A system comprises a sensor device and processing circuitry. The sensor device comprises a housing configured to be disposed above shoulders of a patient, a plurality of electrodes on the housing, a motion sensor, and sensing circuitry configured to sense a brain electrical signal and a cardiac electrical signal via the electrodes, and a motion signal via the motion sensor. The processing circuitry is configured to determine values over time of one or more parameters from the brain electrical signal, determine values over time of one or more parameters from the cardiac electrical signal, and generate at least one of a detection, prediction, or a classification a condition of the patient based on the values and the motion signal.

The present disclosure relates to a system and method capable of capturing, processing and analyzing electroencephalography (EEG) signals, including features, patterns or signatures with relevance to diagnosis, prognosis, risk stratification or other clinically relevant interpretation, by means of a low-profile head-mounted wireless recording device that may be rapidly applied to the individual being examined. The head-mounted recording device is comprised of an array of electrode elements that contact the subject's forehead, a docking site and an acquisition device that receives, processes, and transmits the EEG data. In one embodiment, the device simultaneously collects additional data, including but not limited to accelerometer data, heart rate, sound level, light level, temperature and/or pulse oximetry. Subsequently, the data is ingested into an analytics system which is capable of identifying features, signatures or patterns that have significance for diagnosis, prognosis, risk-stratification or other medically pertinent observations, estimates or predictions.

Methods and systems implement a variety of sensors, including in embodiments various combinations of EEG sensors, biochemical sensors, photoplethysmography (PPG) sensors, microphones, and accelerometers, to detect, predict, and/or classify various physiological events and/or conditions related to epilepsy, sleep apnea, and/or vestibular disorders. The events can include neuroelectrical events, cardiac events, and/or pulmonary events, among others. In some cases, the method and systems implement trained artificial intelligence (AI) models to detect, classify, and/or predict. The methods and systems are also capable of optimizing a treatment window, suggesting treatments that may improve the overall well-being of the patient (including improving pre- or post-event symptoms and effects), and/or interacting with various care providers.

A platform system for remotely monitoring one or more behavioral event of at least one user is disclosed. The platform system includes at least one computer system having an internet connection and at least one web application, the at least one computer system having a memory for storing an array of contextual data relating to the at least one user; at least one wearable device having a processor and being communicatively connected to the at least one user and being in electronic communication with the at least one computer system, the at least one wearable device having one or more sensors for detecting physiological data of the at least one user, the at least one wearable device having a memory for storing an array of physiological data detected by the one or more sensors; wherein the processor of the at least one wearable device determines a behavioral event relating to the at least one user based on the array of physiological data in view of the array of contextual data, the processor communicating the behavioral event to the computer system; and wherein the computer system provides a notification relating to the behavioral event.

Provided is a method for mapping a neural area involved in speech processing, including applying a plurality of recording electrodes to a surface of a cortex of a human subject, presenting a plurality of auditory stimuli to the subject wherein some of the plurality of stimuli are speech sounds and others of the plurality of auditory stimuli are non-speech sounds, recording brain activity during the presenting of the plurality of auditory stimuli, and identifying one or more brain areas wherein activity changes more after presentation of speech sounds than it does after presentation of non-speech sounds, wherein the human subject does not speak during the presenting and the recording. Also provided is a method for mapping a neural area involved in speech production wherein the human subject does not speak during presenting speech stimuli and recording neural activity.

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

A method for analyzing heart rate variability, and an apparatus and use thereof, the method for analyzing heart rate variability including collecting ECG data in vitro; digitizing and denoising the ECG data; forming the processed ECG data into a sinus NN interval sequence; selecting sinus NN interval data of 4 hours in an awake state; performing MSE calculation on the sinus NN interval sequence of 4 hours in an awake state; and extracting parameters representing the complexity of a heart rate by using MSE curves. The present invention may provide accurate and efficient screening for drug-refractory epilepsy patients who are suitable for vagus nerve stimulation surgery, thus avoiding unnecessary expenses, and avoiding missing the most opportune moment for treatment. At the same time, patients suitable for VNS surgery are selected by using MSE complexity feature parameters of ECG, thus improving the overall efficacy of VNS therapy.

An apparatus (10) for minimally-invasive, including non-invasive, prevention and/or treatment of hydrocephalus and method for use of the same are disclosed. In one embodiment of the apparatus (10), a housing (50) is sized for superjacent contact with a skull having a fontanel. Within the housing (50), a compartment (12) includes a pressure applicator (88), such as a fluid-filled bladder (22), under the control of a pressure regulator (14). The pressure applicator (88) is configured to selectively apply an external pressure to the fontanel. The compartment (12) includes a pressure sensor (90) configured to measure intracranial pulse pressure of the fontanel. Further, in one embodiment, the apparatus (10) can cause pulse pressure modulation by adjusting the intracranial pulse pressure via the pressure applicator (88). This enables a non-invasive measurement of the pulse pressure and modulation thereof in infants, for example.

There is provided a system and method for classifying time series data for state identification. The method including: training a machine learning model to classify occurrences of the state; receiving a new time series data stream; determining whether a current sample in the new time series data stream is an occurrence of the state by determining a classified feature vector, the classified feature vector determined by passing the current sample and samples in at least one continuous sampling window into the trained machine learning model, each continuous sampling window including a plurality of preceding samples from the time series data, an epoch for each respective continuous sampling window determined according to a respective exponential decay rate; and outputting the determination of whether the current sample is an occurrence of the state.

A method, comprising receiving at least one of a signal relating to a first cardiac activity and a signal relating to a first body movement from a patient; triggering at least one of a test of the patient's responsiveness, awareness, a second cardiac activity, a second body movement, a spectral analysis test of the second cardiac activity, and a spectral analysis test of the second body movement, based on at least one of the signal relating to the first cardiac activity and the signal relating to the first body movement; determining an occurrence of an epileptic event based at least in part on said one or more triggered tests; and performing a further action in response to said determination of said occurrence of said epileptic event. Further methods allow classification of epileptic events. Apparatus and systems capable of implementing the method.