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    • 2. 发明公开
    • ANOMALY DETECTION BY SELF-LEARNING OF SENSOR SIGNALS
    • 通过传感器信号的自学习来进行异常检测
    • EP3312765A1
    • 2018-04-25
    • EP17160491.1
    • 2017-03-13
    • Tata Consultancy Services Limited
    • Bandyopadhyay, SomaUKIL, ArijitSingh, RiturajPuri, ChetanyaPal, ArpanMurthy, C A
    • G06K9/00G06F17/18A61B5/0468
    • A61B5/7267A61B5/021A61B5/0468A61B5/14551G06F17/18G06K9/0053G06N99/005
    • 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.
    • 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。
    • 6. 发明公开
    • A METHOD AND A MONITORING DEVICE FOR ANALYZING ECG DATA BY COMPARING CONSECUTIVE TIME INTERVALS
    • 方法和分析的心电数据,通过比较连续的时间间隔一个ÜBERWACHUNGSGEÄT
    • EP3155964A1
    • 2017-04-19
    • EP16188684.1
    • 2016-09-14
    • BIOTRONIK SE & Co. KG
    • Bellock, StevenWhittington, R. HollisGarner, Garth
    • A61B5/0468
    • A61B5/0468A61B5/04012A61B5/0422A61B5/0432
    • The disclosure describes a method for analyzing ECG data (200), comprising the steps of:
      - detecting points in the ECG data which represent ventricular activity (101),
      - measuring time intervals (210, 220, 211, 221, 212, 222) between each two consecutive points in the ECG data which represent ventricular activity,
      - evaluating the time intervals within a set of time intervals by
      - computing at least one comparative dimension for at least one time interval subset, wherein the time interval subset comprises at least two time intervals, wherein the comparative dimension represents a difference of the interval length between the time intervals of the time interval subset, wherein a fixed number N of time intervals lies between each two time intervals of the time interval subset and wherein N ≥ 1.
    • 本公开描述了用于分析ECG数据包括以下步骤的方法(200): - 在ECG数据中检测点代表心室活动(101) - 测量时间间隔(210,220,211,221,212,222) 计算用于至少一个时间间隔的子集的至少一个比较尺寸,worin的时间间隔的子集包括至少两个 - 通过评估一组时间间隔内的时间间隔 - 在ECG数据中的每个的两个连续点,这代表心室活动,之间 的时间间隔,worin比较尺寸darstellt的时间间隔子集的时间间隔之间的时间间隔长度的差,worin时间间隔的固定数目N的时间间隔的子集和worinñ‰¥的每两个时间间隔之间位于第一