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    Systems and methods for labeling large datasets of physiological records based on unsupervised machine learning
    2.
    发明授权
    Systems and methods for labeling large datasets of physiological records based on unsupervised machine learning 有权

    公开(公告)号：US11481578B2

    公开(公告)日：2022-10-25

    申请号：US16796692

    申请日：2020-02-20

    Applicant: NeuroPace, Inc.

    Inventor： Sharanya Arcot Desai ,  Thomas K. Tcheng ,  Benjamin E. Shanahan

    IPC: G06K9/62 ,  G06N3/08 ,  G06F9/30

    Abstract: A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.

    SYSTEMS AND METHODS FOR LABELING LARGE DATASETS OF PHYSIOLOGICAL RECORDS BASED ON UNSUPERVISED MACHINE LEARNING
    4.
    发明申请
    SYSTEMS AND METHODS FOR LABELING LARGE DATASETS OF PHYSIOLOGICAL RECORDS BASED ON UNSUPERVISED MACHINE LEARNING 有权

    公开(公告)号：US20230017756A1

    公开(公告)日：2023-01-19

    申请号：US17948947

    申请日：2022-09-20

    Applicant: NeuroPace, Inc.

    Inventor： Sharanya Arcot Desai ,  Thomas K. Tcheng ,  Benjamin E. Shanahan

    IPC: G06K9/62 ,  G06N3/08 ,  G06F9/30

    Abstract: A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.

    SYSTEMS AND METHODS FOR LABELING LARGE DATASETS OF PHYSIOLOGIAL RECORDS BASED ON UNSUPERVISED MACHINE LEARNING
    5.
    发明申请
    SYSTEMS AND METHODS FOR LABELING LARGE DATASETS OF PHYSIOLOGIAL RECORDS BASED ON UNSUPERVISED MACHINE LEARNING 审中-公开

    公开(公告)号：US20200272857A1

    公开(公告)日：2020-08-27

    申请号：US16796692

    申请日：2020-02-20

    Applicant: NeuroPace, Inc.

    Inventor： Sharanya Arcot Desai ,  Thomas K. Tcheng ,  Benjamin E. Shanahan

    IPC: G06K9/62 ,  G06N3/08 ,  G06F9/30

    Abstract: A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.

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