公开(公告)号：US10172528B2

公开(公告)日：2019-01-08

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: A61B5/04 ,  A61B5/024 ,  A61B5/00 ,  A61B5/0245 ,  A61B5/0402 ,  A61B5/0456 ,  A61B5/046 ,  A61B5/0468 ,  A61B5/021

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.

公开(公告)号：US11263450B2

公开(公告)日：2022-03-01

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 ,  A61B5/349

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：US11304663B2

公开(公告)日：2022-04-19

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 ,  G16H50/20 ,  G16H50/30 ,  A61B5/318 ,  G16H50/70 ,  A61B5/024

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：US10743821B2

公开(公告)日：2020-08-18

申请号：US15456199

申请日：2017-03-10

Applicant: Tata Consultancy Services Limited

Inventor： Soma Bandyopadhyay ,  Arijit Ukil ,  Rituraj Singh ,  Chetanya Puri ,  Arpan Pal ,  C A Murthy

IPC: G06N3/08 ,  G06F11/22 ,  G06N3/02 ,  A61B5/00 ,  A61B5/021 ,  G06N20/00 ,  G06F17/18 ,  G06K9/00 ,  A61B5/0468 ,  A61B5/1455

Abstract: Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.