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High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms.

机译:高性能数据压缩方法,具有模式匹配的生物医学ECG和动脉脉搏波形。

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摘要

Biomedical waveforms, such as electrocardiogram (ECG) and arterial pulse, always possess a lot of important clinical information in medicine and are usually recorded in a long period of time in the application of telemedicine. Due to the huge amount of data, to compress the biomedical waveform data is vital. By recognizing the strong similarity and correlation between successive beat patterns in biomedical waveform sequences, an efficient data compression scheme mainly based on pattern matching is introduced in this paper. The waveform codec consists mainly of four units: beat segmentation, beat normalization, two-stage pattern matching and template updating and residual beat coding. Three different residual beat coding methods, such as Huffman/run-length coding, Huffman/run-length coding in discrete cosine transform domain, and vector quantization, are employed. The simulation results show that our compression algorithms achieve a very significant improvement in the performances of compression ratio and error measurement for both ECG and pulse, as compared with some other compression methods.
机译:生物医学波形,例如心电图(ECG)和动脉搏动,在医学中始终拥有许多重要的临床信息,并且在远程医学应用中通常会长时间记录。由于数据量巨大,因此压缩生物医学波形数据至关重要。通过识别生物医学波形序列中连续搏动模式之间的强相似性和相关性,提出了一种主要基于模式匹配的有效数据压缩方案。波形编解码器主要由四个单元组成:拍子分割,拍子归一化,两阶段模式匹配以及模板更新和残留拍子编码。采用了三种不同的残差拍编码方法,例如霍夫曼/游程编码,离散余弦变换域中的霍夫曼/游程编码和矢量量化。仿真结果表明,与其他一些压缩方法相比,我们的压缩算法在ECG和脉冲的压缩率和误差测量性能方面都有了非常显着的提高。

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