公开(公告)号：US20200046244A1

公开(公告)日：2020-02-13

申请号：US16534955

申请日：2019-08-07

Applicant: Tata Consultancy Services Limited

Inventor： Shahnawaz ALAM ,  Rohan BANERJEE ,  Soma BANDYOPADHYAY

IPC: A61B5/04 ,  A61B5/00 ,  G06N3/04 ,  A61B5/0452

Abstract: Conventional systems and methods of classifying heart signals include segmenting them which can fail due to the presence of noise, artifacts and other sounds including third heart sound ‘S3’, fourth heart sound ‘S4’, and murmur. Heart sounds are inherently prone to interfering noise (ambient, speech, etc.) and motion artifact, which can overlap time location and frequency spectra of murmur in heart sound. Embodiments of the present disclosure provide parallel implementation of Deep Neural Networks (DNN) for classifying heart sound signals (HSS) wherein spatial (presence of different frequencies component) filters from Spectrogram feature(s) of the HSS are learnt by a first DNN while time-varying component of the signals from MFCC features of the HSS are learnt by a second DNN for classifying the heart sound signal as one of normal sound signal or murmur sound signal.

公开(公告)号：US20150261959A1

公开(公告)日：2015-09-17

申请号：US14627185

申请日：2015-02-20

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Arpan PAL

IPC: G06F21/57 ,  G06F11/30 ,  G06F11/34

CPC classification number: G06F21/577 ,  G06F11/3024 ,  G06F11/3409 ,  G06F21/6254 ,  G06F2221/034

Abstract: System(s) and method(s) to provide privacy measurement and privacy quantification of sensor data are disclosed. The sensor data is received from a sensor. The private content associated with the sensor data is used to calculate a privacy measuring factor by using entropy based information theoretic model. A compensation value with respect to distribution dissimilarity is determined. The compensation value compensates a statistical deviation in the privacy measuring factor. The compensation value and the privacy measuring factor are used to determine a privacy quantification factor. The privacy quantification factor is scaled with respect to a predefined finite scale to obtain at least one scaled privacy quantification factor to provide quantification of privacy of the sensor data.

Abstract translation: 公开了提供传感器数据的隐私测量和隐私定量的系统和方法。 从传感器接收传感器数据。 与传感器数据相关联的私有内容用于通过使用基于熵的信息理论模型来计算隐私测量因子。 确定相对于分布不相似度的补偿值。 补偿值补偿隐私测量因子中的统计偏差。 补偿值和隐私测量因子用于确定隐私量化因子。 隐私量化因子相对于预定义的有限比例被缩放以获得至少一个缩放的隐私量化因子，以提供传感器数据的隐私的量化。

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.

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：US20150261963A1

公开(公告)日：2015-09-17

申请号：US14618280

申请日：2015-02-10

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Arpan PAL

IPC: G06F21/60

CPC classification number: G06F21/6245 ,  G01D4/002 ,  H04L2209/04 ,  H04L2209/42 ,  Y02B90/241 ,  Y04S20/32 ,  Y04S40/24

Abstract: A system and method for detecting sensitivity content in time-series data is disclosed. The method comprises receiving the time-series data from a source. The data is received for one or more instances. The method further comprises detecting the sensitivity content in the time-series data. The sensitivity content indicates presence of an anomaly. The detecting comprises determining a kurtosis value corresponding to the time-series data. The detecting further comprises comparing the kurtosis value with a reference value. The detecting further comprises processing the data using a first filtering means or a second filtering means. The first filtering means is used when the data distribution of the time-series data is either of a platykurtic distribution or a mesokurtic distribution. The second filtering means is used when the data distribution of the time-series data is a leptokurtic distribution.

Abstract translation: 公开了一种用于检测时间序列数据中的灵敏度内容的系统和方法。 该方法包括从源接收时间序列数据。 接收到一个或多个实例的数据。 该方法还包括检测时间序列数据中的灵敏度内容。 灵敏度内容表示存在异常。 该检测包括确定对应于时间序列数据的峰度值。 该检测还包括将峰度值与参考值进行比较。 检测还包括使用第一滤波装置或第二滤波装置处理数据。 当时间序列数据的数据分布是板状分布或间质分布时，使用第一过滤装置。 当时间序列数据的数据分布是leptokurtic分布时，使用第二过滤装置。

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

IPC: A61B5/02 ,  A61B5/00 ,  G16H50/30

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.

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：US20220027711A1

公开(公告)日：2022-01-27

申请号：US17357614

申请日：2021-06-24

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Arpan PAL

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/063

Abstract: This disclosure relates generally to a system and a method for mitigating generalization loss in deep neural network for time series classification. In an embodiment, the disclosed method includes compute an entropy of a timeseries training dataset, and a mean and a variance of the entropy and a regularization factor is computed. A plurality of iterations are performed to dynamically adjust the learning rate of the deep Neural Network (DNN) using a Mod-Adam optimization, and obtain a network parameter, and based on the network parameter, the regularization factor is updated to obtain an updated regularized factor. The learning rate is adjusted in the plurality of iterations by repeatedly updating the network parameter based on a variation of a generalization loss during the plurality of iterations. The updated regularized factor of the current iteration is used for adjusting the learning rate in a subsequent iteration of the plurality of iterations.

公开(公告)号：US20210326765A1

公开(公告)日：2021-10-21

申请号：US17156821

申请日：2021-01-25

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Arpan PAL ,  Soma BANDYOPADHYAY ,  Ishan SAHU ,  Trisrota DEB

IPC: G06N20/10 ,  G06F17/16 ,  G06K9/62

Abstract: This disclosure relates generally to method and system for an adaptive filter based learning model for time series sensor signal classification on edge devices. The adaptive filter based learning model for time series sensor signal classification enables automated-computationally lightweight learning (significant reduction in computational resources) and inferring/classification in real-time or near-real-time on CPU/memory/battery life constrained edge devices. The disclosed techniques for time series sensor signal classification on edge devices characterizes the intrinsic signal processing properties of the input time series sensor signals using linear adaptive filtering and derivative spectrum to efficiently construct the adaptive filter based learning model based on standard classification algorithms for time series sensor signal classification.