Brain computer interface BCI comprising an input adapted to be connected to at least one electroencephalography EEG sensor to receive EEG signals, the BCI further comprising a processor running an associative model trained to simulate electrocorticography ECoG signal features from EEG signals received via the input, the BCI comprising an output to transmit the simulated ECoG signal features.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.

In part, the disclosure relates to computer-based methods, devices, and systems suitable for performing intravascular data analysis and measurement of various types of data such as pressure and flow data. The disclosure relates to probes and methods suitable for determining an event in a cardiac cycle such as flow threshold such as a peak flow, a fraction thereof, other intravascular parameters or a point in time during which peak flow or a change in one of the parameters occurs. An exemplary probe includes one or more of a pressure sensor, a resistor, a flow sensor and can be used to generate diagnostic data based upon measured intravascular and other parameters. In part, the disclosure relates to methods and systems suitable for determining a coronary flow reserve value in response to one or more of intravascular pressure and flow data or data otherwise correlated therewith.

Disclosed is a glucose measuring apparatus including a pressure measurer having an elastic part or a pressure sensor, that measures a pressure applied to an object, a film that comprises a first optical waveguide configured to be close to the object, a near infrared ray (NIR) irradiator that irradiates an NIR to the first optical waveguide if the measured pressure is greater than or equal to a preset value, an NIR receiver that receives an attenuated total reflection NIR (ATR-NIR) from the first optical waveguide, and an analyzer that measures a blood glucose level based on the ATR-NIR, wherein the film is an independent module that can be combined with and separated from the glucose measuring apparatus.

An approach for determining an estimated pressure curve for the ventricle of the heart, the method comprising: using data from a motion sensor that has been implanted at the heart to determine the timing of heart cycle events; scaling a reference pressure-time curve including timing of reference heart cycle events in order to fit the reference pressure-time curve to the motion sensor data, the scaling comprising scaling the reference curve along the time axis to fit it to the measured timing of the heart cycle events; and thereby obtaining an estimated pressure-time curve in the form of the scaled reference pressure-time curve.

A method includes generating first electrocardiogram (ECG) data by adding synthetic noise to naturally occurring ECG data using a first deep neural network (DNN). The method further includes providing one of: (i) the first ECG data, or (ii) second ECG data including naturally occurring noise, to a second DNN. An output is generated by the second DNN indicating whether the second DNN received the first ECG data or the second ECG data.

An oximetry device sealed in a sheath directs a user to allow the oximetry device to make oximetry readings at a number of different tissue locations of a patient and average two or more of the oximetry readings by directing the lifts and placements of the oximetry device and sheath to and from the different tissue locations and detecting the lift and placements. The averages are generated and displayed on a display of the device for the oximetry readings if the lifts are made while use directions for the lifts are displayed on a display of the oximetry device. The averages are not generated if the lifts are not made while the user directions for the lifts are not displayed. The averages are simultaneously displayed with the oximetry readings which are instantaneous measurement for patient tissue.

A measurement unit for measuring a bio-impedance of a body, the measurement unit comprising a current generator circuit, a readout circuit, and a baseline cancellation current circuit, wherein the current generator circuit is configured to amplify a reference current to form a measurement current to be driven through a body to generate a measurement voltage representing the bio-impedance; wherein the readout circuit comprises a Instrumentation amplifier (IA) which has a transconductance stage and a transimpedance stage, wherein the IA is configured to: produce a first current in the transconductance stage, the first current being proportional to the measurement voltage, receive a second current from the baseline cancellation current circuit, produce an output voltage in the transimpedance stage, the output voltage being proportional to a difference between the first current and the second current and representative of the measured bio-impedance; wherein the baseline cancellation current circuit is configured to amplify the reference current by a factor to form the second current and deliver it to the IA, wherein the factor is such that that the absolute value of the difference between the first and the second current is below a threshold such that a baseline of the first current is cancelled by the second current.

Audio signals, collected with equipment commonly available to individuals (e.g., a mobile device), can be used to analyze a patient’s breathing. An audio signal associated with the patient’s breathing for a time period can be detected with the mobile device and used to approximate the patient’s respiratory flow for the time period. For example, the audio signal can be analyzed by determining a representation of an audio frequency of the audio signal, splitting the audio frequency of the audio signal into distinct time steps, determining points comprising a weighted mean frequency at each time step, applying a frequency-to-flow rate linear transformation at each time step to approximate the respiratory flow versus time, and plotting a graphical representation of the respiratory flow versus time. The respiratory flow for the time period can be tagged with a factor related to the patient and saved in a database for future analysis.