Baseline correction and extraction of heartbeat profiles

    公开(公告)号:US11918328B2

    公开(公告)日:2024-03-05

    申请号:US17823083

    申请日:2022-08-30

    CPC classification number: A61B5/02416 A61B5/681

    Abstract: A device may determine end-of-phase information for a plurality of wavelength channels of photoplethysmography (PPG) data. The device may calculate a set of baseline correction points for each wavelength channel of the plurality of wavelength channels. The set of baseline correction points may be calculated based on end-of-phase information for a wavelength channel of the plurality of wavelength channels and PPG data associated with the wavelength channel. The device may perform a baseline correction for each wavelength channel of the plurality of wavelength channels. A baseline correction may be performed for the wavelength channel based on the set of baseline correction points associated with the wavelength channel and the PPG data associated with the wavelength channel. The device may generate a baseline corrected heartbeat profile using a principal component analysis of a result of baseline correcting each wavelength channel of the plurality of wavelength channels.

    Baseline correction and extraction of heartbeat profiles

    公开(公告)号:US11844595B2

    公开(公告)日:2023-12-19

    申请号:US17823083

    申请日:2022-08-30

    CPC classification number: A61B5/02416 A61B5/681

    Abstract: A device may determine end-of-phase information for a plurality of wavelength channels of photoplethysmography (PPG) data. The device may calculate a set of baseline correction points for each wavelength channel of the plurality of wavelength channels. The set of baseline correction points may be calculated based on end-of-phase information for a wavelength channel of the plurality of wavelength channels and PPG data associated with the wavelength channel. The device may perform a baseline correction for each wavelength channel of the plurality of wavelength channels. A baseline correction may be performed for the wavelength channel based on the set of baseline correction points associated with the wavelength channel and the PPG data associated with the wavelength channel. The device may generate a baseline corrected heartbeat profile using a principal component analysis of a result of baseline correcting each wavelength channel of the plurality of wavelength channels.

    Cross-validation based calibration of a spectroscopic model

    公开(公告)号:US11719628B2

    公开(公告)日:2023-08-08

    申请号:US17248867

    申请日:2021-02-11

    CPC classification number: G01N21/274 G01J3/0275 G01N2201/129

    Abstract: A device may receive a master data set for a first spectroscopic model; receive a target data set for a target population associated with the first spectroscopic model to update the first spectroscopic model; generate a training data set that includes the master data set and first data from the target data set; generate a validation data set that includes second data from the target data set and not the master data set; generate, using cross-validation and using the training data set and the validation data set, a second spectroscopic model that is an update of the first spectroscopic model; and provide the second spectroscopic model.

    Focusing linear model correction and linear model correction for multivariate calibration model maintenance

    公开(公告)号:US10969331B2

    公开(公告)日:2021-04-06

    申请号:US16032978

    申请日:2018-07-11

    Abstract: A device may obtain a master beta coefficient of a master calibration model associated with a master instrument. The master beta coefficient may be at a grid of a target instrument. The device may perform constrained optimization of an objective function, in accordance with a set of constraints, in order to determine a pair of transferred beta coefficients. The constrained optimization may be performed based on an initial pair of transferred beta coefficients, the master beta coefficient, and spectra associated with a scouting set. The device may determine, based on the pair of transferred beta coefficients, a transferred beta coefficient. The device may determine a final transferred beta coefficient based on a set of transferred beta coefficients including the transferred beta coefficient. The final transferred beta coefficient may be associated with generating a transferred calibration model, corresponding to the master calibration model, for use by the target instrument.

    Identification using spectroscopy
    28.
    发明授权

    公开(公告)号:US10309894B2

    公开(公告)日:2019-06-04

    申请号:US15247554

    申请日:2016-08-25

    Abstract: A device may receive information identifying results of a spectroscopic measurement of an unknown sample. The device may perform a first classification of the unknown sample based on the results of the spectroscopic measurement and a global classification model. The device may generate a local classification model based on the first classification. The device may perform a second classification of the unknown sample based on the results of the spectroscopic measurement and the local classification model. The device may provide information identifying a class associated with the unknown sample based on performing the second classification.

    ENDPOINT DETECTION IN MANUFACTURING PROCESS BY NEAR INFRARED SPECTROSCOPY AND MACHINE LEARNING TECHNIQUES

    公开(公告)号:US20180322399A1

    公开(公告)日:2018-11-08

    申请号:US15586678

    申请日:2017-05-04

    CPC classification number: G06N5/04 G06N99/005

    Abstract: A device may receive training spectral data associated with a manufacturing process that transitions from an unsteady state to a steady state. The device may generate, based on the training spectral data, a plurality of iterations of a support vector machine (SVM) classification model. The device may determine, based on the plurality of iterations of the SVM classification model, a plurality of predicted transition times associated with the manufacturing process. A predicted transition time, of the plurality of predicted transition times, may identify a time, during the manufacturing process, that a corresponding iteration of the SVM classification model predicts that the manufacturing process transitioned from the unsteady state to the steady state. The device may generate, based on the plurality of predicted transition times, a final SVM classification model associated with determining whether the manufacturing process has reached the steady state.

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