Methods and systems detect a random state change in a subject in real time. Eye state changes may be identified in encephalogram brain signals, or honeybee dance patterns may be classified. Multivariate signals including state change information are received via a plurality of channels. The signals are sampled and may be filtered to remove DC components. Statistical characteristics of the signals are monitored. When the statistical characteristics exceed a threshold during a critical time interval, a potential change of state is detected. The critical time segment of the signals may be filtered to generate respective state change artifact signals. The state change artifact signals are decomposed by MEMD, and intrinsic mode functions are generated. Features are extracted from the intrinsic mode functions. These steps may be repeated while the extracted features are provided to a logistic regression classifier that is used to predict a state of the subject.
Saghafi, Abolfazl and Tsokos, Chris P., "Machine learning analytics in real time for health services" (2022). USF Patents. 1287.
University of South Florida