A Factor Graph Approach to Model-Based Signal Separation

This doctoral thesis is about a rather new model-based signal processing methodology that is based on factor graphs and message-passing algorithms. Using this, we have developed exemplary signal processing algorithms for various biomedical applications. The main application to evaluate and demonstrate this new methodology was in the field of electromyographic (EMG) signal analysis. EMG signals are electrical signals that are generated during muscle contractions. They can be measured using various kinds of electrodes, e.g., needle or fine-wire electrodes. Their analysis provides valuable information for the diagnosis of neuromuscular disorders, the study of neuromuscular control mechanisms, and the verification of anatomic hypotheses. EMG signals are essentially made up of superimposed action potential trains from several sources. For a thorough analysis, the measured EMG signals need to be decomposed into their constituent trains. However, this task can become difficult if action potentials from different sources overlap. Many EMG signal decomposition methods have been proposed. Traditional algorithms often use heuristic segmentation and clustering approaches. Especially in the case of difficult superpositions with many overlapping action potentials, these algorithms could often not decompose muscle signals correctly. Many other approaches are computationally not feasible when superpositions of many action potentials are to be resolved. To be able to decompose EMG signals with difficult superpositions, new signal processing algorithms based on factor graphs were developed. Factor graphs allow the systematic derivation of advanced model-based signal processing algorithms. A factor graph is a graphical model that