Signal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite where it is important that the original and the decompressed data should be identical or where deviations from the original data would lead to catastrophic consequences, especially in biomedical signal analysis and diagnostics. For which, the input signal preprocessed with differential pulse code modulation (DPCM) reduces the interchannel dependencies to get the desired output. A whole array of unique compression techniques are being utilized in the compression process. The combination of (K‐means clustering, arithmetic encoding [AE], Huffman encoding [HE]) clustering and coding compression techniques are analyzed using electro cardiogram (ECG) and electroencephalogram (EEG) signals. The proposed method employs k‐means clustering combined with Huffman encoding (DiKHE) and k‐means clustering combined with arithmetic encoding (DiKAE) individually. Compression ratio (CR) is analyzed with these combinations of compression techniques for various cluster size K (K = 2,3,4,5,6). A maximum CR of 6.03144 and 4.54126 is obtained for ECG and EEG signals respectively. The compressions based on these techniques are efficient since the compressed signal is reconstructed perfectly as it matches exactly with the original signal.

中文翻译：

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基于差分聚类和编码技术的生物医学ECG和EEG信号的混合压缩

结合了数据压缩过程的信号处理技术丰富了信号并提高了存储效率和传输可靠性。传输未压缩的原始数据会消耗大量带宽，这会增加传输时间并导致数据锤击。这些限制迫使人们寻找战略性数据压缩技术。在重要的是原始数据和解压缩后的数据应该相同或与原始数据的偏差会导致灾难性后果的情况下（尤其是在生物医学信号分析和诊断中），无损压缩技术是必需的。为此，使用差分脉冲编码调制（DPCM）进行预处理的输入信号可减少通道间依赖性，从而获得所需的输出。压缩过程中正在使用一整套独特的压缩技术。使用心电图（ECG）和脑电图（EEG）信号分析（K均值聚类，算术编码[AE]，霍夫曼编码[HE]）聚类和编码压缩技术的组合。所提出的方法分别采用结合了霍夫曼编码（DiKHE）的k-means聚类和结合算术编码（DiKAE）的k-means聚类。通过压缩技术的这些组合对各种簇大小K（所提出的方法分别采用结合了霍夫曼编码（DiKHE）的k-means聚类和结合算术编码（DiKAE）的k-means聚类。通过压缩技术的这些组合对各种簇大小K（所提出的方法分别采用结合了霍夫曼编码（DiKHE）的k-means聚类和结合算术编码（DiKAE）的k-means聚类。通过压缩技术的这些组合对各种簇大小K（K = 2,3,4,5,6）。分别为ECG和EEG信号获得的最大CR为6.03144和4.54126。由于这些压缩信号与原始信号完全匹配，因此可以完美地重建压缩后的信号，因此基于这些技术的压缩非常有效。