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Am. J. Biomed. Sci. 2016, 8(1), 1-23; doi: 10.5099/aj160100001
Received: 27 December 2015; | Revised: 7 January 2016; | Accepted: 12 January 2016


Advanced Two-State Compressing Algorithm: A Versatile, Reliable and Low-Cost Computational Method for ECG Wireless Applications


Duong Trong Luong*, Nguyen Minh Duc, Nguyen Tuan Linh, Nguyen Thai Ha, Nguyen Duc Thuan

Department of Electronic Technology and Biomedical Engineering, Hanoi University of Science and Technology, Vietnam.

*Corresponding Author

Duong Trong Luong

Department of Electronic Technology and Biomedical Engineering

Hanoi University of Science and Technology


Email: luong.duongtrong@hust.edu.vn



Compressing the ECG signal is considered a feasible solution for supporting a system to manipulate the package size, a major factor leading to congestion in an ECG wireless network. Hence, this paper proposes a compression algorithm, called the advanced two-state algorithm, which achieves three necessary characteristics: a) flexibility towards all ECG signal conditions, b) the ability to adapt to each requirement of the package size and c) be simple enough. In this algorithm, the ECG pattern is divided into two categories: "complex" durations such as QRS complexes, are labeled as low-state durations, and "plain" durations such P or T waves, are labeled as high-state durations. Each duration type can be compressed at different compression ratios, and Piecewise Cubic Spline can be used for reconstructing the signal. For evaluation, the algorithm was applied to 48 records of the MIT-BIH arrhythmia database (clear PQRST complexes) and 9 records of the CU ventricular tachyarrhythmia database (unclear PQRST complexes). Parameters including Compression Ratio (CR), Percentage Root mean square Difference (PRD), Percentage Root mean square Difference, Normalized (PRDN), root mean square (RMS), Signal-to-noise Ratio (SNR) and a new proposed index called Peak Maximum Absolute Error (PMAE) were used to comprehensively evaluate the performance of the algorithm. Eventually, the results obtained were positive with low PRD, PRDN and PMAE at different compression ratios compared to many other loss-type compressing methods, proving the high efficiency of the proposed algorithm. All in all, with its extremely low-cost computation, versatility and good-quality reconstruction, this algorithm could be applied to a number of wireless applications to control package size and overcome congested situations.

Keywords: ECG compression, Telemedicine, ECG pattern classification, adaptive package size.

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