公开(公告)号：US20210000356A1

公开(公告)日：2021-01-07

申请号：US16946718

申请日：2020-07-01

Applicant: Tata Consultancy Services Limited

Inventor： Sanjay Madhukar KIMBAHUNE ,  Sujit Raghunath SHINDE ,  Arpan PAL ,  Sundeep KHANDELWAL ,  Tanuka BHATTACHARJEE ,  Shalini MUKHOPADHAYAY ,  Rohan BANERJEE ,  Avik GHOSE ,  Tapas CHAKRAVARTY

IPC: A61B5/024 ,  A61B5/00 ,  G06N20/00

Abstract: 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.

公开(公告)号：US20240096492A1

公开(公告)日：2024-03-21

申请号：US18367546

申请日：2023-09-13

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Trisrota DEB ,  Ishan SAHU ,  Sai Chander RACHA ,  Sundeep KHANDELWAL ,  Arpan PAL ,  Utpal GARAIN ,  Soumadeep SAHA

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: The present invention relates to the field of evaluating clinical diagnostic models. Conventional metrics does not consider context dependent clinical principles and is unable to capture critically important features that ought to be present in a diagnostic model. Thus, present disclosure provides a method and system for evaluating clinical efficacy of multi-label multi-class computational diagnostic models. Diagnosis for a given dataset of diagnostic samples is obtained from the diagnostic model which is then classified as wrong, missed, over or right diagnosis, based on which a first penalty is calculated. A second penalty is calculated for each diagnostic sample using a contradiction matrix. The first and second penalties are summed up to compute a pre-score for each diagnostic sample. Finally, the diagnostic model is evaluated using a metric that is based on sum of pre-scores, and scores from a perfect and a null multi-label multi-class computational diagnostic model.

公开(公告)号：US20190082988A1

公开(公告)日：2019-03-21

申请号：US15883712

申请日：2018-01-30

Applicant: Tata Consultancy Services Limited

Inventor： Shreyasi DATTA ,  Chetanya PURI ,  Ayan MUKHERJEE ,  Rohan BANERJEE ,  Anirban Dutta CHOUDHURY ,  Arijit UKIL ,  Soma BANDYOPADHYAY ,  Arpan PAL ,  Sundeep KHANDELWAL ,  Rituraj SINGH

IPC: A61B5/04 ,  A61B5/0452 ,  A61B5/00 ,  A61B5/0472 ,  A61B5/046

Abstract: Current technologies analyze electrocardiogram (ECG) signals for a long duration, which is not always a practical scenario. Moreover the current scenarios perform a binary classification between normal and Atrial Fibrillation (AF) only, whereas there are many abnormal rhythms apart from AF. Conventional systems/methods have their own limitations and may tend to misclassify ECG signals, thereby resulting in an unbalanced multi-label classification problem. Embodiments of the present disclosure provide systems and methods that are robust and more efficient for classifying rhythms for example, normal, AF, other abnormal rhythms and noisy ECG recordings by implementing a spectrogram based noise removal that obtains clean ECG signal from an acquired single-lead ECG signal, an optimum feature selection at each layer of classification that selects optimum features from a pool of extracted features, and a multi-layer cascaded binary classifier that identifies rhythms in the clean ECG signal at each layer of the classifier.

公开(公告)号：US20210315511A1

公开(公告)日：2021-10-14

申请号：US17117375

申请日：2020-12-10

Applicant: Tata Consultancy Services Limited

Inventor： Varsha SHARMA ,  Chirayata BHATTACHARYYA ,  Tanuka BHATTACHARJEE ,  Murali PODUVAL ,  Sundeep KHANDELWAL ,  Anirban DUTTA CHOUDHURY

IPC: A61B5/00 ,  G06K9/62 ,  G06K9/00 ,  G06N20/00 ,  G16H50/30 ,  G16H40/67 ,  G16H50/70

Abstract: 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.

公开(公告)号：US20190138688A1

公开(公告)日：2019-05-09

申请号：US16179784

申请日：2018-11-02

Applicant: Tata Consultancy Services Limited

Inventor： Vivek CHANDEL ,  Dhaval Satish JANI ,  Sundeep KHANDELWAL ,  Shalini MUKHOPADHYAY ,  Dibyanshu JAISWAL ,  Avik GHOSE ,  Arpan PAL ,  Kartik MURALIDHARAN

IPC: G06F19/24 ,  G06F19/28 ,  A61B5/024 ,  A61B5/00

Abstract: This disclosure relates generally to classification of cardiopulmonary fatigue. The method and system provides a longitudinal monitoring platform to classify cardiopulmonary fatigue of a subject using a wearable device worn by the subject. The activities of the subject is continuous monitored by plurality of sensors embedded in a wearable device. The received sensor signals are processed in multiple stages to classify cardiopulmonary fatigue as healthy or unhealthy based on respiratory, heart rate and recovery duration parameters extracted from the received sensor data. Further using the classified cardiopulmonary fatigue level, the C2P also performs longitudinal analysis to detect potential cardiopulmonary disorders.

公开(公告)号：US20210290175A1

公开(公告)日：2021-09-23

申请号：US17203578

申请日：2021-03-16

Applicant: Tata Consultancy Services Limited

Inventor： Srinivasan JAYARAMAN ,  Joshin SAHADEVAN ,  Sundeep KHANDELWAL ,  Ponnuraj KIRTHI PRIYA

IPC: A61B5/00 ,  A61B5/0205 ,  G16H50/30 ,  G16H50/20 ,  G16H40/67

Abstract: Continuous monitoring of subject's cardiac system using biological signal(s) (BS) during day-to-day activities is essential for managing personal cardiac health/disorders, etc. Conventional systems/methods lack in improvising overall classification results and configured for specific device/signal say ECG or PPG and so on. Present disclosure provides systems and methods for classifying BS obtained from users, wherein BS are preprocessed to obtain filtered signals (FS). Corresponding feature extraction module is utilized for feature set extraction based on features in FS. The feature set is reduced and segmented into test and training data. Biological signal classification model(s) are generated using training data and a BCM is applied on test data to classify biological signals (BS) as one of Atrial Fibrillation (AF), a non-AF, a cardiac arrythmia disorder, or ischemia. Accelerometer features of connected device associated with the users can be obtained to detect activities which in conjunction with the BCM's output improvises above classification.

公开(公告)号：US20210280319A1

公开(公告)日：2021-09-09

申请号：US17149059

申请日：2021-01-14

Applicant: Tata Consultancy Services Limited

Inventor： Dibyendu ROY ,  Oishee MAZUMDER ,  Aniruddha SINHA ,  Sundeep KHANDELWAL

IPC: G16H50/50 ,  G16H50/70 ,  G16H40/67 ,  G16H70/60 ,  G16H20/30

Abstract: This disclosure provides a simulation platform to study and perform predictive analysis on valvular heart disease, Mitral stenosis (MS) and provides a control approach to correct hemodynamic imbalances during MS conditions. Conventional approaches of valve repair or replacement are often associated with risk of thromboembolism, need for anticoagulation, prosthetic endocarditis, and impaired left ventricle function. The cardiovascular hemodynamics model of the present disclosure helps to create ‘what if’ conditions to study variations in different hemodynamic parameters like blood flow, aortic and ventricular pressure, etc. during normal and pathological conditions. An adaptive control system in conjunction with the hemodynamic cardiovascular system (CVS) is provided to handle hemodynamic disbalance during moderate to severe MS conditions. The adaptive controller is hypothesized in line with the neuromodulation approach and modulates left ventricular contractility and vagal tone to counter the symptoms associated with MS.

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

IPC: G06N3/04 ,  A61B5/11 ,  A61B5/00

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.