Abstract
Electrocardiogram (ECG) is used to record electrical activity associated with the functioning of the heart. These signals are highly data intensive and have higher resolution; thus, ECG signals require large space for storage in database and more transmission bandwidth. The ECG signals contain information signal with some redundancies; by removing these redundancies, better ECG signal compression can be achieved. The ECG compression algorithm should have high compression ratio (CR), low percent root-mean-square difference (PRD), low reconstruction error, and less computational complexity. DCT/FFT methods use frequency transformation and parameter extraction techniques. In dynamic compression scheme, IF sampler and lossless encoder are used. Both methods require preprocessing of the ECG signal. In the proposed method, the preprocessing of ECG signal is not required; signal compression is based on two threshold values, and the noise is eliminated. Using these techniques, better CR, PRD, and less storage space are achieved.
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References
Mahajan R, Bansal D (2014) Hybrid ECG signal compression system: a step towards efficient tele-cardiology. In: IEEE 2014 international conference on reliability, optimization and information technology (ICROIT)
Luo K, Li J, Wu J (2014) A dynamic compression scheme for energy-efficient real-time wireless electrocardiogram biosensors. IEEE Trans Instrum Meas 63(9)
Negoita M, Goras L (2008) On a compression algorithm for ECG signals. IEEE Trans Biomed Eng
Mukhopadhyay SK, Mitra M, Mitra S (2011) An ECG data compression method via R-peak detection and ASCII character encoding. In: International conference on computer, communication and electrical technology—ICCCET
Qureshi KA, Patel VP (2013) Compression of ECG signal using discrete wavelet transform. In: Proceedings of SARC-IRAJ international conference. ISBN: 978-93-82702-21-4
Qu X-C, Zhang Y (2014) A compression algorithm for ECG data using variable length classified template sets. In: International symposium on computer, consumer and control
Wadhwani S, Wadhwani AK, Tripathi E (1982) ECG data compression using enhanced modified AZTEC technique. IEEE Transaction on BME, 35(3):43–8
Akhter S, Haque MA ECG compression using run length encoding. In: 18th European signal processing conference (EUSIPCO-2010) Aalborg, Denmark, 23–27 Aug 2010
Siao S-R, Hsu C-C, Po-Hung Lin M, A Novel Approach for ECG Data Compression in Healthcare Monitoring System. In: 2014 IEEE international symposium on bioelectronics and bioinformatics (IEEE ISBB 2014)
Pathak A, Wadhwani AK (2012) Data compression of ECG signals using error back propagation (EBP) algorithm. Int J Eng Adv Technol (IJEAT) 1(4). ISSN: 2249-8958
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Mahesha, Y., Lavanya, B.L. (2020). Adaptive Bi-threshold Algorithm for ECG Compression Based on Signal Slope. In: Kalya, S., Kulkarni, M., Shivaprakasha, K. (eds) Advances in Communication, Signal Processing, VLSI, and Embedded Systems. Lecture Notes in Electrical Engineering, vol 614. Springer, Singapore. https://doi.org/10.1007/978-981-15-0626-0_19
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DOI: https://doi.org/10.1007/978-981-15-0626-0_19
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