Hybrid compression of biomedical ECG and EEG signals based on differential clustering and encoding techniques

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.