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.
Highlights
According to the World Health Organization (WHO), annual mortality from cardiovascular disease is expected to increase from 17.5 million in 2012 to 22.2 million in 2030, and annual cancer deaths are expected to climb from 8.2 million to 12.6 million during the same period [1]
When the compression ratio (CR) was lower, the signal-to-noise ratio (SNR) values obtained using the proposed algorithm were higher than those obtained using compressed sensing (CS) and orthogonal matching pursuit (OMP)
When the high-beating waveforms are located in non-QRS region, the compressed algorithm utilizing the CS algorithm and SNR gives poorer results when using the CS-compressed algorithm in a high- beating waveform in the proposed architecture
Summary
According to the World Health Organization (WHO), annual mortality from cardiovascular disease is expected to increase from 17.5 million in 2012 to 22.2 million in 2030, and annual cancer deaths are expected to climb from 8.2 million to 12.6 million during the same period [1]. In order to prevent cardiovascular disease in advance, personal heart monitors have been developed to safeguard a wide variety of human activities. The wireless body sensor network (WBSN) is a special class of wireless sensor network (WSN) comprising various types of miniature biosensors, which are worn or implanted for the continuous monitoring of biomedical signals, such as those from an electrocardiogram (ECG). These devices require an algorithm for the compression and subsequent storage of data. The signal-to-noise ratio (SNR) and compression ratio (CR) are important parameters when dealing with these types of algorithms. Some hardware design issues like power consumption, hardware cost, and recovery performance are crucial to the effectiveness of portable devices, and researchers have proposed algorithms to deal with these issues
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