The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal. Wavelet-based features or parameters may also include measures that are derived from a statistical quantification of the distribution of the power of the high-power spectral contents in the waveform signals of interest.

Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.

A system may include an ultrasound probe and a controller unit configured to communicate with the ultrasound probe. The controller unit may be further configured to transmit ultrasound signals using the ultrasound probe toward an area of interest in a patient's body, wherein the ultrasound signals include a fundamental frequency signal and at least one harmonic frequency signal; receive echo signals from the area of interest based on the transmitted ultrasound signals; obtain a fundamental frequency echo signal and at least one harmonic frequency echo signal from the received echo signals; and generate a visual representation of the area of interest based on the obtained fundamental frequency echo signal and the obtained at least one harmonic frequency echo signal.

The present disclosure relates generally to using detected bladder events for the diagnosis of urinary incontinence or the treatment of lower urinary tract dysfunction. A system includes a sensing device comprising a pressure sensor to directly detect a pressure within a bladder. The sensing device is adapted to be located within the bladder. The system also includes a signal processing device to: receive a signal indicating the detected pressure within the bladder; detect a bladder event based the detected pressure within the signal; and characterize the bladder event as a bladder contraction event or a non-contraction event. The characterization of the bladder event can be used in the diagnosis of urinary incontinence or the treatment of lower urinary tract dysfunction.

An electroanatomical mapping system graphically represents local activation time (LAT) information contained in data set including a plurality of electrophysiology (EP) data points. The system computes a spatial gradient over a spatial kernel centered at an EP data point and a plurality of temporal gradients for the EP data points set. Using the gradients, the electroanatomical mapping system can detect spatial outlier EP data points and temporal outlier EP data points. These outlier EP data points can then be corrected prior to outputting a graphical representation of the LAT map on a model of a cardiac surface.

There is provided a method of providing information about prognosis after cardiac arrest. The method includes the steps of: calculating biological information based on a signal relating to a hemoglobin concentration measured from a cerebral region of a subject to be measured; and providing the information about the prognosis after cardiac arrest of the subject with reference to the calculated biological information and a biomarker relating to the prognosis after cardiac arrest measured from a blood of the subject.

The present disclosure provides a method for recognizing crackles, including: processing collected lung sound signal to extract moist rale component for a respiratory cycle; calculating a power spectrum of the moist rale component, and, calculating at least one of a ratio of power of each preset frequency band to total power of all preset frequency bands and the total power of all the preset frequency bands as a frequency domain parameter, and/or calculating at least one of a ratio of the number of occurrence of moist rale and a maximum amplitude of moist rale in the entire inspiratory phase as a time domain parameter; inputting the frequency domain parameter and/or the time domain parameter serving as a parameter feature into a classification model for recognition. The present disclosure further provides a system for recognizing crackles.

A plurality of electrophysiology (EP) data points, each including an electrogram signal, can be used to visualize cardiac activity. Each EP data point can be characterized as substrate or healthy, and a cloud map of the substrate EP data points can be generated. A graphical representation of the cloud map can be output in combination with a graphical representation of an electrophysiology map of the healthy EP data points. In alternative embodiments, the electrogram signals can be transformed into the wavelet domain, thereby computing a plurality of scalograms, and computing a wave function of each scalogram, thereby computing a plurality of wave functions. A propagation map, such as a propagation wave map and/or propagation wave trail map, can then be generated from the wave functions and output graphically.

The embodiment of the present disclosure provides a method and a system for identifying a user action. The method and system may obtain user action data collected from a plurality of measurement positions on a user, the user action data corresponding to an unknown user action, identify that the user action includes a target action when obtaining the user action data based on at least one set of target reference action data, the at least one set of target reference action data corresponding to the target action, and send information related to the target action to the user.

A method and system are provided for continuous monitoring perfusion of an organ or extremity, and for early detection of progressive partial occlusion of arterial blood supply or venous drainage of tissue. The method and system measure a delay in wave propagation of a blood perfusion wave, which is associated with flow of blood through a blood vessel. The delay is correlated to an amount of obstruction in the blood vessel.