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This paper proposes the Stationarity Index, a measure of the similarities of the auto correlation integral of a section of a time series and the cross correlation of that section with others of the same time series. This measure of similarity is a measure of the stationarity of the time series and therefore can be used not only to detect nonstationarity but to also quantify it. The index is then successfully used in the analysis of electrocardiogram (ECG) and electroencephalogram (EEG) profiles to identify the changes in the dynamics of the signals as well as the occurrence of various events. The index displays sensitivity to changes in the dynamics exhibited in ECG signals that are the result of partial epileptic seizures, with the index going high in responseto the changes in the dynamics.