In some examples, a medical device system includes an electrode. The medical device system may include impedance measurement circuitry coupled to the electrode, the impedance measurement circuitry may be configured to generate an impedance signal indicating impedance proximate to the electrode. The medical device system may include processing circuitry that may be configured to identify a first component of the impedance signal. The first component of the impedance signal may be correlated to a cardiac event. The processing circuitry may be configured to determine that the cardiac event occurred based on the identification of the first component of the impedance signal.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

Disclosed are a method and system for detecting a concentration of an analyte based on a change in relative permittivity of a biological tissue within a living body. The method of detecting a concentration of an analyte may include generating a fringing field, measuring a change in a resonant frequency generated by an oscillator based on a change in capacitance attributable to a change in an analyte within a region of the fringing field, and measuring a change characteristic of the analyte within the fringing field based on the change in the resonant frequency.

An apparatus for assisting in providing patient ventilations includes electrodes configured to provide airflow activities signals, chest compression sensors configured to provide chest displacement signals due to chest compressions, a processor and a memory configured to receive the airflow activities and chest displacement signals, identify a presence of chest compressions based on the chest displacement signals, subsequently confirm an absence of chest compressions applied to the patient based on the chest displacement signals, adjust signal processing parameters for the airflow activities signals in response to the confirmed absence of chest compressions, and process the airflow activities signals using the adjusted signal processing parameters to determine feedback for providing the patient ventilations in the absence of chest compressions, and an output device coupled to the processor and the memory and configured to provide the ventilation feedback.

Systems and methods are described herein for tracking, localization or controlling an elongate instrument or other medical instrument in an image or patient.

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.

This drowsiness estimating device estimates a subject's drowsiness from a time-series signal of the subject's eye-openness width. A filtering circuitry filters the time-series signal of the eye-openness width to eliminate signal changes due to the subject blanking and outputs the filtered time-series signal of eye-openness. A feature calculator calculates a feature from at least the filtered time-series signal of the eye-openness width. A drowsiness estimator estimates a drowsiness evaluated value from the feature and outputs an estimated result. The feature calculator includes at least a first feature calculation circuit that calculates a variation of the filtered time-series signal of the eye-openness width within a feature calculation window width and outputs the variation as a first feature.

A method and system for heterogeneous event detection. Sensor data is obtained and divided into discrete data windows. Each data window is defined by and corresponds to a time period of the sensor data. A time-frequency representation over the time period is calculated for each data window. A filter mask is calculated based on the data window corresponding to the time-frequency representation. The filter mask is applied for reverting the time-frequency representation to a time representation, resulting in filtered data. Features, such as extrema or other inflection points, are identified in the filtered data. The features define events, and transforming the time-frequency representation back into the time domain emphasizes differences between more and less prominent frequencies, facilitating identification of heterogeneous events. The method and system may be applied to body movements of people or animals, automaton movement, audio signals, light intensity, or any suitable time-dependent variable.

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.

Provided are an apparatus and method for detecting ventricular depolarization (QRS) of an electrocardiogram (ECG). The apparatus includes an input unit configured to receive an ECG signal, a memory configured to store a program for detecting R and ventricular depolarization using the ECG signal, and a processor configured to execute the program, wherein the processor detects a QRS interval and an R-peak using a first-order derivative filter and a max-filter.