Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

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.

Provided are a biosignal visualizing system, which may easily learn a biosignal, may easily make a diagnosis, and may perform analysis in real time, and an effective pattern extracting method using the same, in order to determine a disease using a biosignal via deep learning.

An optical code system for manipulating an optical code that represents an audio waveform present in an audio file, for example, includes a server, a database module, and a non-volatile storage system, all interconnected and preferably in communication with a network to remote users. The system includes two primary sub-systems: a code generating system and a code matching system. The code generating system generates an optical code based on the audio waveform, such an optical code being subsequently scanned and identified by the code matching system. As such, once the code matching system matches an optical code to a particular audio waveform, the associated audio file and associated waveform data may be delivered to the user.

The abnormality detection device includes a first extraction unit extracting an input waveform data from a waveform data input to the first extraction unit, a first determination unit for determining whether the input waveform data includes a detection object waveform data, a second extraction unit for extracting and outputting the detection object waveform data from the input waveform data when the input waveform data is determined to include the detection object waveform data by the first determination unit; and a second determination unit for determining whether the detection target device has an abnormality based on whether the detection object waveform output from the second extraction unit indicates an abnormality.

A pulse wave analysis apparatus including a memory, and a processor coupled to the memory and the processor configured to execute a process, the process including extracting, from each of a plurality of captured images of a subject, a plurality of image areas corresponding to each of a plurality of parts of the subject respectively, generating pieces of waveform data corresponding to the plurality of parts based on an image analysis for the plurality of image areas, each of the pieces of waveform data indicating a pulse wave of the subject, calculating a first matching degree between the pieces of waveform data, and determining whether a noise is included in the pieces of waveform data based on the first matching degree.

A system includes a pattern engine that, in response to selection by a user of a first data set, generates a similarity self-join of the first data set for a specified length. The similarity self-join indicates, for each reference subsequence, a minimum value of distances between the reference subsequence and other subsequences within the first data set. A user portal generates a user interface visually representing the first data set and identifying two subsequences that correspond to the lowest value of the similarity self-join. An alert system receives an alert request specifying a second data set and a pattern specification. An incremental pattern engine generates an initial state of a similarity join of the second data set and the pattern specification. In response to data being received for the first data set, the alert system transmits an alert message if any value of the similarity join meets a threshold.

A method of processing sensor logs is described. The method includes accessing a first sensor log and a corresponding first reference log. Each of the first sensor log and the first reference log includes a series of measured values of a parameter according to a first time series. The method also includes accessing a second sensor log and a corresponding second reference log. Each of the second sensor log and the second reference log includes a series of measured values of a parameter according to a second time series. The method also includes dynamically time warping the first reference log and/or and second reference log by a first transformation between the first time series and a common time-frame and/or a second transformation between the second time series and the common time-frame. The method also includes generating first and second warped sensor logs by applying the or each transformation to the corresponding ones of the first and second sensor logs.

A computer implemented method and system for confirming a device documented arrhythmia in cardiac activity are provided. The method is under control of one or more processors configured with executable instructions. The method obtains a cardiac activity (CA) data set that includes CA signals for a series of cardiac events and includes device documented (DD) markers within the series of cardiac events. The device documented markers are indicative of atrial fibrillation (AF) detected by the ICM utilizing an on-board R-R interval irregularity (ORI) process to analyze the CA signals. The method applies a feature enhancement function to the CA signals to form modified CA signals with enhanced sinus features and analyzes the enhanced sinus features in the modified CA signals. The method utilized a confirmatory feature detection process to identify false AF detection by the ORI process. The method records a result of the analysis identifying false AF detection by the ORI process.

A computer implemented method and system for confirming a device documented arrhythmia in cardiac activity are provided. The method is under control of one or more processors configured with executable instructions. The method obtains a cardiac activity (CA) data set that includes CA signals for a series of cardiac events and includes device documented (DD) markers within the series of cardiac events. The device documented markers are indicative of atrial fibrillation (AF) detected by the ICM utilizing an on-board R-R interval irregularity (ORI) process to analyze the CA signals. The method applies a feature enhancement function to the CA signals to form modified CA signals with enhanced sinus features and analyzes the enhanced sinus features in the modified CA signals. The method utilized a confirmatory feature detection process to identify false AF detection by the ORI process. The method records a result of the analysis identifying false AF detection by the ORI process.