An apparatus comprises a data processing unit and a stimulator. The data processing unit receives first electroencephalographic data based on a measurement of a brain of a person exposed to one or more estimated or measured non-zero amounts of anesthetic drug substance. The data processing unit performs a first comparison between the first electroencephalographic data and corresponding data of a reference brain function, and outputs information about the first comparison. The stimulator provides the brain of the person exposed to the anesthetic drug substance with brain stimulation on the basis of a direct or indirect reception of the information about the first comparison.

The invention provides a sensor for measuring both impedance and ECG waveforms that is configured to be worn around a patient's neck. The sensor features 1) an ECG system that includes an analog ECG circuit, in electrical contact with at least two ECG electrodes, that generates an analog ECG waveform; and 2) an impedance system that includes an analog impedance circuit, in electrical contact with at least two (and typically four) impedance electrodes, that generates an analog impedance waveform. Also included in the neck-worn system are a digital processing system featuring a microprocessor, and an analog-to-digital converter. During a measurement, the digital processing system receives and processes the analog ECG and impedance waveforms to measure physiological information from the patient. Finally, a cable that drapes around the patient's neck connects the ECG system, impedance system, and digital processing system.

Disclosed is a method for scoring in real time neural signals of a subject with respect to a reference state characterized by k=1 . . . K reference covariance matrices, the method including the following steps: (i) obtaining neural signals from the subject; (ii) computing a covariance matrix of the neural signals; (iii) computing the Riemannian distances between the covariance matrix and k=1 . . . K reference covariance matrices; (iv) computing a continuous score s in real time based on at least one of the distances computed in step (iii). Also disclosed is a system and method for self-paced modulation or external modulation of neural activity of a subject.

Neural interfaces with the peripheral nervous system have been developed to provide a direct communication pathway between peripheral nerves and prosthetic limbs. Described herein is a microchannel integrated neural network device and system which can control the reinnervated muscles and interpret neurological signals. The acquired bioelectrical signals can be used for the interpretation of mind and create a neural map.

A method of processing EEC signals received from a plurality electrodes. The method comprises processing the EEC signals to determine a modulation index value for each electrode, determining one or more electrodes that have a modulation index value above a threshold level observed during ictal activity, and using the determined one or more electrodes, to identify one or more possible regions of interest corresponding to seizure zones of a subject's brain.

A heart rate monitor includes a magnet supported to move responsive to an arterial pulse and a magnetometer configured to detect changes in a magnetic field produced by the magnet. The magnet can include a plurality of ferromagnetic particles disposed in or on a flexible substrate configured to be held adjacent to human skin subject to arterial palpation and a magnetic sensor configured to sense movement of the ferromagnetic particles.

Examples described herein include defibrillators or other medical equipment that may employ hidden Markov models to classify cardiac rhythms in ECG signals. Hidden Markov models may additionally or instead be used to determine presence of a chest compression from the thoracic impedance signal. Classification of cardiac rhythms may be used to determine when to deliver a shock to a patient. Other applications are also described.

A computer-implemented method for processing ECG data may include: receiving ECG data representing a plurality of heartbeats; analyzing the ECG data to determine whether each of the plurality of heartbeats is a normal heartbeat or an abnormal heartbeat; associating each of the abnormal heartbeats with an existing template or a new template; receiving input related to each new template, wherein the input includes either: a) a confirmation that the new template represents an abnormal heartbeat, or b) a reclassification of the new template as representing a normal heartbeat or a different abnormal heartbeat; and in response to the user input, updating a label of each of the heartbeats associated with each confirmed new template and each of the heartbeats associated with each reclassified new template.

A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing electrodes or other sensors placed proximate to portions of the body below the head to develop raw signals without physically touching the body and penetrating hair and clothing. Additionally, apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing non-contacting sensors placed proximate to portions of the head to develop raw signals. The raw signals are filtered to produce analysis signals including frequency components relevant to brain electrical activity while attenuating unrelated frequency components. The apparatus and methods can be used for biofeedback-based attention training, human performance training, gaming, biometrics, cognitive state detection, and relaxation training. Either wired or wireless signal connections are made to electronic circuitry, typically including a digital computer, for performing signal processing and analysis functions.