Electromyography (EMG) signal compression using sinusoidal segmental model

Muscle signal called electromyography signal have a positive-negative signal generated by MUAP. The number of MUAP is depends on the muscle activity. On the stressed muscle, the characteristics is like periodic signal. Based on its characteristic, we can be modeled this by sinusoidal model. On the sinusoidal model, there are many kinds for representing the signal. One of model is Segmental Sinusoidal model. EMG signal can be represented as a combination of sinusoidal signal which was generated by muscle system with infinite combination of amplitude, frequency and phase. On quantization based on peak to peak, EMG signal was detected its peaks, both of positive and negative. Then time distance between peak to peak would be quantized. In this paper, we proposed a new method to quantize and reconstruct ECG signal which segmented by peak to peak based on sinusoidal model. The part of signal between positive peak and following negative peak or vice versa was estimated as a half period of the sinusoid signal. Magnitude between peaks was the double of the estimated sine amplitude. The information which taken from quantization process were period and gain. The experiment result show that synthesis signal quality was reduced on the high frequency component.