Electromyographic recordings of low back pain subjects and non-pain controls in six different positions effect of pain levels

Abstract Previous investigators have experimentally demonstrated and/or analytically predicted that temporal whitening of the surface electromyograph (EMG) waveform prior to demodulation improves the EMG amplitude estimate. However, no systematic study of the influence of various whitening filters upon amplitude estimate performance has been reported. The authors describe a phenomenological mathematical model of a single site of the surface EMG waveform and reports on experimental studies which examined the performance of several temporal whit...

Abstract Temporal whitening of individual surface electromyograph (EMG) waveforms and spatial combination of multiple recording sites have separately been demonstrated to improve the performance of EMG amplitude estimation. This investigation combined these two techniques by first whitening, then combining the data from multiple EMG recording sites to form an EMG amplitude estimate. A phenomenological mathematical model of multiple sites of the surface EMG waveform, with analytic solution for an optimal amplitude estimate, is presented. Experi...

Abstract To determine the status of a muscle, surface electromyography (SEMG) is a useful tool being non-invasive and easy to record. Clinicians are able to classify the signal visually but because of the large number of parameters of the signal, automatic classification becomes difficult. This paper reports our efforts at using Wavelet Transforms to process the signal before using Neural Networks for classification. The paper reports that by using specific wavelets for transform and at specific levels of decomposition, the features of the sig...

Abstract To determine the status of a muscle, surface electromyography (SEMG) is a useful tool being non-invasive and easy to record. Clinicians are able to classify the signal visually but because of the large number of parameters of the signal, automatic classification becomes difficult. This paper reports our efforts at using Wavelet Transforms to process the signal before using Neural Networks for classification. The paper reports that by using specific wavelets for transform and at specific levels of decomposition, the features of the sig...