Proposed Title :
FPGA Implementation of Hybrid ECG Compression Method to Improve the Compression Ratio
Improvement of this project :
To design hybrid method of ECG Compression Scheme based on lossless compression algorithm, run length coding and Golomb-Rice coding.
To reduce the data load of transmitted ECG samples and improve CR, a hybrid encoding scheme based on Golomb–Rice and run-length coding is proposed.
Wearable sensor nodes create and transmit a considerable quantity of data while performing intelligent long-term monitoring of any biomedical signal in wireless body area networks. This results in an increase in the amount of transmission power that is required. This study presents a lossless data compression approach for an ECG signal monitoring system. The goal of this technique is to lower the amount of space needed for storing data and the amount of power needed to run the system. The reduction of power consumption, in addition to the reduction of data size via the use of compression methods, is a primary concern in these nodes in order to guarantee a longer battery life. Use of the proposed method of lossless compression, run-length coding, and Golomb–Rice encoding, together with strategies based on prediction, is carried out. In order to minimize latency and lower the amount of storage space required, an adaptive linear predictor has been constructed, and the anticipated difference has been encoded using variable-length encoding. A hybrid encoding system that is based on Golomb–Rice and run-length coding has been suggested as a means of reducing the amount of data burden associated with transmitted ECG samples and improving CR (Compression Ratio). In the end, this work was produced using Verilog HDL, and it was generated using Xilinx Vertex-5 FPGA. Lastly, the characteristics of area, delay, and power were compared in order to do a performance study.
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A VLSI-Based Hybrid ECG Compression Scheme for Wearable Sensor Node
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