Abstract
This paper introduces an advanced approach to the hardware implementation of lossless compression for electrocardiogram (ECG) signals, combining Content Adaptive Golomb Rice (CAGR) coding with Adaptive Trending Prediction (ATP). The CAGR technique dynamically adjusts its parameters based on local signal characteristics, effectively optimizing compression efficiency. Simultaneously, Adaptive Trending Prediction analyzes and predicts signal trends to further minimize redundancy before encoding, enhancing overall compression ratios. The hardware architecture presented in this study is meticulously designed to leverage these techniques, ensuring efficient utilization of resources while maintaining real time performance suitable for medical applications. Design considerations include algorithmic complexity, throughput, and resource utilization metrics tailored to meet the stringent requirements of medical monitoring systems. Experimental evaluations validate the efficacy of the proposed method, demonstrating substantial reductions in data size while preserving clinical fidelity crucial for accurate diagnosis and monitoring. The findings underscore the feasibility and practicality of integrating advanced compression algorithms into dedicated hardware platforms, thereby facilitating more efficient data transmission and storage in medical environments.
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