公开(公告)号：US20190278971A1

公开(公告)日：2019-09-12

申请号：US16264786

申请日：2019-02-01

Applicant: Tata Consultancy Services Limited

Inventor： Soma BANDYOPADHYAY ,  Arijit UKIL ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL ,  C A. MURTHY

IPC: G06K9/00 ,  G06K9/62 ,  A61B5/0452

Abstract: The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

公开(公告)号：US20190200935A1

公开(公告)日：2019-07-04

申请号：US16230053

申请日：2018-12-21

Applicant: Tata Consultancy Services Limited

Inventor： Soma BANDYOPADHYAY ,  Arijit UKIL ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL ,  C A. MURTHY

IPC: A61B5/00 ,  G06K9/00

CPC classification number: G16H50/30 ,  G16H50/20 ,  G16H50/70

Abstract: Systems and methods for detecting an anomaly in a cardiovascular signal using hierarchical extremas and repetitions. The traditional systems and methods provide for some anomaly detection in the cardiovascular signal but do not consider the discrete nature and strict rising and falling patterns of the cardiovascular signal and frequency in terms of hierarchical maxima points and minima points. Embodiments of the present disclosure provide for detecting the anomaly in the cardiovascular signal using hierarchical extremas and repetitions by smoothening the cardiovascular signal, deriving sets of hierarchical extremas using window detection, identifying signal patterns based upon the sets of hierarchical extremas, identifying repetitions in the signal patterns based upon occurrences and randomness of occurrences of the signal patterns and classifying the cardiovascular signal as anomalous and non-anomalous for detecting the anomaly in the cardiovascular signal.

公开(公告)号：US20190050690A1

公开(公告)日：2019-02-14

申请号：US15922435

申请日：2018-03-15

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL

IPC: G06K9/62 ,  G06F15/18 ,  G06F17/16 ,  G06F17/18

Abstract: Absence of well-represented training datasets cause a class imbalance problem in one-class support vector machines (OC-SVMs). The present disclosure addresses this challenge by computing optimal hyperparameters of the OC-SVM based on imbalanced training sets wherein one of the class examples outnumbers the other class examples. The hyperparameters kernel co-efficient γ and rejection rate hyperparameter ν of the OC-SVM are optimized to trade-off the maximization of classification performance while maintaining stability thereby ensuring that the optimized hyperparameters are not transient and provide a smooth non-linear decision boundary to reduce misclassification as known in the art. This finds application particularly in clinical decision making such as detecting cardiac abnormality condition under practical conditions of contaminated inputs and scarcity of well-represented training datasets.

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：US20180153419A1

公开(公告)日：2018-06-07

申请号：US15828540

申请日：2017-12-01

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL ,  Debayan MUKHERJEE

IPC: A61B5/024 ,  A61B7/04

CPC classification number: A61B5/02405 ,  A61B5/02444 ,  A61B7/04 ,  A61B34/10 ,  G06F16/7834 ,  G16H50/20 ,  H04R29/00

Abstract: This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.

公开(公告)号：US20170340211A1

公开(公告)日：2017-11-30

申请号：US15467789

申请日：2017-03-23

Applicant: Tata Consultancy Services Limited

Inventor： Soma BANDYOPADHYAY ,  Arijit UKIL ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL ,  C. A. MURTHY ,  Kayapanda MANDANA

IPC: A61B5/024 ,  A61B5/046 ,  A61B5/0245 ,  A61B5/0402 ,  A61B5/00 ,  A61B5/0468 ,  A61B5/0456

CPC classification number: A61B5/024 ,  A61B5/0002 ,  A61B5/02108 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/02438 ,  A61B5/0245 ,  A61B5/0402 ,  A61B5/0456 ,  A61B5/046 ,  A61B5/0468 ,  A61B5/7203

Abstract: This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.