Delirium classification computing system

A delirium detection computing method, comprising:

• receiving a plurality of electroencephalography (EEG) signals from one or more EEG electrodes;
• preprocessing the received plurality of EEG signals to generate preprocessed EEG signals;
• extracting features to generate EEG representations based on the preprocessed EEG signals;
• executing a delirium classifier to generate a classification indicating whether or not delirium is likely, based on the generated EEG representations;
• and
• generating and outputting one or more notifications based on the generated classification.

A delirium detection computing device, comprising:

• one or more electroencephalography (EEG) electrodes configured to generate a plurality of EEG signals at a position in a forehead region or a periauricular region on a scalp or at an in-ear position;
• processing circuitry;
• and
• a non-volatile memory storing executable instructions that, in response to execution by the processing circuitry, cause the processing circuitry to:
• receive the plurality of EEG signals;
• segment the received plurality of EEG signals into discrete temporal data segments to generate preprocessed EEG signals;
• extract features to generate EEG graphs based on the preprocessed EEG signals, wherein to generate each EEG graph, a current EEG graph is superimposed onto a preceding EEG graph from a time-delayed segment;
• execute a delirium classifier to generate a classification indicating whether or not delirium is likely, based on the generated EEG graphs;
• and
• generate and output one or more notifications based on the generated classification, wherein
• the delirium classifier is configured as a time-continuous machine learning model.

A brain state detection computing system, comprising:

• a sensor system comprising one or more electroencephalography (EEG) electrodes configured to generate a plurality of EEG signals;
• processing circuitry;
• and
• a non-volatile memory storing executable instructions that, in response to execution by the processing circuitry, cause the processing circuitry to:
• receive the plurality of EEG signals;
• preprocess the received plurality of EEG signals to generate preprocessed EEG signals;
• extract features to generate EEG representations based on the preprocessed EEG signals;
• execute a brain state classifier to generate a classification of a predetermined brain state, based on the generated EEG representations;
• and
• generate and output one or more notifications based on the generated classification.