The present invention is to perform appropriate noise reduction processing on an image having different signal levels or noise levels depending on an imaging condition or a reconstruction condition. A magnetic resonance imaging apparatus according to the invention includes: a measurement unit that receives a nuclear magnetic resonance signal generated in a subject by a receiving coil; an image reconstruction unit that processes the nuclear magnetic resonance signal received by the receiving coil and reconstructs an image of the subject; an SNR spatial distribution calculation unit that calculates spatial distribution of a signal-to-noise ratio of the image using spatial distribution of a noise level and spatial distribution of the signal of the image; and a noise reduction unit that reduces noise from the image based on the spatial distribution of the signal-to-noise ratio.

Systems and methods are provided for detecting and analyzing changes in a body. For example, a system includes an electric field generator configured to produce an electric field. The system includes an external sensor device configured to detect physical changes in the electric field, where the physical changes affect amplitude and frequency of the electric field. The system includes a quadrature demodulator configured to detect changes of the frequency of the output of the electric field generator. The system includes an amplitude reference source and an amplitude comparison switch configured to detect changes of the amplitude of the output of the electric field generator. The system includes a signal processor configured to analyze the changes of the amplitude and frequency of the output of the electric field generator.

This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques. Finally based on personalized training model, the extracted breathing pattern is classified as high cognitive workload or low cognitive workload.

A therapeutic or diagnostic device comprises a wearable electrodes garment including electrodes disposed to contact skin when the wearable electrodes garment is worn, and an electronic controller operatively connected with the electrodes. The electronic controller is programmed to perform a method including: receiving surface electromyography (EMG) signals via the electrodes and extracting one or more motor unit (MU) action potentials from the surface EMG signals. The method may further include identifying an intended movement based at least on features representing the one or more extracted MU action potentials and delivering functional electrical stimulation (FES) effective to implement the intended movement via the electrodes of the wearable electrodes garment. The method may further include generating a patient performance report based at least on a comparison of features representing the one or more extracted MU action potentials and features representing expected and/or baseline MU action potentials for a known intended movement.

A method for determining if a tissue is ganglionic is provided. The method comprises a) generating at least one hyperspectral Raman image of a tissue from a region of interest in a tissue suspected to contain ganglion cells that was optionally identified by a method comprising autofluorescence (AF) and Second Harmonic generation (SHG) imaging of the tissue; and b) analyzing any of the images for one or more of the following: i) optical excitation; ii) chemical information or emission spectra; or iii) AF, SHG, and/or Raman signatures, wherein the analysis provides indicators that the region of interest is either ganglionic or non-ganglionic. A system for analysis of in vivo tissue or ex vivo tissue samples including a multiphoton autofluorescence microscope, a Second Harmonic Generation microscope, and a hyperspectral Raman microscope in operative communication is also provided.

A computer implemented method is provided that includes, under control of one or more processors of an implantable medical device (IMD), obtaining motion data indicative of a first posture, and determining a first sense setting of the IMD based on the first posture. The method also includes obtaining cardiac activity (CA) signals for a series of beats while applying the first sense setting, obtaining a characteristic of interest (COI) from the CA signals for the series of beats, and calculating a statistical indicator from the COI over the series of beats based on the COI from the CA signals. The method also includes deriving a second sense setting based on the first sense setting and the statistical indicator of the COI.

A method can include receiving (1) images of at least one subject and (2) at least one total mass value for the at least one subject. The method can further include executing a first machine learning model to identify joints of the at least one subject. The method can further include executing a second machine learning model to determine limbs of the at least one subject based on the joints and the images. The method can further include generating three-dimensional (3D) representations of a skeleton based on the joints and the limbs. The method can further include determining a torque value for each limb, based on at least one of a mass value and a linear acceleration value, or a torque inertia and an angular acceleration value. The method can further include generating a risk assessment report based on at least one torque value being above a predetermined threshold.

A method and a wearable device are provided. The method includes providing a swimming mode in the wearable device, and storing standard swimming stroke data that have been collected in advance as corresponding template data, when a monitoring process starts, activating the swimming mode according to an instruction given by a user who will immediately enter water, and after the swimming mode has been activated, controlling a sensor to collect swimming stroke data of the user; obtaining test data for identifying a swimming state of the user from the swimming stroke data; and matching the test data with each template data, when the test data successfully matches the template data, identifying the swimming state of the user to be the swimming state that corresponds to the template data.

A method of characterizing a patient's disordered breathing during a sleeping period includes performing a first partial characterization of a time axis of an audio signal in order to learn the most prominent and highly relevant events. Only at a later stage, i.e., after sufficient observation of the highly relevant events, is a full segmentation of the entire time axis actually carried out. Linear prediction is used to create an excitation signal that is employed to provide better segmentation than would be possible using the original audio signal alone. Warped linear prediction or Laguerre linear prediction is employed to create an accurate spectral representation with flexibility in the details provided in different frequency ranges. A resonance probability function is generated to further characterize the signals in order to identify disordered breathing. An output includes a characterization in any of a variety of forms of identified disordered breathing.

An embodiment of the invention provides a method of estimating a body temperature of an individual where physiological data is received from at least one sensor 510. Environmental data is received and the physiological data and the environmental data are input into a model. The model generates an estimated body temperature and an estimated physiological condition based on the physiological data and the environmental data. A processor 520 compares the estimated physiological condition to a measured physiological condition in the physiological data. A controller 530 modifies at least one parameter in the model when the difference between the estimated physiological condition and the measured physiological condition is above a threshold.