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2

Robert Bamler. Understanding entropy coding with asymmetric numeral systems (ans): a statistician's perspective. arXiv preprint arXiv:2201.01741, 2022.

3

Richard G Baraniuk. Compressive sensing [lecture notes]. Signal Processing Magazine, IEEE, 24(4):118–121, 2007.

4

Leonardo Vidal Batista, Elmar Uwe Kurt Melcher, and Luis Carlos Carvalho. Compression of ecg signals by optimized quantization of discrete cosine transform coefficients. Medical engineering & physics, 23(2):127–134, 2001.

5

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Emmanuel J Candes and Terence Tao. Near-optimal signal recovery from random projections: universal encoding strategies? Information Theory, IEEE Transactions on, 52(12):5406–5425, 2006.

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Emmanuel J Candès. Compressive sampling. In Proceedings of the International Congress of Mathematicians: Madrid, August 22-30, 2006: invited lectures, 1433–1452. 2006.

8

Emmanuel J Candès and Michael B Wakin. An introduction to compressive sampling. Signal Processing Magazine, IEEE, 25(2):21–30, 2008.

9

Huasong Cao, Victor Leung, Cupid Chow, and Henry Chan. Enabling technologies for wireless body area networks: a survey and outlook. IEEE Communications Magazine, 47(12):84–93, 2009.

10

Sid Ahmed Chouakri, O Djaafri, and Abdelmalik Taleb-Ahmed. Wavelet transform and huffman coding based electrocardiogram compression algorithm: application to telecardiology. In Journal of Physics: Conference Series, volume 454, 012086. IOP Publishing, 2013.

11

Darren Craven, Brian McGinley, Liam Kilmartin, Martin Glavin, and Edward Jones. Compressed sensing for bioelectric signals: a review. IEEE journal of biomedical and health informatics, 19(2):529–540, 2014.

12

Adrianus Djohan, Truong Q Nguyen, and Willis J Tompkins. Ecg compression using discrete symmetric wavelet transform. In Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society, volume 1, 167–168. IEEE, 1995.

13

David L Donoho. Compressed sensing. Information Theory, IEEE Transactions on, 52(4):1289–1306, 2006.

14

Jarek Duda. Asymmetric numeral systems: entropy coding combining speed of huffman coding with compression rate of arithmetic coding. arXiv preprint arXiv:1311.2540, 2013.

15

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16

Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation, 101(23):e215–e220, 2000.

17

Byung S Kim, Sun Kook Yoo, and Moon-Hyoung Lee. Wavelet-based low-delay ecg compression algorithm for continuous ecg transmission. IEEE Transactions on Information Technology in Biomedicine, 10(1):77–83, 2006.

18

Shailesh Kumar. Cr-sparse: hardware accelerated functional algorithms for sparse signal processing in python using jax. Journal of Open Source Software, 6(68):3917, 2021.

19

Subramanyam Shashi Kumar and Prakash Ramachandran. Review on compressive sensing algorithms for ecg signal for iot based deep learning framework. Applied Sciences, 12(16):8368, 2022.

20

Zhitao Lu, Dong Youn Kim, and William A Pearlman. Wavelet compression of ecg signals by the set partitioning in hierarchical trees algorithm. IEEE transactions on Biomedical Engineering, 47(7):849–856, 2000.

21

Kan Luo, Jianqing Li, and Jianfeng Wu. A dynamic compression scheme for energy-efficient real-time wireless electrocardiogram biosensors. IEEE Transactions on Instrumentation and Measurement, 63(9):2160–2169, 2014.

22

Hossein Mamaghanian, Nadia Khaled, David Atienza, and Pierre Vandergheynst. Compressed sensing for real-time energy-efficient ecg compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering, 58(9):2456–2466, 2011.

23

Mauro Mangia, Luciano Prono, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti. Deep neural oracles for short-window optimized compressed sensing of biosignals. IEEE transactions on biomedical circuits and systems, 14(3):545–557, 2020.

24

Aleksandar Milenković, Chris Otto, and Emil Jovanov. Wireless sensor networks for personal health monitoring: issues and an implementation. Computer communications, 29(13-14):2521–2533, 2006.

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George B Moody and Roger G Mark. The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):45–50, 2001.

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Francesco Picariello, Grazia Iadarola, Eulalia Balestrieri, Ioan Tudosa, and Luca De Vito. A novel compressive sampling method for ecg wearable measurement systems. Measurement, 167:108259, 2021.

27

Luisa F Polania, Rafael E Carrillo, Manuel Blanco-Velasco, and Kenneth E Barner. Compressed sensing based method for ecg compression. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), 761–764. IEEE, 2011.

28

Luisa F Polanía and Rafael I Plaza. Compressed sensing ecg using restricted boltzmann machines. Biomedical Signal Processing and Control, 45:237–245, 2018.

29

Mohammad Pooyan, Ali Taheri, Morteza Moazami-Goudarzi, and Iman Saboori. Wavelet compression of ecg signals using spiht algorithm. International Journal of signal processing, 1(3):4, 2004.

30

Supriya O Rajankar and Sanjay N Talbar. An electrocardiogram signal compression techniques: a comprehensive review. Analog Integrated Circuits and Signal Processing, 98(1):59–74, 2019.

31

Butta Singh, Amandeep Kaur, and Jugraj Singh. A review of ecg data compression techniques. International journal of computer applications, 2015.

32

Hongpo Zhang, Zhongren Dong, Zhen Wang, Lili Guo, and Zongmin Wang. Csnet: a deep learning approach for ecg compressed sensing. Biomedical Signal Processing and Control, 70:103065, 2021.

33

Jun Zhang, Zhenghui Gu, Zhu Liang Yu, and Yuanqing Li. Energy-efficient ecg compression on wireless biosensors via minimal coherence sensing and weighted l1 minimization reconstruction. IEEE Journal of Biomedical and Health Informatics, 19(2):520–528, 2014.

34

Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Zhouyue Pi, and Bhaskar D Rao. Spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals. IEEE transactions on neural systems and rehabilitation engineering, 22(6):1186–1197, 2014.

35

Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, and Bhaskar D Rao. Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ecg via block sparse bayesian learning. IEEE Transactions on Biomedical Engineering, 60(2):300–309, 2012.

36

Zhilin Zhang and Bhaskar D Rao. Extension of sbl algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Transactions on Signal Processing, 61(8):2009–2015, 2013.

37

Zhimin Zhang, Shoushui Wei, Dingwen Wei, Liping Li, Feng Liu, and Chengyu Liu. Comparison of four recovery algorithms used in compressed sensing for ecg signal processing. In 2016 Computing in Cardiology Conference (CinC), 401–404. IEEE, 2016.

38

Yaniv Zigel, Arnon Cohen, and Amos Katz. The weighted diagnostic distortion (wdd) measure for ecg signal compression. IEEE transactions on biomedical engineering, 47(11):1422–1430, 2000.

39

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