Advanced Two-State Compressing Algorithm: A Versatile, Reliable and Low-Cost Computational Method for ECG Wireless Applications
Duong Trong Luong1, Nguyen Minh Duc2, Nguyen Tuan Linh3, Nguyen Thai Ha4, Nguyen Duc Thuan5
1PHD. Candidate Duong Trong Luong, Department of Electronics Technology and Biomedical Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
2Msc. Nguyen Minh Duc, Department of Electronics Technology and Biomedical Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
3B.Eng. Nguyen Tuan Linh, Department of Electronics Technology and Biomedical Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
4Prof. Nguyen Duc Thuan, Department of Electronics Technology and
Biomedical Electronics Engineering, Hanoi University of Science and
Technology, Hanoi, Vietnam.
5Dr. Nguyen Thai Ha, Department of Electronics Technology and Biomedical Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
Manuscript received on December 16, 2016 . | Revised Manuscript received on December 29, 2016 . | Manuscript published on January 05, 2016 . | PP: 56-70 | Volume-5 Issue-6, January 2016 . | Retrieval Number: F2787015616/2016©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: 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 labelled as low-state durations, and “plain” durations such P or T waves, are labelled 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.