BASELINE CORRECTION AND EXTRACTION OF HEARTBEAT PROFILES

    公开(公告)号:US20240074669A1

    公开(公告)日:2024-03-07

    申请号:US18507273

    申请日:2023-11-13

    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:US20200253492A1

    公开(公告)日:2020-08-13

    申请号:US16780406

    申请日:2020-02-03

    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.

    TRANSFER OF A CALIBRATION MODEL USING A SPARSE TRANSFER SET

    公开(公告)号:US20200018648A1

    公开(公告)日:2020-01-16

    申请号:US16582538

    申请日:2019-09-25

    Abstract: A device may obtain a master calibration set, associated with a master calibration model of a master instrument, that includes spectra, associated with a set of samples, generated by the master instrument. The device may identify a selected set of master calibrants based on the master calibration set. The device may obtain a selected set of target calibrants that includes spectra, associated with the subset of the set of samples, generated by the target instrument. The device may create a transfer set based on the selected set of master calibrants and the selected set of target calibrants. The device may create a target calibration set, corresponding to the master calibration set, based on the transfer set. The device may generate, using an optimization technique associated with the transfer set and a support vector regression modeling technique, a transferred calibration model, for the target instrument, based on the target calibration set.

    DYNAMIC PROCESS END POINT DETECTION
    7.
    发明公开

    公开(公告)号:US20230204502A1

    公开(公告)日:2023-06-29

    申请号:US18163712

    申请日:2023-02-02

    CPC classification number: G01N21/3577 G01N21/359 G01N33/15 G01N1/38

    Abstract: A device may receive spectroscopic data associated with a dynamic process. The device may identify a pseudo steady state end point based on the spectroscopic data. The pseudo steady state end point may indicate an end of a pseudo steady state associated with the dynamic process. The device may identify a reference block and a test block based on the pseudo steady state end point, and may generate a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate an averaged statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block, and may determine whether the dynamic process has reached a steady state based on the averaged statistical detection signal.

    CROSS-VALIDATION BASED CALIBRATION OF A SPECTROSCOPIC MODEL

    公开(公告)号:US20210164891A1

    公开(公告)日:2021-06-03

    申请号:US17248867

    申请日:2021-02-11

    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.

    LOCAL AUTO-SCALING CLASSIFICATION OF A SPECTROSCOPIC DATASET

    公开(公告)号:US20250021892A1

    公开(公告)日:2025-01-16

    申请号:US18352072

    申请日:2023-07-13

    Abstract: In some implementations, a device may receive a spectroscopic dataset associated with an unknown sample. The device may obtain a multiclass classification model to be used for classification of the unknown sample into at least one class of a plurality of classes; wherein the multiclass classification model comprises a plurality of local auto-scaled one-versus-one (OVO) binary classifiers, each local auto-scaled OVO binary classifier of the plurality of local auto-scaled OVO binary classifiers being associated with a different pair of classes from the plurality of classes. The device may apply local auto-scaling to the spectroscopic dataset associated with the unknown sample to create a local auto-scaled spectroscopic dataset. The device may perform a classification of the unknown sample based on the local auto-scaled spectroscopic dataset and using the multiclass classification model comprising the plurality of local auto-scaled OVO binary classifiers.

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