Described herein are systems and methods for the detection and monitoring of delirium in a subject. Other neurological conditions may also be detected and monitored. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor delirium and/or one or more other neurological conditions that the subject is experiencing or likely to experience. The processing module may also generate indications or assessments for delirium and/or for each neurological condition at an individual level, or optionally, between two or more related neurological conditions.

Described herein are systems and methods for the detection and monitoring of delirium in a subject. Other neurological conditions may also be detected and monitored. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor delirium and/or one or more other neurological conditions that the subject is experiencing or likely to experience. The processing module may also generate indications or assessments for delirium and/or for each neurological condition at an individual level, or optionally, between two or more related neurological conditions.

Methods, systems and wearable devices for real-time mental stress assessment are provided. The methods and systems employ deep learning using a Gated Recurrent Unit (GRU) gating mechanism in a recurrent neural network with a sliding window approach applied to raw EEG data.

Methods, systems and wearable devices for real-time mental stress assessment are provided. The methods and systems employ deep learning using a Gated Recurrent Unit (GRU) gating mechanism in a recurrent neural network with a sliding window approach applied to raw EEG data.

A local wearable brain wave cap device for detection is provided to simultaneously detect brainwave and heart rate variability data of a subject and includes a brain wave detection cap, at least one ear electrode and a transmission unit. The brain wave detection cap includes a wearable device and a plurality of electrode units. The wearable device is suitable for arranging the plurality of electrode units on brain wave positions corresponding to head of a subject. Each of the plurality of electrode units includes an accelerator, a storage unit, an input/output unit and a primary amplifier for detecting a brain wave.

A local wearable brain wave cap device for detection is provided to simultaneously detect brainwave and heart rate variability data of a subject and includes a brain wave detection cap, at least one ear electrode and a transmission unit. The brain wave detection cap includes a wearable device and a plurality of electrode units. The wearable device is suitable for arranging the plurality of electrode units on brain wave positions corresponding to head of a subject. Each of the plurality of electrode units includes an accelerator, a storage unit, an input/output unit and a primary amplifier for detecting a brain wave.

A method for time-synchronizing waveforms from different patient monitors that does not require devices to have high-precision synchronized clocks or to be coupled to a triggering synchronization signal generator. Comparable signals may be obtained from different devices either by placing selected sensors from the devices in the same locations, or by filtering signals from one device to obtain a signal comparable to signals from another device. Filtering may for example transform waveforms into independent components and identify a component that matches a signal from another device. The comparable signals may then be transformed into frequency variation curves, such as time intervals between peak values, to facilitate detection of the time shift between the signals. Cross correlation of the frequency variation curves may be used to locate the precise time shift between the signals. Use of frequency variation curves may be more robust than directly comparing and correlating the original signals.

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

The present invention relates to a system and method of emotion recognition. An emotion recognition system may utilize a Valence-Arousal factor along with training data. The training data may exist as emotions assigned to actual measurements of user inputs. The actual measurements of user inputs may be assigned to a plurality of points on the Valence-Arousal model. A user input acquisition device may be used to collect actual measurements of user inputs. A processor may utilize an algorithm to assign user emotions based on the training data. A user may provide feedback on the assigned user emotions, and the training data may be updated based on the user feedback, depending on whether the user feedback is considered an outlier to the training data.

The present invention relates to a system and method of emotion recognition. An emotion recognition system may utilize a Valence-Arousal factor along with training data. The training data may exist as emotions assigned to actual measurements of user inputs. The actual measurements of user inputs may be assigned to a plurality of points on the Valence-Arousal model. A user input acquisition device may be used to collect actual measurements of user inputs. A processor may utilize an algorithm to assign user emotions based on the training data. A user may provide feedback on the assigned user emotions, and the training data may be updated based on the user feedback, depending on whether the user feedback is considered an outlier to the training data.