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.

A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.

A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.

The present invention relates to a method and system for calculating eating bites of a user. The method comprises: (a) continuously measuring the electrical properties data of mastication of a user for a predetermined period of time; (b) periodically determining single eating bites according to the data obtained in step (a) through a time interval; (c) periodically storing the bites determined throughout the predetermined period of time, through a time interval.

The present invention relates to a method and system for calculating eating bites of a user. The method comprises: (a) continuously measuring the electrical properties data of mastication of a user for a predetermined period of time; (b) periodically determining single eating bites according to the data obtained in step (a) through a time interval; (c) periodically storing the bites determined throughout the predetermined period of time, through a time interval.

A system for controlling blood pressure includes a wearable interface having an internal contact surface, the wearable interface configured to at least partially encircle a first portion of a first limb of a subject, a sensing module carried by the wearable interface and configured to determine at least a change in blood pressure of the first limb of the subject, and an energy application module carried by the wearable interface and configured to apply energy of two or more types to the first limb of the subject.

A flexible head probe and modular head probe system that includes an optical functional near-infrared spectroscopy (fNIRS) system and integrated position sensor. The head probe and modular head probe system determines physiological data based upon the optical information gathered by the fNIRS system and gathers motion and position data from the position sensor. The physiological data and motion and position data are combined to permit topographical and tomographic analyses of a user's brain tissue.

A flexible head probe and modular head probe system that includes an optical functional near-infrared spectroscopy (fNIRS) system and integrated position sensor. The head probe and modular head probe system determines physiological data based upon the optical information gathered by the fNIRS system and gathers motion and position data from the position sensor. The physiological data and motion and position data are combined to permit topographical and tomographic analyses of a user's brain tissue.

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 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.