A novel RLE &amp; LZW for bit-stream compression

Li, Tianjiao; Zhao, Tiandong; Nho, Minwoo; Zhou, Xiaofang

doi:10.1109/icsict.2016.7998815

Cited by 1 publication

(1 citation statement)

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“…Lossless compression techniques ensure perfect recovery of the original data from the compressed data, thereby preserving the quality of all the signals. Notable examples of lossless compression include run-length encoding (RLE) [6], Human coding [7], and Lempel-Ziv-Welch (LZW) compression [8], among others [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Electrocardiography Data Compression

Luanloet,

Suwansantisuk,

Kumhom

2023

ECTI-CIT Transactions

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In healthcare, electrocardiography (ECG) sensors generate a large amount of heart electrical signal that must be efficiently compressed to enable fast data transfer and reduce storage costs. Existing methods for ECG data compression do not fully exploit the characteristics of ECG signals, leading to suboptimal compression. This study proposes a data compression technique for ECG data by exploiting the known characteristics of ECG signals. Our approach combines Savitzky-Golay filtering, detrending, discrete cosine transform, scalar quantization, run-length encoding, and Huffman coding for the effective compression. To optimize the compression performance, we generated quantization intervals tailored to the ECG data characteristics. The proposed method experimentally produces a high compression ratio of 127.61 for a design parameter K = 8, a minimum percentage root mean square difference of 1.03% for K = 128, and a maximum quality score (QS) of 39.78, where K is the number of quantization intervals. Moreover, we compared the proposed method to state-of-the-art methods on a widely used ECG benchmark dataset. We found that the proposed method outperforms the others in terms of the QS, which measures the overall compression-decompression ability. By enabling more storage and faster data transfer, the proposed method can facilitate the widespread use and analysis of large volumes of ECG data, thereby contributing to advances in healthcare.

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