1. A method for processing data to provide a forewarning of a critical event, comprising: acquiring a plurality of sets of data with a plurality of channels of data for at least one test subject or process; computing a renormalized measure of dissimilarity for distribution functions derived from a connected phase space for each respective channel of data; comparing said renormalized measure of dissimilarity to a threshold (U C ) for a number of occurrences (N OCC ) to indicate a condition change in said renormalized measure of dissimilarity; detecting a simultaneous condition change in a plurality (N SIM ) of renormalized measures of dissimilarity to determine a forewarning of the critical event; determining true positive, true negative, false positive and false negative indications of condition change forewarning of the critical event for each channel of data in the plurality of sets of data; calculating a total true rate for forewarning indications for each channel of data; and comparing the total true rates for respective channels to determine at least one channel with the greatest channel-consistent total-true rate in said at least one channel.