Analyses Of Emg Signal Processing Method And Emg Assessment Of Occupational Low Back Pain

The electromyographic (EMG) signal is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. It is an exceedingly complicated signal, which is affected by the anatomical and physiological properties of muscles, the control scheme of the peripheral nervous system, as well as the characteristics of the instrumentation used to detect it. In our research, multi-channel surface EMG signals picked up from the para-spinal muscle by an electrode array during specific postures and movements have been suggested and utilized to assess back muscle function of low back pain (LBP) patients and normal controls. Preprocessing methods are investigated to make an EMG signal contain the maximum amount of information from muscle and the minimum amount of contamination from electrical noise. In the noise rejection and filtering stage, an artifacts-removing technique based on Independent Component Analysis (ICA) is emphasized to depress ECG and other contaminations in our multi-channel signals. Unlike the conventional ICA denoising techniques, a subtle change is made in the noise removing procedure to possibly preserve more useful EMG signal, while rejecting the unwanted noise. Later, a simulation study is proposed to evaluate the performance of our denoising method, which is proved to be better than the conventional one.Though EMG has been extensively used to study function of back muscle that plays an important role in the objective assessment of occupational low back pain, the inherent large variability of EMG signals across subjects produced by reasons already known or unknown may mask true biological differences. Some useful parameters abstracted from EMG signals in time domain, frequency domain, and time-frequency domain are used or proposed and calculated in this dissertation to decrease this variability, when comparing the possible difference between LBP patients and normal control group. In time domain, EMG amplitude is normalized under the condition of maximum voluntary contraction to make the root mean square (RMS) value draw a more clear line between the two groups. In frequency domain, to overcome the nonstationary nature of EMG signal in dynamic contractions, we cut the signal into several segments according to the features available in different motion stages and calculate median frequency from EMG power spectral density (PSD) separately. In time-frequency domain, the calculation of instantaneous median frequency is improved to decrease the interrupt of back ground noise. And, different power densities in EMG time-frequency spectrogram are observed in two groups. In addition, multi-channel EMG topography is suggested to analyze the balance of para-spinal muscle function, which proved to be crucial to LBP assessment.