Tata Consultancy Services Limited

Tanuka Bhattacharjee

Arpan Pal

Anirban Dutta Choudhury

Deepan Das

Shreyasi Datta

Rohan Banerjee

Sundeep Khandelwal

Prasanta Kumar Ghosh

Tapas Chakravarty

Avik Ghose

Sanjay Madhukar Kimbahune

Sujit Shinde

Murali Poduval

Shahnawaz Alam

Pratyusha Das

Varsha Sharma

Achuth Rao Melavarige Venkatagiri

Ayush Ranjan Lohani

Chirayata Bhattacharyya

Shalini Mukhopadhayay

Anurban Dutta Choudhury

Embodiments herein provide a system and method for screening and monitoring of cardiac diseases by analyzing acquired physiological signals. Unlike state of art approaches that consider only synchronized ECG and PPG signals for cardiac health analysis and do not consider PCG which is a critical signal for CAD analysis, the system synchronously captures physiological signals such as photo plethysmograph (PPG), phonocardiogram (PCG) and electrocardiogram (ECG) from subject(s) and builds an analytical model in the cloud for analyzing heart conditions from the captured physiological signals. The system and method provides a fusion based approach of combining the captured physiological signals such as PPG, PCG and ECG along with other details such as subject clinical information, demography information and so on. The analytical model is pretrained using ECG. PPG and PCG along with metadata associated with the subject such as demography and clinical information.

Method and system for screening and monitoring of cardiac diseases

Sepsis is one of the most prevalent causes of mortality in Intensive Care Units (ICUs) and delayed treatment is associated with increase in death and financial burden. There is no single laboratory test or clinical sign that by itself can be considered diagnostic of sepsis. The present disclosure provides discriminating domain specific continuous and categorical features that can reliably classify a subject being monitored into a sepsis class or a normal class. A combination of physiological parameters, laboratory parameters and demographic details are used to extract the discriminating features. Even though the parameters may be sporadic in nature, the systems and methods of the present disclosure make use of a sliding time window to generate continuous features that capture the trend in the sporadic data; and a binning approach to generate categorical features to discriminate deviation from the normal class and facilitate timely treatment.

Discriminating features based sepsis prediction

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.

System and method for non-apnea sleep arousal detection

A method and system for clustering users using cognitive stress report for classifying stress levels is provided. Detection and monitoring of cognitive stress experienced by users while performing a task is very crucial. The method includes receiving, user evaluated cognitive stress reports and the physiological signals of the user during the performance of the task. A normalized cognitive report is generated from the user evaluated cognitive stress report by computing mode and range value. The normalized cognitive stress reports of the users are used to cluster the users into a primary cluster and a secondary cluster. Feature sets are extracted from the physiological signals of the said users associated with the primary cluster. Using the said feature sets a classifier model is trained to classify the cognitive stress levels of the users as stressful class or stressless class.

Method and system for clustering users using cognitive stress report for classifying stress levels

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) 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.

Heart rate driven unsupervised techniques for continuous monitoring of arousal trend of users

This disclosure relates generally to stress classification and quantification, and more particularly to system and method for classification and quantitative estimation of cognitive stress from analysis of keystrokes and signals derived from physiological sensors. In one embodiment, a method includes obtaining, while a user is engaged in performance of a task, physiological signals from physiological sensors accessible to the user. Feature sets are identified from the physiological signals which correlate with cognitive stress experienced by the user. Using a regression model, a stress indicator metric comprising a quantitative estimate of the cognitive stress is predicted. The regression model is trained using the feature sets and independently determined quantitative estimates of cognitive stress used as a ground truth to output the value of the stress indicator metric. The ground truth is determined from keystroke data associated with the performance of keyboard-based tasks comprising navigation of moving objects to the target.

System and method for classification and quantitative estimation of cognitive stress

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Kolkata, India

Tanuka Bhattacharjee