Abstract: A system for measuring changes in the number of maxima and minima occurring in a signal over time which changes indicate activity changes in a subject from which the signal measured is obtained.

Abstract: Such representations may include the average frequency value of electroencephalographic signals, the logarithm of the power of electromyographic signals, and eye movement indications from electrooculographic signals. Criteria for dividing the records of such representations in the system into stages of sleep can be set by a system operator reviewing these records based on accepted stage definition rules. In addition, transient events such as sleep spindles, etc., in these records can be selected therefrom by criteria set by the system operator also based on accepted rules therefor.

Abstract: An electroencephalograph (EEG) signal analyzer allows the examination of the changes in EEG cerebral activity at various sites in response to an event (e.g. a stimulus or task). The analyzer includes one or more signal processing modules which periodically sample the EEG signal from each of the sites, convert the sampled signals to digital sample values, and store those values. Digitized waveforms based on the stored digital sample values and having a length equal to or greater than the period of the lowest frequency of interest are transformed from the time to the frequency domain. For each of a plurality of epochs, a frequency spectrum is produced having frequency content which has a content uniquely due to the digital sample values from that epoch. A weighted means frequency value for each site during each epoch is derived from the corresponding frequency spectrum.

Abstract: An electroencephelograph (EEG) signal analysis system automatically determines and displays, on a real-time basis, the frequency content of spontaneous EEG signals from the brain. The analog EEG signals from a plurality of channels are sampled and converted to digitized EEG waveforms during a continuing sequence of epochs of predetermined time duration. The digitized EEG waveform for each epoch and channel is transformed from the time domain to the frequency domain to produce a frequency spectrum representing amplitude of the EEG signal as a function of frequency. The digital data is screened both prior and subsequent to the transformation to identify those epochs which contain artifacts. During each update period, the frequency spectra for each channel from the artifact-free epochs are averaged. The amplitudes of each averaged frequency spectrum for frequencies in each of four basic frequency bands are then summed. The result is an amplitude value for each of the four frequency bands at each channel.