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1. 发明公开

EP3251588A1 METHOD AND SYSTEM FOR PHYSIOLOGICAL PARAMETER DERIVATION FROM PULSATING SIGNALS WITH REDUCED ERROR 审中-公开
标题翻译：具有减少的错误的脉冲信号的生理参数派生方法和系统
公开(公告)号：EP3251588A1
公开(公告)日：2017-12-06
申请号：EP17163197.1
申请日：2017-03-28
申请人： Tata Consultancy Services Limited
发明人： Bandyopadhyay, Soma , Ukil, Arijit , Puri, Chetanya , Singh, Rituraj , Pal, Arpan , Murthy, C A , Mandana, Kayapanda
IPC分类号： A61B5/00 , A61B5/024
CPC分类号： A61B5/024 , A61B5/0002 , A61B5/02108 , A61B5/02405 , A61B5/02416 , A61B5/02438 , A61B5/0245 , A61B5/0402 , A61B5/0456 , A61B5/046 , A61B5/0468 , A61B5/7203
摘要： A method and system for deriving physiological parameters from pulsating signals with reduced error is provided. The method is performed by extracting pulsating signals, removing spurious perturbations in the extracted pulsating signals for smoothening of said extracted pulsating signals, deriving local minima points in the smoothened pulsating signal, deriving systolic maxima point between two derived local minimas, deriving most probable pulse duration and most probable peak-to-peak distance using the derived local minima points and the derived systolic maxima points, deriving dicrotic minima while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, deriving diastolic peak while ensuring that every dicrotic maxima is preceded by a diastolic notch and followed by next beat start point of that maxima and deriving physiological parameters from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.
摘要翻译：提供了一种用于从具有降低的误差的脉动信号中导出生理参数的方法和系统。该方法通过提取脉动信号，去除所提取的脉动信号中的虚假扰动以平滑所提取的脉动信号，导出平滑脉动信号中的局部最小点，导出两个导出的局部最小值之间的收缩极大值点，导出最可能的脉冲持续时间以及使用派生的局部最小点和所得到的收缩极大值点的最可能的峰到峰距离，导出重重最小值，同时确保每个重搏最小值之前都有收缩极大值点并且之后是所述收缩极大值的节拍起点，同时确保每个重搏最大值先于舒张期切迹，然后是该最大值的下一个搏动起始点，并从派生的局部最小值点，收缩最大值点，重搏切迹和舒张峰值推导出生理参数。

2. 发明公开

EP3456246A1 A CASCADED BINARY CLASSIFIER FOR IDENTIFYING RHYTHMS IN A SINGLE-LEAD ELECTROCARDIOGRAM (ECG) SIGNAL 审中-实审
公开(公告)号：EP3456246A1
公开(公告)日：2019-03-20
申请号：EP18153347.2
申请日：2018-01-25
申请人： Tata Consultancy Services Limited
发明人： Datta, Shreyasi , Puri, Chetanya , Mukherjee, Ayan , Banerjee, Rohan , Choudhury, Anirban Dutta , Ukil, Arijit , Bandyopadhyay, Soma , Pal, Arpan , Khandelwal, Sundeep , Singh, Rituraj
IPC分类号： A61B5/00 , A61B5/0452 , A61B5/0456
摘要： 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.

3. 发明公开

EP3312765A1 ANOMALY DETECTION BY SELF-LEARNING OF SENSOR SIGNALS 有权
标题翻译：通过传感器信号的自学习来进行异常检测
公开(公告)号：EP3312765A1
公开(公告)日：2018-04-25
申请号：EP17160491.1
申请日：2017-03-13
申请人： Tata Consultancy Services Limited
发明人： Bandyopadhyay, Soma , UKIL, Arijit , Singh, Rituraj , Puri, Chetanya , Pal, Arpan , Murthy, C A
IPC分类号： G06K9/00 , G06F17/18 , A61B5/0468
CPC分类号： A61B5/7267 , A61B5/021 , A61B5/0468 , A61B5/14551 , G06F17/18 , G06K9/0053 , G06N99/005
摘要： 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.
摘要翻译：准确检测传感器信号中的异常非常重要，并且可能会对医疗保健领域产生巨大影响。因此，识别具有减少的错误和减少的资源使用的异常值或异常是本公开所解决的挑战。输入传感器信号的正常特征的自学习被用于基于传感器信号的谷点和峰值点导出主要特征。通过使用输入传感器信号的离散特性和严格的上升和下降沿来识别模式。基于输入传感器信号的统计特性以及时间和频率域属性，从派生特征中识别一个或多个定义特征。基于定义特征的值，针对输入传感器信号识别变密度的簇，并且基于簇的密度，对输入传感器信号的异常和非异常部分进行分类。

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