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1.发明申请公开(公告)号：US20190313920A1
公开(公告)日：2019-10-17
申请号：US16285519
申请日：2019-02-26
Applicant: Tata Consultancy Services Limited
Inventor： Rohan BANERJEE , Sakyajit BHATTACHARYA , Soma BANDYOPADHYAY , Arpan PAL , Kayapanda Muthana MANDANA
Abstract: Non-invasive methods for accurately classifying Coronary Artery Disease (CAD) is a challenging task. In the present disclosure, a two stage classification is performed. In the first stage of classification, a metadata based rule engine is utilized to classify a subject into one of a confirmed CAD subject, a CAD subject and a non-CAD subject. Here, a set of optimal parameters are selected from a set of metadata associated with the subject based on a difference in frequency of occurrence of the CAD among a disease population and a non-disease population. Further, an optimal threshold associated with each optimal parameter is calculated based on an inflexion based correlation analysis. Further, the CAD subject, classified by the metadata based rule engine is further reclassified in a second stage by utilizing a set of cardiovascular signal into one of the CAD subject and the non-CAD subject.
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公开(公告)号：US20210081844A1
公开(公告)日：2021-03-18
申请号：US17025137
申请日：2020-09-18
Applicant: Tata Consultancy Services Limited
Inventor： Sakyajit BHATTACHARYA , Avik GHOSE
Abstract: This disclosure relates generally to categorical time-series clustering. In an embodiment, the method for categorical time-series clustering for categorical time-series associated with distinct subjects obtained from sensors. Based on the categorical time-series, the subjects are clustered into clusters by using a Markov chain model. Clustering the subjects include assigning each subject to a cluster. The subjects are assigned to the clusters by determining cluster-specific transition matrices based on a transitional probability of the subject's transitioning between states. A semi-distance function is constructed for each cluster-specific transitional matrix between the states at multiple time instances, which us indicative of a conditional probability of movement of the subject between the states at different time instance. Using an expectation maximization (EM) model, one or more latent variables of each of the cluster-specific transitional matrices are obtained to determine a likelihood of association of the subject to the cluster.
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公开(公告)号：US20210342641A1
公开(公告)日：2021-11-04
申请号：US17196406
申请日：2021-03-09
Applicant: Tata Consultancy Services Limited
Inventor： Sakyajit BHATTACHARYA , Oishee MUZUMDER , Aniruddha SINHA , Dibyendu ROY , Avik GHOSE
Abstract: State of the art systems and methods attempting to generate synthetic biosignals such as PPG generate patient specific PPG signatures and do not correlate with pathophysiological changes. Embodiments herein provide a method and system for generating synthetic time domain signals to build a classifier. The synthetic signals are generated using statistical explosion. Initially, a parent dataset of actual sample data of class and non-class subjects is identified, and statistical features are extracted. Kernel density estimate (KDE) is used to vary the feature distribution and create multiple data template from a single parent signal. PPG signal is again reconstructed from the distribution pattern using non-parametric techniques. The generated synthetic data set is used to build the two stage cascaded classifier to classify CAD and Non CAD, wherein the classifier design enables reducing bias towards any class.
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公开(公告)号：US20200034690A1
公开(公告)日：2020-01-30
申请号：US16526340
申请日：2019-07-30
Applicant: Tata Consultancy Services Limited
Inventor： Avik GHOSE , Arpan PAL , Sundeep KHANDELWAL , Rohan BANERJEE , Sakyajit BHATTACHARYA , Soma BANDYOPADHYAY , Arijit UKIL , Dhaval Satish JANI
Abstract: This disclosure relates generally to methods and systems for unobtrusive digital health assessment of high risk subjects, wherein bio-markers pertaining to a disease are identified automatically using physical activity and physiology monitoring on a continuous basis. Identification of bio-markers in the medical domain is conventionally dependent on insights derived from medical tests which are obtrusive in nature. Systems and methods of the present disclosure integrate physical characteristics, lifestyle habits and prevailing medical conditions with monitored physical activities and physiological measurements to assess health of high risk subjects. Systems and methods of the present disclosure also enable automatic generation of control class and treatment class that may be effectively used for health assessment.
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公开(公告)号：US20240211815A1
公开(公告)日：2024-06-27
申请号：US18527487
申请日：2023-12-04
Applicant: Tata Consultancy Services Limited
Inventor： Shashee KUMARI , Sakyajit BHATTACHARYA , Avik GHOSE , Arnab CHATTERJEE
IPC: G06N20/20
CPC classification number: G06N20/20
Abstract: Research work in the literature on imputation of mobility data for missing records of a subject's location trajectory has been specifically revolved around usage of historical data. Thus, performances drop when missing records or imputation mobility data for unknown subject with very little or no historical data has to be predicted. A method and system for training an ensemble classifier for imputation of mobility data of unknown subject based on cohort of the unknown subject is disclosed. The method and system disclosed herein exploits the knowledge that semantic trajectories of different individuals has considerable similarity when individuals belong to the same cohort. This concept is used by the method to predict the behavior of all the individuals in a cohort using ensemble classifier, also referred to as imputation model, trained on the semantic location data of a fraction of total individuals in the cohort with a certain accuracy.
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Patent Agency Ranking
公开(公告)号：US20190332950A1
公开(公告)日：2019-10-31
申请号：US16396276
申请日：2019-04-26
Applicant: Tata Consultancy Services Limited
Inventor： Avik GHOSE , Arijit CHOWDHURY , Sakyajit BHATTACHARYA , Vivek CHANDEL , Arpan PAL , Soma BANDYOPADHYAY
IPC: G06N5/02 , G06N5/04 , G06K9/62 , G06F16/908 , G06F16/9035 , A61B5/00
Abstract: This disclosure relates generally to a unified platform for domain adaptable human behaviour inference. The platform provides a unified, low level inference and high level inference of domain adaptable human behaviour inference. The low level inferences include cross-sectional analysis techniques to infer location, activity, physiology. Further the high inference that provide useful and actionable for longitudinal tracking, prediction and anomaly detection is performed based on several longitudinal analysis techniques that include welch analysis, cross-spectrum analysis, Feature of interest (FOI) identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with low level inference, human meta-data and application domain knowledge. Further the unified human behaviour inference can be obtained across multiple domains that include health, retail and transportation.
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公开(公告)号：US20190008475A1
公开(公告)日：2019-01-10
申请号：US15912234
申请日：2018-03-05
Applicant: Tata Consultancy Services Limited
Inventor： Shreyasi DATTA , Anirban DUTTA CHOUDHURY , Parijat DESHPANDE , Sakyajit BHATTACHARYA , Arpan PAL
Abstract: Identification of pulmonary diseases involves accurate auscultation as well as elaborate and expensive pulmonary function tests. Also, there is a dependency on a reference signal from a flowmeter or need for labelled respiratory phases. The present disclosure provides extraction of frequency and time-frequency domain lung sound features such as spectral and spectrogram features respectively that enable classification of healthy and abnormal lung sounds without the dependencies of prior art. Furthermore extraction of wavelet and cepstral features improves accuracy of classification. The lung sound signals are pre-processed prior to feature extraction to eliminate heart sounds and reduce computational requirements while ensuring that information providing adequate discrimination between healthy and abnormal lung sounds is not lost.
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8.发明申请公开(公告)号：US20240366150A1
公开(公告)日：2024-11-07
申请号：US18633767
申请日：2024-04-12
Applicant: Tata Consultancy Services Limited
Inventor： Sakyajit BHATTACHARYA , Dibyendu ROY , Aniruddha SINHA , Avik GHOSE , Varsha SHARMA , Oishee MAZUMDER
Abstract: It is important to monitor the cardiac condition of an individual outside the clinic, using wearable physiological sensors. However, existing methods for calculating the cardiac risk score of an individual are primarily based on static information like individual's metadata, lifestyle, family history, clinical assessment, etc. but do not consider the cardiac state in a daily living scenario using wearable-based measurements. Embodiments herein provide a method and a system for determining post-exercise cardiac score in a recovery period using personalized cardiac model. A clinical decision support system (CDSS) is disclosed to predict cardiac recovery score of a subject in post-exercise conditions. The system employs a hybrid approach using a computational cardiac model and wearable data. Further, several personalized cardiac parameters are simulated using a cardiovascular simulation (CVS) platform. These parameters are used along with the wearable ECG data and meta-data information to derive the post-exercise recovery score.
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9.发明申请公开(公告)号：US20210027895A1
公开(公告)日：2021-01-28
申请号：US16809964
申请日：2020-03-05
Applicant: Tata Consultancy Services Limited
Inventor： Oishee MAZUMDER , Dibyendu ROY , Sakyajit BHATTACHARYA , Aniruddha SINHA , Arpan PAL
Abstract: The disclosure relates to digital twin of cardiovascular system called as cardiovascular model to generate synthetic Photoplethysmogram (PPG) signal pertaining to disease conditions. The conventional methods are stochastic model capable of generating statistically equivalent PPG signals by utilizing shape parameterization and a nonstationary model of PPG signal time evolution. But these technique generates only patient specific PPG signatures and do not correlate with pathophysiological changes. Further, these techniques like most synthetic data generation techniques lack interpretability. The cardiovascular model of the present disclosure is configured to generate the plurality of synthetic PPG signals corresponding to the plurality of disease conditions. The plurality of synthetic PPG signals can be used to tune Machine Learning algorithms. Further, the plurality of synthetic PPG signals can be utilized to understand, analyze and classify cardiovascular disease progression.
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10.发明申请公开(公告)号：US20200151599A1
公开(公告)日：2020-05-14
申请号：US16547380
申请日：2019-08-21
Applicant: Tata Consultancy Services Limited
Inventor： Sounak DEY , Sakyajit BHATTACHARYA , Kaustab PAL , Arijit MUKHERJEE
Abstract: Systems and methods for modelling prediction errors in path-learning of an autonomous learning agent are provided. The traditional systems and methods provide for machine learning techniques, wherein estimation of errors in prediction is reduced with an increase in the number of path-iterations of the autonomous learning agent. Embodiments of the present disclosure provide for a two-stage modelling technique to model the prediction errors in the path-learning of the autonomous learning agent, wherein the two-stage modelling technique comprises extracting a plurality of fitted error values corresponding to a plurality of predicted actions and actual actions by implementing an Autoregressive moving average (ARMA) technique on a set of prediction error values; and estimating, by implementing a linear regression technique on the plurality of fitted error values, a probable deviation of the autonomous learning agent from each of an actual action amongst a plurality of predicted and actual actions.
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