Comparison of vector quantization methods for medical fidelity preserving lossy compression of EMG signals

In this study, different vector quantization methods are used to compress EMG signals. EMG is a versatile biosignal used in many fields of medical research and its compression is necessary for long-term recordings and narrow-band wireless transmission. The quality of lossy compression-decompression process of a biosignal has to be evaluated with the fidelity of essential medical parameters in mind. In this study, the analysis is focused on the preservation of primarily used spectral variables, namely mean frequency and median frequency. Modified versions of the basic vector quantization, classified vector quantization with two codebooks, classified vector quantization with three codebooks, classified vector quantization with four codebooks, gain-shape vector quantization with unquantized gain value, gain-shape vector quantization with eight gain values, and gain-shape vector quantization with four gain values have been implemented and tested with the segment length of 8 samples and the codebook sizes of 32 codevectors and 64 codevectors. The gain values, used both for criteria of codebook selection in classified vector quantization, and as product code in gain-shape vector quantization, was calculated as a sum of absolute segment values. The gain-shape method with eight quantized gain values shows best results in terms of the compression ratio. The general results reveal systematic bias up to 17.34 Hz in the mean frequency value, but a linear model for correcting the error is presented