Measurement of synchrony in networks of complex or high-dimensional, nonstationary, and noisy systems such as the mammalian brain is technically difficult. We present a general method to analyze synchrony from multichannel time series. The idea is to calculate the phase-synchronization times and to construct a matrix. We develop a random-matrix-based criterion for proper choosing of the diagonal matrix elements. Monitoring of the eigenvalues and the determinant provides an effective way to assess changes in synchrony. The method is tested using a prototype nonstationary dynamical system, electroencephalogram (scalp) data from absence seizures for which enhanced synchrony is presumed, and electrocorticogram (intracranial) data from subjects having partial seizures with secondary generalization.