Patents by Inventor Sushmita PAUL

Sushmita PAUL 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).

  • Method and system for cuffless blood pressure estimation using photoplethysmogram features and pulse transit time
    Patent number: 10980429
    Abstract: A method and system for blood pressure (BP) estimation of a person is provided. The system is estimating pulse transit time (PTT) using the ECG signal and PPG signal of the person. A plurality of features are extracted from the PPG. The plurality of PPG features and the PTT are provided as inputs to an automated feature selection algorithm. This algorithm selects a set of features suitable for BP estimation. The selected features are fed to a classifier to classify the database into low/normal BP range and a high BP range. The correctly classified normal BP data are then used to create a regression model to predict BP from the selected features. The current methodology uses automated feature selection mechanism and also employs a block to reject extreme BP data. Thus the available accuracy in predicting BP is expected to be more than the existing BP estimation methods.
    Type: Grant
    Filed: February 21, 2018
    Date of Patent: April 20, 2021
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Sushmita Paul, Anirban Dutta Choudhury, Shreyasi Datta, Arpan Pal, Rohan Banerjee, Kayapanda Mandana
  • METHOD AND SYSTEM FOR CUFFLESS BLOOD PRESSURE ESTIMATION USING PHOTOPLETHYSMOGRAM FEATURES AND PULSE TRANSIT TIME
    Publication number: 20180235487
    Abstract: A method and system for blood pressure (BP) estimation of a person is provided. The system is estimating pulse transit time (PTT) using the ECG signal and PPG signal of the person. A plurality of features are extracted from the PPG. The plurality of PPG features and the PTT are provided as inputs to an automated feature selection algorithm. This algorithm selects a set of features suitable for BP estimation. The selected features are fed to a classifier to classify the database into low/normal BP range and a high BP range. The correctly classified normal BP data are then used to create a regression model to predict BP from the selected features. The current methodology uses automated feature selection mechanism and also employs a block to reject extreme BP data. Thus the available accuracy in predicting BP is expected to be more than the existing BP estimation methods.
    Type: Application
    Filed: February 21, 2018
    Publication date: August 23, 2018
    Applicant: Tata Consultancy Services Limited
    Inventors: Sushmita PAUL, Anirban Dutta CHOUDHURY, Shreyasi DATTA, Arpan PAL, Rohan BANERJEE, Kayapanda MANDANA