Exploring Multiple Algorithms for Lossless Electrocardiogram Compression

As a solution to the problem of large amounts of ECG data in resource-constrained settings, this paper discusses effective lossless compression techniques. Through the integration of algorithmic optimizations and predictive coding approaches, it has achieved significant reductions in signal size without sacrificing critical information. Huffman Encoding is the most effective technique among the various techniques examined. It yields compression ratios between 0.26 and 0.45 and Quality Scores between 0.037 and 0.081. These outcomes demonstrate how well our method works to preserve signal integrity while drastically lowering transmission and storage costs. This paper tries to significantly improve healthcare outcomes by tackling these issues, especially in situations when resource optimization is essential.