A comparison of neural network and traditional signal processing techniques in the classification of EMG signals

The decomposition of an electromyographic (EMG) signal is the separation of the complex signal into its constituent motor units' action potentials (MUAPs). The goal of this work is to achieve real-time interpretation of MUAPs. Traditional approaches are relatively successful but suffer from the drawback of being relatively slow and often requiring human intervention. Our work attempts to overcome these problems in two stages. (1) Use traditional methods where appropriate, but speed up the processing. We are currently developing a multiprocessor array using an Analogue Devices ADSP-21060 SHARC processor. (2) Use AI techniques to reduce the amount of human intervention. Work has started on using neuro-fuzzy methods to resolve MUAPs and thus to classify the waveforms for each train. It is likely that the solution to the decomposition problem will require both AI and signal processing techniques. Because of the non-stationary nature of the EMG signal, the neural net requires the weights to be continually modified to reflect the changes in wave shape. All of the methods described in this paper require a certain amount of pre-processing using digital filters and some used simple compression techniques, which if to be realised in real time, will require fast DSP hardware. The pattern recognition aspect of the decomposition has been realised using both traditional methods and AI, but, because of their generalisation abilities, neural nets appear to be a slightly better option.