An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

A method is described for extracting aperiodic components from a time-series wave data set for diagnosis purposes. The method may include collecting time-series wave data within a controlled environment were a plurality of contrasting conditions can be used in collecting the time-series wave data set. Aperiodic components can be extracted from the time-series wave data set and the aperiodic components can then be fitted to the plurality of contrasting conditions of the controlled environment to product regressed aperiodic components from which diagnostic determination can be made.

A mass spectrometer support apparatus includes a deconvolution logic and a centroider logic. The deconvolution logic is configured to deconvolve a mass spectrum measured by a mass spectrometer using an approximate peak shape. The centroider logic is configured to integrate the deconvolved spectrum and populate a sparse vector of peak locations.

A Deep-Learning (DL) explainable AI system for Radio Frequency (RF) machine learning applications with expert driven neural explainability of input signals combines three algorithms (A1, A2, and A3). A1 is a neural network that learns to classify spectrograms. During training, A1 learns to map a spectrogram to its paired label. It outputs a label estimate from a spectrogram. Labels account for device number and spectrum utilization. The neural network is built on two-dimensional dilated causal convolutions to account for frequency and time dimensions of spectrogram data. A2 is a user-defined function that converts an input spectrogram into a vector that quantifies human-identifiable elements of the spectrogram. A3 is a random forest feature extraction algorithm. It takes as input the outputs of A2 and A1. From these, A3 learns which elements in the vector output by A2 were most important for choosing the labels output from A1.

Disclosed are a heartbeat data classification method and device based on a point R. The method comprises: (1) obtaining one-dimensional electrocardiogram data of which a time length is a preset fragment time threshold to generate an electrocardiogram data fragment, and calling a target detection algorithm to perform feature recognition of heartbeat signal data on the electrocardiogram data fragment to generate a sequence of first bounding box, wherein the sequence of first bounding box comprises a plurality of first bounding boxes; (2) performing absolute value conversion on all the first bounding boxes in the sequence of first bounding box to generate a sequence of second bounding box, and performing non-maximum suppression on the sequence of second bounding box, wherein the sequence of second bounding box comprises a plurality of second bounding boxes, each second bounding box comprises a group of probability for point R heartbeat classification, and the group of heartbeat classification probability comprises at least one class of parameters for heartbeat classification probability; (3) performing valid parameter and invalid parameter marking on all the parameters for heartbeat classification probability of the group of heartbeat classification probability of all the second bounding boxes in the sequence of second bounding box; and (4) performing point R position information and valid parameter extraction on all the second bounding boxes in the sequence of second bounding box in chronological order to generate a sequence for point R position and heartbeat data classification information.

A method detects presence of a multi-tone siren type in an acoustic signal. The multi-tone siren type is associated with one or more siren patterns, where each siren pattern includes a number of time patterns at corresponding frequencies. The method includes processing a number of frequency components of a frequency domain representation of the acoustic signal over time to determine a corresponding plurality of values. That processing includes determining, for each frequency component, a value characterizing a presence of a time pattern associated with at least one siren pattern. The method also includes processing the values according to the siren patterns to determine a detection result indicating whether the multi-tone siren type is present in the acoustic signal.

An authentication method comprising creating electrocardiogram data of users; calculating a similarity between electrocardiogram data of each user and template data created by averaging electrocardiogram data of each user; creating and training a first NNmodel for every user by using similarities between electrocardiogram data of a user and template data of the same user and similarities between electrocardiogram data of a user and template data of another user, and creating and training second NNmodels for users by using similarities between electrocardiogram data of a user and template data of the user and similarities between electrocardiogram data of the user and template data of another user; and executing a first step in which the similarities calculated using electrocardiogram data for authentication of a user to be authenticated and template data are input to the first NNmodel, and executing a second step in which the similarities are input to the second NNmodels.

According to one embodiment, a condition monitoring device includes a processor. The processor is configured to acquire a time-series signal about a condition of a monitor target from a first sensor, acquire operation timing information indicating start of operation of the monitor target, detect a first operation segment signal from the time-series signal based on the operation timing information, detect a second operation segment signal from the first operation segment signal based on a waveform feature of the first operation segment signal, and determine the condition of the monitor target based on the second operation segment signal.

A method for processing flight data having been recorded during three or more flights of an aircraft by a flight data recorder including obtaining two signature vectors with respective sizes, the two signature vectors corresponding to two different flights among the three or more flights, determining a similarity matrix, the components of which quantify the proximity between the two flight signature vectors, each component allowing identifying, for each element of a first signature, an element of the other signature which is closest, the proximity between two components of the signature vectors being a distance weighted by a mean value of the neighboring components of the similarity matrix, repeating the obtaining and determining in order to compare, two by two, all the flight signatures so as to obtain three or more similarity matrices, and processing said similarity matrices in order to evaluate the similarity between two flights.

The invention relates to a method for a computer device, for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers, and a plate map configuration, wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient, the candidate compound being comprised in a plurality of candidate compounds, said method comprising generating (310) phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the steps obtaining (312) image data depicting each sample comprised in the concentration gradient, generating (314) a class-label and a class for each cell of the samples based on the image data, detecting (320) the optimal candidate compound by evaluating a comparison criterion on the plurality of compound profiles. Furthermore, the invention also relates to corresponding computer device, a computer program, and a computer program product.