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The purpose of this paper is to present an algorithm developed for real-time estimation of skeletal muscle ischemia, based on parameters extracted from in vivo obtained electrical impedance spectra. A custom impedance spectrometer was used to acquire data sets: complex impedance spectra measured at 27 frequencies in the range of 100 Hz-1 MHz, and tissue pH. Twenty-nine in vivo animal studies on rabbit anterior tibialis muscle were performed to gather data on the behavior of tissue impedance during ischemia. An artificial neural network (ANN) was used to quantitatively describe the relationship between the parameters of complex tissue impedance spectra and tissue ischemia via pH. The ANN was trained on 1249, and tested on 946 ischemic tissue impedance data sets. A correlation of 94.5% and a standard deviation of 0.15 pH units was achieved between the ANN estimated pH and measured tissue pH values.