Competitive Advantages:
Significantly Improved Processing Speed, Improved Accuracy of Machine Learning Analytics, Real-time Processing, Automated Decision Making.
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.USF researchers have created analytic systems with the ability to analyze data in real-time. These analytics take the random nature of changes into account via a control process to speed up the prediction process and provide more accurate decision making procedures. The analytic method combines process control, signal processing, and machine learning analytics together to form a decision making system. This novel method is applicable to multiple processes over a variety of fields such as health sciences (real-time epileptic seizure prediction, heart problem diagnosis), environmental sciences (predicting and detecting honey bee dance moves used for communication), finance (decision making upon potential market changes). The adaptability of the analytic method has great potential to transform machine learning across multiple disciplines.
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