Wen Chen,, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), ISBN 978-1-4577-2040-6, 2012.
Digital Object Identifier: 10.1109/HealthCom.2012.6379455
[CrossRef]

[22] Reconstruction of compressed sensed ECG signals using patient specific dictionaries, Fira, Monica, Goras, Liviu, Barabasa, Constantin, International Symposium on Signals, Circuits and Systems ISSCS2013, ISBN 978-1-4673-6143-9, 2013.
Digital Object Identifier: 10.1109/ISSCS.2013.6651246
[CrossRef]

[23] A Clustering Approach to Construct Multi-scale Overcomplete Dictionaries for ECG Modeling, Meltzer, David, Luengo, David, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISBN 978-1-4799-8131-1, 2019.
Digital Object Identifier: 10.1109/ICASSP.2019.8682758
[CrossRef]

[24] A new method for ECG compression evaluation, Fira, Monica, Goras, Liviu, 2015 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-4673-7488-0, 2015.
Digital Object Identifier: 10.1109/ISSCS.2015.7203924
[CrossRef]

[25] On the KNN Classifier, the Type of Distance Used and the Weighting of the Votes, Fira, Monica, Goras, Liviu, 2022 E-Health and Bioengineering Conference (EHB), ISBN 978-1-6654-8557-9, 2022.
Digital Object Identifier: 10.1109/EHB55594.2022.9991398
[CrossRef]

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