A method and an apparatus for assessing an effect of a relaxation stimulus exposed to a human have been disclosed. The method comprises the steps of providing a skin conductance signal measured at an area of the human's skin through a time interval; detecting peaks in the skin conductance signal through the time interval; determining a rate of the peaks through the time interval; and determining the effect of the relaxation stimulus based on the determined rate of peaks through the time interval.

Devices and methods for stimulation and recording of muscle responses for determining degree of neuromuscular blockade, particularly relevant to elicitation of such responses in those under the influence of anesthesia. A system for estimating the degree of neuromuscular blockade includes at least one stimulating electrode, at least one recording electrode, a pulse generator for providing stimulation to a nerve through the stimulating electrode, and a computing device configured to: apply stimuli to the nerve according to a stimulation protocol, wherein the stimulation protocol provides a plurality of stimulation sequences that vary in frequency of pulses in the stimulation sequence, frequency of the stimulation sequences, number of pulses in the stimulation sequence, or all of the above; measure, by the recording electrode, electrical responses of a muscle; and estimate the degree of neuromuscular blockade based on changes in the electrical responses of the muscle during a stimulation sequence.

A procedural sedation monitoring system increases awareness of medical providers of physiologic parameters of a patient throughout a medical procedure and includes a patient monitoring system having a plurality of patient sensors disposed in an operative engagement with the patient. A processor assembly includes a patient monitoring signal receiver and signal processor to receive and analyze a plurality of patient monitoring signals. An environmental control assembly includes an environmental control device and the processor assembly transmits an environmental control signal to the environmental control device to control operation thereof and to alter an aspect of the environment based on the signal. A sensory feedback assembly includes a sensory feedback device wearable by medical providers and having a sensory feedback generator providing a sensory feedback sensation to medical providers based on the sensory feedback signal to alert medical providers of physiologic parameters of the patient throughout the medical procedure.

A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.

The process for classifying anesthetic depth includes: collecting of biological signals, conditioning of said signals, monitoring of activity of the central and autonomic systems, measurement of indexes and classification of patterns in anesthetic depth. The activity includes: i) Awake: Vigil—Ak. and recovery of verbal response—Rc. ii) Light Anesthesia: Light induction anesthesia—Li. Light recovery—Lr, Light dose, increase in drugs or patient movement (La), iii) General anesthesia: General anesthesia—Ga, one minute after the start of the surgery, and iv) Deep anesthesia: identification of the EEG burst-suppression pattern (BSP) associated with deep anesthesia.

The present invention relates to sedation assessment. In order to facilitate sedation depth monitoring in an autonomous imaging setting, it is proposed to use the imaging modality itself to measure the response to suitable reflexes in order to determine the depth of sedation wherein suitable reflexes include, but are not limited to, the pupil reflex, so-called superficial reflexes and the withdrawal reflexes. In one embodiment, the pupil reflex may be measured in an MRI system by repeated interleaving of dedicated iris MR imaging with the conventional scan protocol. In another embodiment, superficial reflexes in response to stroking of the skin may be measured. This may involve a dedicated actuator that may be closely integrated with the imaging modality, e.g. an MR receive coil applied to the patient. Alternatively, remote haptic systems may be used. The reflex is then acquired with a suitable diagnostic imaging method. In another embodiment, the withdrawal reflex in response to pain may be measured. This may involve an actuator that induces sudden stitching pain or very local temperature-induced pain and that is closely integrated with the imaging modality, e.g. a pinching device integrated with a patient support or an MR receive coil applied to the patient. The reflex is then acquired with a suitable diagnostic imaging method.

The present invention provides a system for implementing a logistic regression classification mechanism to measure and assess a depth of anesthesia of an animal subject based on electroencephalography (EEG), which includes a signal pre-processor, an epoch generator, a feature extractor, a classifier, and a predictor. Related method of how to pre-process the raw data of EEG signal, epoch generation thereof, feature extraction from each epoch, classification based on extracted features, and prediction of different states of the animal subject based on a prediction decision mechanism is also provided. Classification accuracy of the present invention for 1-second and 10% overlapping epochs is about 94% with an average total system delay of about 12 μs and low on-chip power consumption. The present system is entirely optimized, which leads to a 100% accurate channel prediction after a 7-second run-time on average.

This document discusses a medical system for coupling to one or more implantable electrodes. The medical system includes a sensing circuit, memory, and processing circuitry. The sensing circuit is configured to sense one or more neural signal representative of neural activity of a subject when connected to an implantable electrode of the one or more implantable electrodes, and the memory is to store a reference signal that is representative of a neural response associated with a state of arousal at or near an anatomical location of the implantable electrode. The processing circuitry is configured to compare the one or more sensed neural signals to the reference signal, and to determine a depth of anesthesia of the subject according to the comparison of the one or more sensed neural signals and the reference signal.

A method of method of high flow oxygen therapy (HFOT) and carbon dioxide (CO.sub.2) monitoring includes delivering high flow oxygen therapy (HFOT) via a central lumen of a nasal cannula, the nasal cannula comprising a proximal end, a distal end positioned within a pharynx region of a patient's airway, and the central lumen and a sampling lumen formed within a wall of the nasal cannula. The method also includes receiving sampled exhaled breath of the patient via the sampling lumen at a CO.sub.2 monitor, wherein the sampling lumen is configured to sample the exhaled breath at the pharynx region through the CO2-permeable membrane and direct the sampled exhaled breath to a CO.sub.2 monitor fluidly coupled to the sampling lumen and determining a level of CO.sub.2 in the exhaled breath using the CO.sub.2 monitor.

A camera (26) takes an image of at least one user (U1, U2, U3). A room environment information sensor (13) senses room environment information relating to an environment of a room (r1) in which the at least one user (U1, U2, U3) is present. The estimator (66) estimates a drowsiness condition of the at least one user (U1, U2, U3) based on the image of the at least one user (U1, U2, U3) taken by the camera (26) and the room environment information sensed by the room environment information sensor (13).