Abstract
During smart long-term monitoring of any biomedical signal in wireless body area networks, wearable sensor nodes generate and transmit a large amount of data, increasing transmission power consumption. In order to reduce data storage and power consumption, a lossless data compression technique for an electrocardiogram signal monitoring system is presented in this letter. For this, a hybrid lossless compression algorithm based on Run-length coding and Golomb–Rice coding is proposed to enhance the bit compressing rate. The lossless encoding scheme is implemented on the MIT-BIH arrhythmia database, achieving a compression ratio of 2.91. A VLSI-based architecture of the data compression algorithm is implemented in 90nm CMOS technology that consumes power of 18.78 <inline-formula><tex-math notation="LaTeX">$\mu \text{W}$</tex-math></inline-formula> at 100 MHz operating frequency and 1.2 V supply voltage, occupying an area of 0.0051 <inline-formula><tex-math notation="LaTeX">$\text{mm}^{2}$</tex-math></inline-formula>.
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