Linear predictive coding of myoelectric signals

Despite the great interest towards long term recordings of electromyographic (EMG) signals, which find applications, for example, in telemedicine, only a few studies have dealt with the compression of these signals. We propose a lossy coding technique for surface EMG signals. The technique is based on the linear predictive coding paradigm widely used for speech compression. The algorithm was tested on both simulated and experimental signals. Mean frequency, median frequency, variance, skewness and kurtosis of the EMG signals were preserved with an error less than 3% with respect to the original values for synthetic signals and experimental signals, reducing the bitrate from 24 kbit/s (12 kbit/s after downsampling) to 352 bit/s, with a compression factor of 97.1%. It was concluded that the linear predictive coding paradigm can be effectively used for high rate compression of surface EMG signals when preservation of only the power spectrum of the signal is of interest. This has applications in ergonomics and occupational medicine.