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公开(公告)号：US11748658B2
公开(公告)日：2023-09-05
申请号：US17025137
申请日：2020-09-18
发明人： Sakyajit Bhattacharya , Avik Ghose
CPC分类号： G06N20/00 , G06F16/285
摘要： This disclosure relates generally to categorical time-series clustering. In an embodiment, the method for categorical time-series clustering for categorical time-series associated with distinct subjects obtained from sensors. Based on the categorical time-series, the subjects are clustered into clusters by using a Markov chain model. Clustering the subjects include assigning each subject to a cluster. The subjects are assigned to the clusters by determining cluster-specific transition matrices based on a transitional probability of the subject's transitioning between states. A semi-distance function is constructed for each cluster-specific transitional matrix between the states at multiple time instances, which us indicative of a conditional probability of movement of the subject between the states at different time instance. Using an expectation maximization (EM) model, one or more latent variables of each of the cluster-specific transitional matrices are obtained to determine a likelihood of association of the subject to the cluster.
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2.发明申请公开(公告)号：US20220359078A1
公开(公告)日：2022-11-10
申请号：US17684992
申请日：2022-03-02
发明人： Shivam SINGHAL , Nasimuddin Ahmed , Varsha Sharma , Sakyajit Bhattacharya , Aniruddha Sinha , Avik Ghose
摘要： This disclosure relates generally to patient invariant model for freezing of gait detection based on empirical wavelet decomposition. The method receives a motion data from an accelerometer sensor coupled to an ankle of a subject. The motion data is further processed to denoise a plurality of data windows using a peak detection technique to classify into a real motion data window or a noisy data window. Further, a plurality of denoised data windows are generated by processing spectrums associated with each real motion data window and a plurality of empirical modes using an empirical wavelet decomposition technique (EWT). Then, a resultant acceleration is computed, and a plurality of features are extracted from the denoised data window which enables detection of freezing of gait based on a pretrained classifier model into a (i) a positive class, or (ii) a negative class.
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公开(公告)号：US11051741B2
公开(公告)日：2021-07-06
申请号：US16557904
申请日：2019-08-30
摘要： Electrocardiography (ECG) signals contain important markers for Coronary Heart Disease (CHD). State of the art systems and methods rely on clinically available multi-lead ECG for CHD classification which is not cost effective. Moreover the state of the art methods are applied on digital ECG time series data only. Also, discriminative HRV markers are not often present in short ECG recordings necessitating long hours of ECG data to analyze. In accordance with the present disclosure, systems and methods described hereinafter extract ECG time series from ECG images obtained from commercially available low-cost single lead ECG devices through a combination of image and signal processing steps including Histogram analysis, Morphological operation-thinning, Extraction of lines, Extraction of Reference Pulse, Extraction of ECG and interpolating missing data. Further, domain independent statistical features such as self-similarity of raw ECG time series and average Maharaj's distance along with domain specific features are used for classifying CHD.
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公开(公告)号：US11699522B2
公开(公告)日：2023-07-11
申请号：US16396276
申请日：2019-04-26
发明人： Avik Ghose , Arijit Chowdhury , Sakyajit Bhattacharya , Vivek Chandel , Arpan Pal , Soma Bandyopadhyay
IPC分类号： G06N5/02 , G06N5/04 , G06K9/62 , G06F16/9035 , A61B5/00 , G16H40/67 , G06F16/908 , G06N5/022 , G06F18/25
CPC分类号： G16H40/67 , A61B5/7275 , G06F16/908 , G06F16/9035 , G06F18/251 , G06N5/022 , G06N5/04
摘要： This disclosure relates generally to a unified platform for domain adaptable human behaviour inference. The platform provides a unified, low level inference and high level inference of domain adaptable human behaviour inference. The low level inferences include cross-sectional analysis techniques to infer location, activity, physiology. Further the high inference that provide useful and actionable for longitudinal tracking, prediction and anomaly detection is performed based on several longitudinal analysis techniques that include welch analysis, cross-spectrum analysis, Feature of interest (FOI) identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with low level inference, human meta-data and application domain knowledge. Further the unified human behaviour inference can be obtained across multiple domains that include health, retail and transportation.
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5.发明授权公开(公告)号：US11462331B2
公开(公告)日：2022-10-04
申请号：US16809964
申请日：2020-03-05
摘要： The disclosure relates to digital twin of cardiovascular system called as cardiovascular model to generate synthetic Photoplethysmogram (PPG) signal pertaining to disease conditions. The conventional methods are stochastic model capable of generating statistically equivalent PPG signals by utilizing shape parameterization and a nonstationary model of PPG signal time evolution. But these technique generates only patient specific PPG signatures and do not correlate with pathophysiological changes. Further, these techniques like most synthetic data generation techniques lack interpretability. The cardiovascular model of the present disclosure is configured to generate the plurality of synthetic PPG signals corresponding to the plurality of disease conditions. The plurality of synthetic PPG signals can be used to tune Machine Learning algorithms. Further, the plurality of synthetic PPG signals can be utilized to understand, analyze and classify cardiovascular disease progression.
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公开(公告)号：US11213210B2
公开(公告)日：2022-01-04
申请号：US16285519
申请日：2019-02-26
发明人： Rohan Banerjee , Sakyajit Bhattacharya , Soma Bandyopadhyay , Arpan Pal , Kayapanda Muthana Mandana
摘要： 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.
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公开(公告)号：US10966681B2
公开(公告)日：2021-04-06
申请号：US15912234
申请日：2018-03-05
发明人： Shreyasi Datta , Anirban Dutta Choudhury , Parijat Deshpande , Sakyajit Bhattacharya , Arpan Pal
摘要： Identification of pulmonary diseases involves accurate auscultation as well as elaborate and expensive pulmonary function tests. Also, there is a dependency on a reference signal from a flowmeter or need for labelled respiratory phases. The present disclosure provides extraction of frequency and time-frequency domain lung sound features such as spectral and spectrogram features respectively that enable classification of healthy and abnormal lung sounds without the dependencies of prior art. Furthermore extraction of wavelet and cepstral features improves accuracy of classification. The lung sound signals are pre-processed prior to feature extraction to eliminate heart sounds and reduce computational requirements while ensuring that information providing adequate discrimination between healthy and abnormal lung sounds is not lost.
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公开(公告)号：US12106196B2
公开(公告)日：2024-10-01
申请号：US17196406
申请日：2021-03-09
IPC分类号： G06N20/10 , G06F18/2134 , G06F18/214 , G06F18/2321 , G06F18/243 , G16H50/20 , G16H50/70
CPC分类号： G06N20/10 , G06F18/2134 , G06F18/2148 , G06F18/2321 , G06F18/24317 , G16H50/20 , G16H50/70 , G06F2218/14
摘要： State of the art systems and methods attempting to generate synthetic biosignals such as PPG generate patient specific PPG signatures and do not correlate with pathophysiological changes. Embodiments herein provide a method and system for generating synthetic time domain signals to build a classifier. The synthetic signals are generated using statistical explosion. Initially, a parent dataset of actual sample data of class and non-class subjects is identified, and statistical features are extracted. Kernel density estimate (KDE) is used to vary the feature distribution and create multiple data template from a single parent signal. PPG signal is again reconstructed from the distribution pattern using non-parametric techniques. The generated synthetic data set is used to build the two stage cascaded classifier to classify CAD and Non CAD, wherein the classifier design enables reducing bias towards any class.
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公开(公告)号：US11887730B2
公开(公告)日：2024-01-30
申请号：US16526340
申请日：2019-07-30
发明人： Avik Ghose , Arpan Pal , Sundeep Khandelwal , Rohan Banerjee , Sakyajit Bhattacharya , Soma Bandyopadhyay , Arijit Ukil , Dhaval Satish Jani
摘要： 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.
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