A real-time surface EMG topographic system for lumbar muscular function detection

Abstract

Surface electromyography (SEMG) has been widely used in functional measurement of lumbar muscles, which can be applied for low back pain (LBP) rehabilitation assessment. Previous study has reported the application of SEMG topographic analysis for obtaining an objective and quantitative assessment of LBP. However, the previous SEMG topographic analysis was performed manually and offlined, which was time consuming and with big inter-observer variations. This will limit the use of this technique for practical assessment of LBP and rehabilitation. Therefore, the objectives of this thesis are to design and develop an automatic and online SEMG topographic analysis system, and to verify its potential uses in clinical tests. Current problems in the process of SEMG topographic analysis include noise removal and signal segment. The recording of SEMG on lumbar area is usually contaminated with a lot of noises, while electrocardiography (ECG) is a main source of influences to mislead the characteristics of SEMG. Previous study proved the use of independent components analysis (ICA) to be a useful tool to eliminate ECG from raw SEMG signals, but it was not implemented in current available SEMG system. The second problem is the automatic identification of SEMG signals in different motion phases, i.e. trunk flexion and extension during forward-bending motion. To achieve an automatic and online SEMG topographic analysis, the present study will develop the system with three unique functional components: 1) an automated feature cognition and identification for ECG artifact removal, 2) an automatic segmentation algorithm for signal processing, and 3) a quantitative analysis of SEMG topographic characteristics. Four topographic parameters, namely relative area (RA), relative width (RW), relative height (RH) and relative width-to-relative height (W/H), are employed for quantifying the characteristics of SEMG topograph. A clinical test, enlisting twenty healthy subjects and forty-one LBP patients, was conducted to verify the functionality and reliability of the developed system based on these four parameters. In addition, the dynamic variation of these four topographic parameters with respect to time was also studied by asking the subjects to perform forward-bending movements. Results showed that the developed system is highly reliable. From the results, it was also found that the time-based changing patterns of the four topographic parameters in healthy subjects can be used as a reference indicator for distinguishing between normal subjects and LBP patients. The applicability of the developed system was further verified and demonstrated by studying the effect of wearing soft lumbar corsets (SLCs) on low back neuromuscular function in elderly patients with acute LBP. In conclusion, the developed real-time SEMG topographic analysis system can be used for detecting lumbar muscle function and producing quantitative assessment results for various rehabilitation applications.