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    Epilepsy seizure detection and prediction using techniques such as deep learning methods
    1.
    发明专利
    Epilepsy seizure detection and prediction using techniques such as deep learning methods 未知

    公开(公告)号：GB2588523A

    公开(公告)日：2021-04-28

    申请号：GB202017338

    申请日：2019-03-28

    Applicant: IBM

    Inventor： BENJAMIN SCOTT MASHFORD ,  FILIZ ISABELL KIRAL-KORNEK ,  ROY SUBHRAJIT ,  JIANBIN TANG ,  STEFAN HARRER

    IPC: G06N3/02 ,  A61B5/369

    Abstract: One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction. The streams maybe from multiple people and applied to an individual person.

    Generating three-dimensional spikes using low-power computing hardware
    5.
    发明专利
    Generating three-dimensional spikes using low-power computing hardware 未知

    公开(公告)号：IL295821A

    公开(公告)日：2022-10-01

    申请号：IL29582122

    申请日：2022-08-22

    Applicant: IBM ,  UMAR ASIF ,  SUBHRAJIT ROY ,  JIANBIN TANG ,  STEFAN HARRER

    Inventor： UMAR ASIF ,  SUBHRAJIT ROY ,  JIANBIN TANG ,  STEFAN HARRER

    IPC: G06F16/23 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20

    Abstract: Aspects described herein include a method of generating three-dimensional (3D) spikes. The method comprises receiving a signal comprising time-series data and generating a first two-dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

    Generating three-dimensional spikes using low-power computing hardware
    7.
    发明专利
    Generating three-dimensional spikes using low-power computing hardware 未知

    公开(公告)号：IL295821B1

    公开(公告)日：2025-05-01

    申请号：IL29582122

    申请日：2022-08-22

    Applicant: IBM CORP ,  UMAR ASIF ,  SUBHRAJIT ROY ,  JIANBIN TANG ,  STEFAN HARRER

    Inventor： UMAR ASIF ,  SUBHRAJIT ROY ,  JIANBIN TANG ,  STEFAN HARRER

    IPC: G06F16/23 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20

    Abstract: Aspects described herein include a method of generating three-dimensional (3D) spikes. The method comprises receiving a signal comprising time-series data and generating a first two-dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

    Generating three-dimensional spikes using low-power computing hardware
    8.
    发明专利
    Generating three-dimensional spikes using low-power computing hardware 未知

    公开(公告)号：GB2609832A

    公开(公告)日：2023-02-15

    申请号：GB202216258

    申请日：2021-03-19

    Applicant: IBM

    Inventor： UMAR ASIF ,  SUBHRAJIT ROY ,  JIANBIN TANG ,  STEFAN HARRER

    IPC: G06Q50/02 ,  G06N7/00 ,  G06N7/01 ,  G06N20/00

    Abstract: A method of generating three-dimensional (3D) spikes. The method comprising receiving a signal comprising time-series data and generating a first two- dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

    Mobile AI
    9.
    发明专利
    Mobile AI 未知

    公开(公告)号：GB2603831A

    公开(公告)日：2022-08-17

    申请号：GB202113292

    申请日：2021-09-17

    Applicant: IBM

    Inventor： UMAR ASIF ,  STEFAN VON CAVALLAR ,  JIANBIN TANG ,  STEFAN HARRER

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A machine learning model can be optimized for deployment on a device based on hardware specifications of the device. An existing model is acquired and pruned to reduce hardware resource consumption of the model. The pruned model is then trained based on training data. The pruned model is also trained based on a collection of “teacher” models. Performance of the trained model is then evaluated and compared to performance requirements, which can be based on the hardware specifications of a device.

    Epilepsy seizure detection and prediction using techniques such as deep learning methods
    10.
    发明专利
    Epilepsy seizure detection and prediction using techniques such as deep learning methods 未知

    公开(公告)号：GB2588523B

    公开(公告)日：2022-03-09

    申请号：GB202017338

    申请日：2019-03-28

    Applicant: IBM

    Inventor： BENJAMIN SCOTT MASHFORD ,  FILIZ ISABELL KIRAL-KORNEK ,  ROY SUBHRAJIT ,  JIANBIN TANG ,  STEFAN HARRER

    IPC: G06N3/02 ,  A61B5/369

    Abstract: One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction. The streams may be from multiple people and applied to an individual person.

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