Automatic Classification of Long Term Involuntary Spontaneous EMG

of a dissertation at the University of Miami. Dissertation supervised by Assistant Professor Dejan Tepavac and Dr. Christine Thomas. No. of pages in text. (198) Involuntary electromyographic (EMG) activity has been recorded in the thenar (thumb) muscles of spinal cord injured (SCI) subjects for only short time periods (minutes), but it is unknown if this motor unit activity is ongoing. Longer duration EMG recordings can investigate the physiological significance of this neuromuscular activity. Analysis of these data is complex and time consuming. Since no software is currently capable of classifying 24 hours of data at a single motor unit level, the goal of this research was to devise an algorithm to automatically classify motor unit potentials over 24-hours. Twenty-four-hour, 2-channel thenar muscle EMG recordings were obtained from four different SCI subjects with cervical level injuries using a data logging device with custom software. The automatic motor unit classification algorithm used to classify the 24-hour recordings was a procedure consisting of four stages that included segmentation, clustering, and motor unit template uniting. All individual potentials were then classified and any superimposed potentials were resolved into their constituent classes. Finally, the algorithm found the firing patterns for each of the stable motor unit classes. The classification algorithm performance was compared to the analysis of a human operator and assessed in 2 ways: Tracking global classes over the 24