Weighted Sliding Empirical Mode Decomposition and its application to neuromonitoring data

Empirical Mode Decomposition (EMD) adaptively and locally decomposes time series into a sum of oscillatory modes and a trend. In this thesis an EMD based method is proposed, called weighted Sliding Empirical Mode Decomposition (wSEMD), which, with a reasonable computational effort, extends the application area of EMD to a true online analysis of time series comprising a huge amount of data if recorded with a high sampling rate. An example for such time series are brain status data acquired during neuromonitoring in neurosurgical intensive care units. WSEMD is applied to those data on the one hand using its property to function as a low-pass filter and on the other hand profiting from its possibility to analyze data online