Machine Assisted Analysis of Biosignals

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Biologically related time-series data frequently is made up of localized sections containing information with a high degree of information pertinent to decision making hidden among regions with uninformative signal data. I am interested in extracting the important portions of the signal, scoring these regions with their importance using information measures, to allow a human decision maker insight into where within their data the information associated with their decision rests. The overall objective of my work is to produce tools to assist human users in understanding their data, and identify which parts of the data contain the important information. Many interesting problems are described by data of this type, including those I am exploring currently: electromyographic disease characterization, electrophysiological markers for emotional regulation, dynamic postural state risk characterization, and human driver performance estimation. Association mining, the sub-field of machine learning dealing with identifying patterns and their associated information content, is particularly of interest in these risk-based characterizations, as association mining provides a means to associate statistical confidence with rules generated by machine learning exploration. These rules in turn provide a means of capturing the description of a particular signal pattern defined using a waveform template. In contrast to a typical maximum likelihood model, in which the only description may come in the form of a "best" characterization or suggested course of action, the combination of information metrics and rule based patterns allows me to provide a data landscape that allows interactive exploration to assess the relative support and trade offs of different potential characterizations. I am interested in this work in the context of full decision exploration systems: from the collection of raw measures through to visualized decision support, providing a soup-to-nuts analysis chain that carries the relevant information from data through to the final human insight. In the short term, this research will yield improved understanding of the data of interest: postural state, EMG based muscle characterization, driver attention, and other domains characterized by time-series data of biological measures. In the longer term, tools applicable to more general analysis of temporal data through template based analysis will aid the understanding of similar data in any field with this problem type. The impact: better and deeper understanding of the impact of temporal data.