The present invention is in the field of hearing tests. In particular, the present invention relates to systems and methods for determining the integrity of Auditory Nerve Fibers (ANFs) and/or afferent Auditory Nerve Synapses (ANSs) and/or inner-hair cells (IHC) in a subject.

The disclosure provides methods of managing opioid therapy, particularly, for pain management. The methods comprise determining in a subject, for example, a subject who has received an opioid treatment, pupillary unrest in ambient light (PUAL). Low values of PUAL can be used to identify patients at risk for opioid side-effects, such as opioid-related respiratory depression (OIRD), and who warrant attention to prevent such side effects. Accordingly, the methods include monitoring the patients having low values of PUAL for signs of adverse side-effects and/or limiting or avoiding administration of opioids.

Systems and methods are provided for non-invasive measurement of endogenous S-nitrosothiols and related measurements thereof. One or more sensors non-invasively measures a set of one or more biometric parameters within a region of interest of a subject to provide a time series of measurements for each of the set of biometric parameters. A medium stores machine-readable instructions that are executable by an associated processor to perform processing comprising receiving the time series of measurements of the biometric parameter, generating, using a predictive model, a value representing an endogenous S-nitrosothiol content of tissue within the region of interest from the time series of measurements of the biometric parameter, and providing, by a user interface, the value representing the endogenous S-nitrosothiol content of tissue within the region of interest to a user.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to extract atrial signals from electrical signals acquired from a patient suffering from atrial fibrillation. The electrical signals acquired from the patient may be intra-cardiac signals or body surface electrode signals, or both. At least portions of QRS or QRS-T complexes corresponding to determined initial synchronization times are used to generate Fast Fourier Transforms (FFTs) corresponding to the extracted QRS complexes. A series of steps follow to generate isolated atrial signals corresponding to each electrical signal by subtracting generated reconstructed signals corresponding to each such electrical signal therefrom.

Embodiments of a compact portable nuclear magnetic resonance (NMR) device are described which generally include a housing that provides a magnetic shield; an axisymmetric permanent magnet assembly in the housing and having a bore, a plurality of magnetic elements that together provide a well confined axisymmetric magnetization for generating a near-homogenous magnetic dipole field B.sub.0 directed along a longitudinal axis and providing a sample cavity for receiving a sample, and high magnetic permeability soft steel poles to improve field uniformity: a shimming assembly with coils disposed at the longitudinal axis for spatially correcting the near homogenous magnetic field B.sub.0; and a spectrometer having a control unit for measuring a metabolite in the sample by applying magnetic stimulus pulses to the sample, measuring free induction delay signals generated by an ensemble of hydrogen protons within the sample; and suppressing a water signal by using a dephasing gradient with frequency selective suppression.

A method for monitoring a health parameter in a person is disclosed. The method involves transmitting radio waves below the skin surface of a person and across a range of stepped frequencies, receiving radio waves on a two-dimensional array of receive antennas, the received radio waves including a reflected portion of the transmitted radio waves across the range of stepped frequencies, generating data that corresponds to the received radio waves, wherein the data includes amplitude and phase data, deriving data from at least one of the amplitude and phase data, and determining a value that is indicative of a health parameter in the person in response to the derived data.

A laser device includes a seed laser, a plurality of optical amplifiers, and an optical distribution assembly. The seed laser is configured to emit seed laser light. The plurality of optical amplifiers is configured to generate amplified laser light by amplifying the seed laser light. The optical distribution assembly is configured to distribute the seed laser light to an input of each of the optical amplifiers in the plurality and each of the optical amplifiers is configured to direct its respective amplified laser light to a common target.

Methods, systems, and computer programs encoded on a computer storage medium, for improving EEG measurements by identifying artifacts present in EEG measurements and providing a real-time indication to a user of likely artifacts in EEG measurements are described. EEG measurements of a patient can be obtained by placing a wearable device or EEG cap on a patient's head. Sensors in the cap provide EEG data to a computing device that processes the data to identify one or more artifacts in the EEG data. The artifacts can be identified by conducting one or more operations of determining the signal to noise ratio of the line noise, calculating mutual information between sensor pairs, and applying the p-welch method. Based on the types of artifacts identified, the computing device can output an indicator that provides feedback to the technician performing an EEG test to make adjustments to the test setup.

A data processing method for detecting and classifying a segment of a signal that is obtained from a single-channel EEG-recording as a target signal segment or as a non-target signal segment. The method includes a voting process to determine whether classification of a first detected segment of the signal as a target signal segment or classification of a second detected segment of the signal as a non-target signal segment is correct. A device and a system that are configured and arranged to perform the data processing method.

The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.