Patents by Inventor Daniele ZAMBON

Daniele ZAMBON has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10610162
    Abstract: A heart-rate associated with a heartbeat signal is determined. A transform is selected based on the determined heart-rate associated with the heartbeat signal and a reference heart-rate associated with a dictionary of a sparse approximation model. The transform is selected independent of other factors associated with generation of the heartbeat signal. The selected transform is applied to the dictionary of the sparse approximation model, generating an adjusted dictionary of the sparse approximation model. Anomalous heartbeats in the heartbeat signal are detected using the adjusted dictionary of the sparse approximation model.
    Type: Grant
    Filed: September 5, 2017
    Date of Patent: April 7, 2020
    Assignee: STMICROELECTRONICS S.R.L.
    Inventors: Beatrice Rossi, Pasqualina Fragneto, Diego Carrera, Giacomo Boracchi, Daniele Zambon
  • Patent number: 10595788
    Abstract: A heartrate monitor detects heartbeats in a test signal. A local heartrate and an energy of acceleration are associated with the detected heartbeats. Detected heartbeats are included or excluded from a test set of heartbeats based on the local heartrate and energy of acceleration associated with the respective heartbeats. Anomalous heartbeats in the test set of heartbeats are detected using a sparse approximation model. The heartrate monitor may detect heartbeats in a training heartbeat signal. A reference heart rate and an energy of acceleration are associated with detected beats of the training heartbeat signal and selectively included in a set of training data based on the heart rate and energy of acceleration associated with the detected beat in the training heartbeat signal. A dictionary of the sparse representation model may be generated using the set of training data.
    Type: Grant
    Filed: May 31, 2016
    Date of Patent: March 24, 2020
    Assignee: STMICROELECTRONICS S.R.L.
    Inventors: Beatrice Rossi, Pasqualina Fragneto, Daniele Zambon, Diego Carrera, Giacomo Boracchi
  • Publication number: 20170360377
    Abstract: A heart-rate associated with a heartbeat signal is determined. A transform is selected based on the determined heart-rate associated with the heartbeat signal and a reference heart-rate associated with a dictionary of a sparse approximation model. The transform is selected independent of other factors associated with generation of the heartbeat signal. The selected transform is applied to the dictionary of the sparse approximation model, generating an adjusted dictionary of the sparse approximation model. Anomalous heartbeats in the heartbeat signal are detected using the adjusted dictionary of the sparse approximation model.
    Type: Application
    Filed: September 5, 2017
    Publication date: December 21, 2017
    Inventors: Beatrice ROSSI, Pasqualina FRAGNETO, Diego CARRERA, Giacomo BORACCHI, Daniele ZAMBON
  • Publication number: 20170340292
    Abstract: A heartrate monitor detects heartbeats in a test signal. A local heartrate and an energy of acceleration are associated with the detected heartbeats. Detected heartbeats are included or excluded from a test set of heartbeats based on the local heartrate and energy of acceleration associated with the respective heartbeats. Anomalous heartbeats in the test set of heartbeats are detected using a sparse approximation model. The heartrate monitor may detect heartbeats in a training heartbeat signal. A reference heart rate and an energy of acceleration are associated with detected beats of the training heartbeat signal and selectively included in a set of training data based on the heart rate and energy of acceleration associated with the detected beat in the training heartbeat signal. A dictionary of the sparse representation model may be generated using the set of training data.
    Type: Application
    Filed: May 31, 2016
    Publication date: November 30, 2017
    Inventors: Beatrice ROSSI, Pasqualina FRAGNETO, Daniele ZAMBON, Diego CARRERA, Giacomo BORACCHI