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Sensors 2017, 17(10), 2288; doi:10.3390/s17102288

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

1
Department of Engineering and System Science, National Tsing Hua University, Hsinchu 300, Taiwan
2
Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
3
Department of Radiation Oncology, Chang Gung Memorial Hospital-Linkou, Taoyuan 333, Taiwan
*
Authors to whom correspondence should be addressed.
Received: 25 August 2017 / Revised: 28 September 2017 / Accepted: 4 October 2017 / Published: 9 October 2017
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Abstract

Compressed sensing (CS) is a promising approach to the compression and reconstruction of electrocardiogram (ECG) signals. It has been shown that following reconstruction, most of the changes between the original and reconstructed signals are distributed in the Q, R, and S waves (QRS) region. Furthermore, any increase in the compression ratio tends to increase the magnitude of the change. This paper presents a novel approach integrating the near-precise compressed (NPC) and CS algorithms. The simulation results presented notable improvements in signal-to-noise ratio (SNR) and compression ratio (CR). The efficacy of this approach was verified by fabricating a highly efficient low-cost chip using the Taiwan Semiconductor Manufacturing Company’s (TSMC) 0.18-μm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The proposed core has an operating frequency of 60 MHz and gate counts of 2.69 K. View Full-Text
Keywords: compressed sensing; electrocardiogram; near-precise compressed algorithm; adaptive integrating compressed algorithm; signal-to-noise ratio; compressed ratio compressed sensing; electrocardiogram; near-precise compressed algorithm; adaptive integrating compressed algorithm; signal-to-noise ratio; compressed ratio
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Tseng, Y.-H.; Chen, Y.-H.; Lu, C.-W. Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation. Sensors 2017, 17, 2288.

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