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1. 发明授权

US08290575B2 Biomedical signal morphology analysis method 有权
标题翻译：生物医学信号形态分析方法
公开(公告)号：US08290575B2
公开(公告)日：2012-10-16
申请号：US12517136
申请日：2007-11-30
申请人： Lionel Tarassenko , Alan Patterson , Iain Guy Strachan
发明人： Lionel Tarassenko , Alan Patterson , Iain Guy Strachan
IPC分类号： A61B5/04
CPC分类号： A61B5/0452 , A61B5/0468 , A61B5/0476 , A61B5/1455 , A61B5/7267
摘要： A way of quantifying the shape of an ECG waveform is disclosed by detecting the JT segment using two Hidden Markov Models and calculating the analytic signal of the JT segment. Parameters calculated from the analytic signal are used as shape descriptors for the JT segment. The shape descriptors may be displayed in a dimensionality-reduced mapping. Templates representing characteristic shapes can be produced by finding cluster centers in the shape descriptor space, and the novelty of new waveforms can be quantified by comparing the position in shape descriptor space of new shape descriptors to a predefined normal training set or to previously encountered waveforms. Novel shape descriptors can be used to retrieve the corresponding waveforms, and templates of such novel shapes can be created by averaging such waveforms, using dynamic time warping to allow for variations in heart rate. The templates can be manually segmented and the manual segmentation propagated back into other waveforms having similar shape descriptors.
摘要翻译：通过使用两个隐马尔可夫模型检测JT段并计算JT段的分析信号来公开量化ECG波形形状的方法。从分析信号计算的参数用作JT段的形状描述符。形状描述符可以被显示在维度降低的映射中。可以通过在形状描述符空间中发现聚类中心来产生表征特征形状的模板，并且可以通过将新形状描述符的形状描述符空间中的位置与预定义的正常训练集或先前遇到的波形进行比较来量化新波形的新颖性。可以使用新颖的形状描述符来检索相应的波形，并且可以通过使用动态时间扭曲来平均这样的波形来创建这种新形状的模板，以允许心率的变化。模板可以手动分割，手动分割传播回具有类似形状描述符的其他波形。

2. 发明授权

US07941209B2 Signal analysis method 有权
标题翻译：信号分析方法
公开(公告)号：US07941209B2
公开(公告)日：2011-05-10
申请号：US11579834
申请日：2005-05-06
申请人： Nicholas Hughes , Lionel Tarassenko , Stephen Roberts
发明人： Nicholas Hughes , Lionel Tarassenko , Stephen Roberts
IPC分类号： A61B5/044 , A61B5/0476 , A61B5/0488
CPC分类号： A61B5/0452 , A61B5/7221 , A61B5/726 , A61B5/7264 , A61B5/7267
摘要： Improvement in the reliability of segmentation of a signal, such as an ECG signal, is achieved through the use of duration constraints. The signal is analysed using a hidden Markov model. The duration constraints specify minimum allowed durations for specific states of the model. The duration constraints can be incorporated either in the model itself or in a Viterbi algorithm used to compute the most probable state sequence given a conventional model. The derivation of a confidence measure from the model can be used to assess the quality and robustness of the segmentation and to identify any signals for which the segmentation is unreliable, for example due to the presence of noise or abnormality in the signal.
摘要翻译：通过使用持续时间约束来实现诸如ECG信号之类的信号的分割的可靠性的改进。使用隐马尔可夫模型分析信号。持续时间约束指定模型的特定状态的最小允许持续时间。持续时间约束可以并入模型本身，也可以并入用于计算给定传统模型的最可能的状态序列的维特比算法。可以使用来自模型的置信度量的推导来评估分割的质量和鲁棒性，并且识别分割不可靠的任何信号，例如由于信号中存在噪声或异常。

3. 发明授权

US08332017B2 Method of biomedical signal analysis including improved automatic segmentation 有权
标题翻译：生物医学信号分析方法包括改进的自动分割
公开(公告)号：US08332017B2
公开(公告)日：2012-12-11
申请号：US12517127
申请日：2007-11-30
申请人： Lionel Tarassenko , Alan Patterson , Iain Guy Strachan
发明人： Lionel Tarassenko , Alan Patterson , Iain Guy Strachan
IPC分类号： A61B5/04
CPC分类号： A61B5/0452 , A61B5/7203 , A61B5/7267
摘要： A method of analysing biomedical signals, for example electrocardiograms, by using a Hidden Markov Model for subsections of the signal. In the case of an electrocardiogram two Hidden Markov Models are used to detect respectively the start and end of the QT interval. The relationship between the QT interval and heart rate can be computed and a contemporaneous value for the slope of this relationship can be obtained by calculating the QT/RR relationship for all of the beats in a sliding time window based on the current beat. Portions of electrocardiograms taken on different days can efficiently and accurately be compared by selecting time windows of the ECGs at the same time of day, and looking for similar beats in those time windows.
摘要翻译：通过使用隐马尔科夫模型来分析生物医学信号（例如心电图）来分析信号的子部分的方法。在心电图的情况下，使用两个隐马尔可夫模型分别检测QT间隔的开始和结束。可以计算QT间隔和心率之间的关系，并且可以通过基于当前节拍计算滑动时间窗口中的所有节拍的QT / RR关系来获得该关系的斜率的同期值。通过在一天的同一时间选择ECG的时间窗口，并在这些时间窗口中寻找类似的节拍，可以有效和准确地比较在不同日期拍摄的部分心电图。

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