公开(公告)号：EP3389474A1

公开(公告)日：2018-10-24

申请号：EP16823381.5

申请日：2016-12-08

申请人： Microsoft Technology Licensing, LLC

发明人： KANNAN, Aadharsh ,  RAMASWAMY, Govind ,  GUJJAR, Avinash ,  BHASKAR, Srinivas

IPC分类号： A61B5/00 ,  A61B5/024 ,  A61B5/18 ,  B60K28/06 ,  G08B21/06 ,  B60W40/08

CPC分类号： A61B5/7282 ,  A61B5/0024 ,  A61B5/02405 ,  A61B5/16 ,  A61B5/18 ,  A61B5/6801 ,  A61B5/681 ,  A61B5/6898 ,  A61B5/7257 ,  A61B5/726 ,  A61B5/7264 ,  A61B5/7267 ,  A61B5/7405 ,  A61B5/742 ,  A61B5/746 ,  B60K28/06 ,  B60W2040/0827 ,  G08B21/06

摘要： Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

公开(公告)号：EP3018477B1

公开(公告)日：2018-10-24

申请号：EP14820208.8

申请日：2014-06-16

申请人： PHC Holdings Corporation

发明人： KAWAMOTO, Toru ,  HYOHGO, Jun'ichi ,  OHSORA, Takeshi

IPC分类号： A61B5/00 ,  A61B5/087 ,  A61B5/091 ,  A61B5/097 ,  F04B45/047 ,  G01N33/497

CPC分类号： A61B5/087 ,  A61B5/091 ,  A61B5/097 ,  A61B5/7267 ,  A61B2560/02 ,  F04B45/047 ,  G01N33/497

摘要： The exhalation measurement device of the present invention comprises a chamber (23), a measurement component (45), a piezoelectric pump (44), a first learning controller (100), and a second learning controller (110). The chamber (23) temporarily holds exhalation. The measurement component (45) measures a specific component in the exhalation. The piezoelectric pump (44) supplies the measurement component (45) with the exhalation held in the chamber (23). The first learning controller (100) performs operational setting on the piezoelectric pump (44) before the piezoelectric pump (44) supplies the exhalation in the chamber (23) to the measurement component (44). The second learning controller (110) performs operational setting on the piezoelectric pump (44) after the piezoelectric pump (44) has started supplying the exhalation in the chamber (23) to the measurement component (45), but before the measurement component (45) performs its measurement.

公开(公告)号：EP3372154A1

公开(公告)日：2018-09-12

申请号：EP15907809.6

申请日：2015-11-05

申请人： Fujitsu Limited

发明人： WASHIZAWA, Shiho ,  MAEDA, Kazuho ,  UCHIDA, Daisuke ,  MORI, Tatsuya ,  INOMATA, Akihiro

IPC分类号： A61B5/0245

CPC分类号： A61B5/0245 ,  A61B5/02438 ,  A61B5/4866 ,  A61B5/7267 ,  A61B5/7282 ,  G16H20/60

摘要： An information processing device includes: a heart rate acquisition unit configured to acquire a heart rate of an object; a feature extraction unit configured to extract a feature from heart rate fluctuation with respect to time that is acquired by the heart rate acquisition unit; a storage configured to, in advance, store information associated with heart rate fluctuation of a drink intake; and a determination unit configured to determine whether the heart rate fluctuation acquired by the heart rate acquisition unit is caused by a drink intake, on a basis of the feature extracted by the feature extraction unit and the information stored in the storage.

公开(公告)号：EP2688479B1

公开(公告)日：2018-08-29

申请号：EP12760500.4

申请日：2012-03-19

申请人： Carestream Dental Technology Topco Limited

发明人： WANG, Wei ,  SONG, Liwei ,  WU, Yingqian ,  WONG, Victor, C. ,  YAN, Jiayong

IPC分类号： G06T7/00 ,  A61B6/14 ,  A61B5/00 ,  A61C9/00 ,  G01J3/50 ,  A61B6/00

CPC分类号： G06T7/0012 ,  A61B5/0071 ,  A61B5/0088 ,  A61B5/4547 ,  A61B5/7267 ,  A61B6/14 ,  A61B6/5217 ,  A61C9/0046 ,  G01J3/508 ,  G06K2209/05 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/30036

摘要： A method for intraoral imaging obtains a digital image of one or more teeth and detects first and second boundaries. At each of the first and second boundaries, there is calculated a boundary ratio of mean gray-scale values for the tooth area on one side of the boundary to mean gray-scale values for background areas on the other side. The calculated boundary ratios are stored. A third ratio of the mean gray-scale values for the tooth area near the first boundary to the mean gray-scale values for the tooth area near the second boundary is calculated and stored. A vector is formed and stored that contains at least the calculated boundary ratios and the third ratio. The tooth surface is classified as either smooth or occlusal according to the stored vector. The image data is processed according to the classification and processing results are reported.

公开(公告)号：EP3363351A1

公开(公告)日：2018-08-22

申请号：EP18155457.7

申请日：2018-02-07

申请人： Tata Consultancy Services Limited

发明人： BANERJEE, Rohan ,  CHOUDHURY, Anirban Dutta ,  PAL, Arpan ,  DESHPANDE, Parijat Dilip ,  MANDANA, Kayapanda Muthana ,  VEMPADA, Ramu Reddy

IPC分类号： A61B5/00 ,  A61B5/0205 ,  A61B5/0402 ,  G16H50/20

CPC分类号： A61B5/7267 ,  A61B5/0205 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/0402 ,  A61B5/0452 ,  A61B5/0533 ,  A61B5/7203 ,  A61B7/04 ,  G06N7/005 ,  G06N99/005 ,  G16H50/20

摘要： A method and system for detection of coronary artery disease (CAD) in a person using a fusion approach has been described. The invention the detection of CAD in the person by capturing of a plurality of physiological signals such as phonocardiogram (PCG), photoplethysmograph (PPG), ECG, galvanic skin response (GSR) etc. from the person. A plurality of features are extracted from the physiological signals. The person is then classified as CAD or normal using the each of the features independently. The classification is done based on supervised machine learning technique. The output of the classification is then fused and used for the detection of the CAD in the person using a predefined criteria.

公开(公告)号：EP3362984A1

公开(公告)日：2018-08-22

申请号：EP16790780.7

申请日：2016-10-12

申请人： Elekta, Inc.

发明人： HAN, Xiao

IPC分类号： G06T3/00 ,  G06T5/00 ,  G06T11/00

CPC分类号： A61B5/7267 ,  A61B5/0035 ,  A61B5/055 ,  A61B5/7278 ,  A61B2090/3762 ,  A61B2576/00 ,  A61N5/1039 ,  G01R33/5608 ,  G06T5/007 ,  G06T11/008 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G16H30/20 ,  G16H30/40 ,  G16H50/50

摘要： Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.

公开(公告)号：EP3359032A1

公开(公告)日：2018-08-15

申请号：EP16854395.7

申请日：2016-10-07

申请人： Brain Sentinel, Inc.

发明人： WHITMIRE, Luke, E. ,  GIROUARD, Michael, R. ,  CAVAZOS, Jose, E.

IPC分类号： A61B5/04

CPC分类号： A61B5/04 ,  A61B5/0488 ,  A61B5/4094 ,  A61B5/726 ,  A61B5/7267

摘要： A method of monitoring a patient for seizure activity may include monitoring the patient by collecting an electromyography signal, determining features of the signal using wavelet analysis and inputting determined feature values into a neural network trained to detect seizure activity. Related apparatuses are also described.

公开(公告)号：EP3338628A1

公开(公告)日：2018-06-27

申请号：EP18155073.2

申请日：2014-01-24

申请人： Dalhousie University

发明人： Sapp, Jr., John Lewis ,  Horacek, Bohumil Milan

IPC分类号： A61B5/0402 ,  A61B5/00 ,  G06F19/00 ,  A61B5/0408 ,  A61B5/06 ,  A61B5/0472

CPC分类号： A61B5/04525 ,  A61B5/04012 ,  A61B5/0408 ,  A61B5/04085 ,  A61B5/044 ,  A61B5/046 ,  A61B5/0464 ,  A61B5/0472 ,  A61B5/061 ,  A61B5/7246 ,  A61B5/7267

摘要： A method for quantifying, during pacemapping, a comparison of a BSPM of interest to a pace site BSPM. The method may include receiving at a computing device a plurality of ECG signals from an acquisition system. The pace site BSPM may be calculated using the plurality of ECG signals. The BSPM of interest may be compared to the pace site BSPM, by: retrieving the BSPM of interest from memory accessible by the computing device; and, calculating one or more comparison metrics for the BSPM of interest as compared to the pace site BSPM. An indication of similarity between the BSPM of interest and the pace site BSPM based on the comparison metric calculated may be displayed on a user interface in communication with the computing device.

公开(公告)号：EP3320840A1

公开(公告)日：2018-05-16

申请号：EP17201120.7

申请日：2017-11-10

申请人： Tata Consultancy Services Limited

发明人： RAKSHIT, Raj ,  SINHARAY, Arjiit ,  DESHPANDE, Parijat Dilip ,  CHAKRAVARTY, Tapas ,  PAL, Arpan ,  GHOSH, Debkumar ,  DATTA, Shreyasi

IPC分类号： A61B5/087 ,  A61B5/08 ,  A61B5/091 ,  A61B5/097 ,  A61B5/00

CPC分类号： A61B5/097 ,  A61B5/0004 ,  A61B5/0803 ,  A61B5/087 ,  A61B5/091 ,  A61B5/7203 ,  A61B5/7242 ,  A61B5/725 ,  A61B5/7267 ,  A61B5/7275 ,  A61B5/7278 ,  A61B2562/0204

摘要： A pulmonary health monitoring system aims at assessing pulmonary health of subjects. Conventional techniques used for pulmonary health monitoring are not convenient to the subjects and needs considerable cooperation from the subjects. But, there is a challenge in utilizing the conventional devices to the subjects not capable of providing considerable cooperation. The present disclosure includes a blow device applicable to all kind of subjects and doesn't need cooperation from the subjects. Further, in the present disclosure, the blow device generates a phase shifted signal corresponding to a breathe signal and the phase shifted signal is further processed to extract a set of physiological features. Further, pulmonary health of a subject is analyzed by processing the set of physiological features based on a ridge regression based machine learning technique.

摘要翻译： 肺部健康监测系统旨在评估受试者的肺部健康状况。 用于肺部健康监测的常规技术对于受试者不方便并且需要来自受试者的相当大的合作。 但是，将常规设备用于不能提供可观合作的主题是一个挑战。 本公开内容包括适用于所有种类主题的吹塑装置，并且不需要来自主题的合作。 此外，在本公开中，吹气装置生成对应于呼吸信号的相移信号，并且相移信号被进一步处理以提取一组生理特征。 此外，通过基于基于脊线回归的机器学习技术处理该组生理特征来分析对象的肺健康。

公开(公告)号：EP3313275A1

公开(公告)日：2018-05-02

申请号：EP16813834.5

申请日：2016-06-24

申请人： Analytics For Life

发明人： Ramchandani, Shyamlal ,  Gupta, Sunny ,  Burton, Timothy

IPC分类号： A61B5/02 ,  A61B90/00 ,  G06F15/18 ,  G06F19/00

CPC分类号： G16H50/20 ,  A61B5/02 ,  A61B5/04 ,  A61B5/04011 ,  A61B5/04012 ,  A61B5/042 ,  A61B5/4848 ,  A61B5/6801 ,  A61B5/681 ,  A61B5/6823 ,  A61B5/6824 ,  A61B5/7203 ,  A61B5/725 ,  A61B5/7257 ,  A61B5/726 ,  A61B5/7264 ,  A61B5/7267 ,  A61B5/7275 ,  A61B5/7278 ,  G06F19/00

摘要： Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.