公开(公告)号：US11419542B2

公开(公告)日：2022-08-23

申请号：US16578270

申请日：2019-09-20

Applicant: Tata Consultancy Services Limited

Inventor： Tanuka Bhattacharjee ,  Deepan Das ,  Shahnawaz Alam ,  Rohan Banerjee ,  Anirban Dutta Choudhury ,  Arpan Pal ,  Achuth Rao Melavarige Venkatagiri ,  Prasanta Kumar Ghosh ,  Ayush Ranjan Lohani

IPC: A61B5/00 ,  G06K9/62 ,  G06V10/40 ,  G06V20/69 ,  G06V30/24

Abstract: Monitoring the quality of sleep of an individual is essential for ensuring one's overall well-being. The existing methods for non-apnea sleep arousal detection are manual. A system and method for the non-apnea sleep arousal detection has been provided. The method uses a feature engineering based binary classification approach for distinguishing non-apnea arousal and non-arousal. A training data set is prepared using a plurality of physiological signals. A plurality of features are derived from the training data set. Out of those only a set of features are selected for training a plurality of random forest classifier models. A test sample is then provided to the plurality of random forest classifier models in the instances of fixed duration. This results in generation of prediction probabilities for each instances. The prediction probabilities are then used to predict the probabilities of non-apnea sleep arousal in the test sample.

公开(公告)号：US11045136B2

公开(公告)日：2021-06-29

申请号：US16190800

申请日：2018-11-14

Applicant: Tata Consultancy Services Limited

Inventor： Tanuka Bhattacharjee ,  Shreyasi Datta ,  Deepan Das ,  Anirban Dutta Choudhury ,  Arpan Pal ,  Prasanta Kumar Ghosh

IPC: A61B5/04 ,  A61B5/352 ,  G16H40/63 ,  A61B5/024 ,  A61B5/16 ,  A61B5/316

Abstract: Traditionally arousal classification has been broadly done in multiple classes but have been insufficient to provide information about how arousal level of user changes over time. Present disclosure propose a continuous and unsupervised approach of monitoring the arousal trend of individual from his/her heart rate by obtaining instantaneous HR for time windows from a resampled time series of RR intervals obtained from ECG signal. A measured average heart rate (a measured HR) is computed from instantaneous HR specific to user for each time window thereby estimating apriori state based on a last instance of an aposteriori state initialized and observation of a state space model of Kalman Filter is determined for computing error and normalizing thereof which gets compared with a threshold for continuous monitoring of arousal trend of the user. The aposterior state is further updated using Kalman gain computed based on measurement noise determined for state space model.