Data fusion in autonomic nervous system event detection

Data fusion is an evolving discipline which provides a way to integrate data from multiple sensors and sources, deriving new comprehensive information. Due to the multisensor nature of many clinical procedures, data handling and interpretation may be improved by the proper definition and application of these techniques. This work presents a data fusion approach to multisensor autonomic nervous system (ANS) evaluation, and an example of the application of these techniques to multisensor autonomic event detection. The example involves two levels of data fusion, combining information related to the chronotropic, inotropic and dromotropic effects of the ANS on the heart. The latter being calculated from the esophageal electrocardiogram, which is not to often used as a signal source.