In some embodiments of the present invention, a method of reducing artifacts includes obtaining OCT/OCTA data from an OCT/OCTA imager; preprocessing OCTA/OCT volume data; extracting features from the preprocessed OCTA/OCT volume data; classifying the OCTA/OCT volume data to provide a probability determination data; determining a percentage data from the probability data determination; and reducing artifacts in response to the percentage data.

A serialized probe component for an optoacoustic device has a unique identifier associated therewith and includes, in an embodiment, an operative connection between a read-write memory and the optoacoustic device. Software adapted to generate and store logs in a read-write memory is executed on the optoacoustic device and stores logs concerning utilization of the serialized probe component on the read-write memory. A method for logging operational information concerning an optoacoustic device is further disclosed.

A method for measuring spasticity is provided and includes: obtaining sensing signals corresponding to a limb movement through at least one sensor during a period of time; transforming the sensing signals into a two-dimensional image; and inputting the two-dimensional image into a convolutional neural network to output a spasticity determination result.

A method for generating an acoustic stimulus for use in an ear biometric process on a user, the method comprising: receiving an indication of stimulation frequencies for use in the ear biometric process; grouping the stimulation frequencies into bands of a psychoacoustic scale; generating the acoustic stimulus, the acoustic stimulus comprising a masked bandpass component within each band of the psychoacoustic scale that comprises one or more of the stimulation frequencies.

Disclosed herein are techniques for graphically indicating aspects of rotors (such as pivot points of rotors) associated with atrial or ventricular fibrillation. Embodiments can include receiving, using a processor, an electrogram for each of a plurality of spatial locations in a heart, each electrogram comprising time series data including a plurality of electrical potential readings over time. Embodiments can also include generating, from the time series data, one or more of multi-scale frequency (MSF), kurtosis, empirical mode decomposition (EMD), and multi-scale entropy (MSE) datasets, each dataset including a plurality of respective values or levels corresponding to the plurality of spatial locations in the heart. Also, examples can include generating, from the one or more datasets, a map including a plurality of graphical indications of the values or levels at the plurality of spatial locations in the heart, wherein the map can include an image of the heart and graphical indications of locations of aspects of rotors in the heart (such as pivot point of rotors).

The method for determining a depression state of the present invention comprises the steps of: measuring a pulsation interval of a subject, and an acceleration or an angular velocity associated with a movement of the subject (that is hereinafter referred to as an “activity”); and determining the subject to be a depression state when at least one of the following conditions [A] and [B] is satisfied: [A] In an awaking time zone of the subject, at least one of the following formulas is calculated and satisfied; the pulsation interval×the activity<C1, HF×the activity<C2, (LF/HF)/the activity>C3; [B] In a sleeping time zone of the subject, at least one of the following formulas is calculated and satisfied: the pulsation interval/the activity<C4, HF/the activity<C5, (LF/HF)×the activity>C6.

A detected signal of a heartbeat sensor and a detected signal of an inertial sensor are received by a processor. The processor estimates a heart rate in a case where an exercise intensity obtain from the detected signal of the inertial sensor is equal to or more than a threshold value, based on a relation between the exercise intensity and a heart rate obtained from the detected signal of the heartbeat sensor during a period in which the exercise intensity obtained from the detected signal of the inertial sensor is less than the threshold value.

The present disclosure relates to systems and methods for probabilistically estimating an individual's pulse rate in the presence of nontrivial noise spectra. In one implementation, the method may include receiving a set of signals from a set of sensors associated with the device, the set of signals including a photoplethysmographic (PPG) signal obtained from the individual; transforming the PPG signal to the frequency domain; applying a band-pass filter to the transformed signal to generate a filtered signal; identifying one or more peaks in the filtered signal; generating a set of one or more sample classifiers for each peak in the set of one or more peaks; and comparing the set of sample classifiers to a set of library classifiers included in a learning library. Each library classifier is associated with a classification coefficient reflecting a degree of correlation between the library classifier and a known pulse rate profile.

A biological information measurement apparatus includes a phase/frequency comparison unit that outputs a deviation signal based on a phase difference between a biological signal and an oscillation signal; a loop filter; and a voltage controlled oscillation unit that generates the oscillation signal in accordance with the deviation signal that has passed through the loop filter. The apparatus further includes a CPU that estimates a SN ratio of the biological signal and analyzes a phase difference/frequency difference between the biological signal and the oscillation signal. A variable low pass filter is provided that selectively blocks a signal of a predetermined frequency band contained in the deviation signal that has passed through the loop filter and the CPU changes a cutoff frequency of the variable low pass filter based on the SN ratio and the phase difference/frequency difference.

In accordance with an example embodiment, a body-weight sensing scale includes cardio-based physiological sensing circuitry to detect heart characteristics of a user, and provide outputs indicative of the detected heart characteristics. A processor circuit is arranged with the cardio-based physiological sensing circuitry to process data to provide a noise-reduced cardiogram signal which characterizes functionality/health of the user's heart.