Tata Consultancy Services Limited

Sundeep Khandelwal

Avik Ghose

Arpan Pal

Rohan Banerjee

Soma Bandyopadhyay

Arijit Ukil

Anirban Dutta Choudhury

Vivek Chandel

Tanuka Bhattacharjee

Dhaval Satish Jani

Tapas Chakravarty

Aniruddha Sinha

Sanjay Madhukar Kimbahune

Sakyajit Bhattacharya

Chetanya Puri

Shreyasi Datta

Oishee Mazumder

Dibyanshu Jaiswal

Kartik Muralidharan

Dibyendu Roy

Srinivasan Jayaraman

Rituraj Singh

Sujit Shinde

Joshin Sahadevan

Karan Bhavsar

Nasimuddin Ahmed

Ayan Mukherjee

Murali Poduval

Shalini Mukhopadhyay

Harsh Vishwakarma

Varsha Sharma

Bhaskar Pawar

Shivam Singhal

Chirayata Bhattacharyya

Shalini Mukhopadhayay

Ponnuraj Kirthi Priya

Avijit Samal

Sujit Raghunath Shinde

Srinivsan Jayaraman

Arijt Arijt

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

Non-communicable diseases (NCDs) are the pandemics of modern era and are generating huge impact in the modern society. Conventional methods are inaccurate due to a challenge in handling data from heterogenous sensors. The present disclosure is capable of tracking fitness parameters of a user even with heterogenous sensors. Initially, the system receives a raw data from a plurality of heterogenous sensors associated with the user. The raw data is further transformed into a metadata format associated with the corresponding sensor. The transformed data is temporally aligned based on a time based slotting. An algorithm pipeline corresponding to a disorder to be analyzed is selected from a Directed Acyclic Graph (DAG) based on a sensor metadata and a plurality of algorithm metadata corresponding to a plurality of algorithms stored in an algorithm database and an algorithm pipeline. The corresponding disorder is analyzed using the algorithm pipeline.

Method and system for digital biomarkers platform

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.

Systems and methods for atrial fibrillation (AF) and cardiac disorders detection from biological signals

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.

Systems and methods for unobtrusive digital health assessment

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

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.

Neuromodulation based adaptive controller for mitral stenosis

Conventionally, Atrial Fibrillation (AF) has been detected using atrial analyses which is vulnerable to background noise. Again there is a dependency on statistical features which are extracted from R-R intervals of long ECG recordings. The present disclosure addresses AF detection from single lead short ECG recordings of less than one minute wherein automatic detection of P-R and P-Q intervals is difficult, which introduces error in feature computing from the segregated intervals and compromises the performance of the classifier. In the present disclosure, a Recurrent Neural Network (RNN) based architecture comprising two Long Short Term Memory (LSTM) networks is provided for temporal analysis of R-R intervals and P wave regions in an ECG signal respectively. Output sates of the two LSTM networks are merged at a dense layer along with a set of hand-crafted statistical features to create a composite feature set for classification of the AF.

Recurrent neural network architecture based classification of atrial fibrillation using single lead ECG

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.

System and method to classify cardiopulmonary fatigue

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.

Cascaded binary classifier for identifying rhythms in a single-lead electrocardiogram (ECG) signal

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Noida, India

Sundeep Khandelwal