Abstract: A method of automatically monitoring electrophysiological data in the brain and detecting clinically significant events comprises receiving signal inputs from at least one or more electrophysiological signal channels each indicative of electrical brain activity. For each of the one or more electrophysiological signal channels, the signals are filtered to obtain a first subchannel having a first frequency range and a second subchannel having a second frequency range. Appearance of a succession of correlated, non-synchronous events are detected in the waveforms of the one or more first subchannels to create a first detection output. Suppression of an amplitude of the signal is detected in one or more of the second subchannels correlated with the detected events in the one or more first subchannels to create a second detection output. The detected events are classified as a predetermined type of clinically significant event according to the first and second detection outputs.