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首页> 外国专利> A CASCADED BINARY CLASSIFIER FOR IDENTIFYING RHYTHMS IN A SINGLE-LEAD ELECTROCARDIOGRAM (ECG) SIGNAL

A CASCADED BINARY CLASSIFIER FOR IDENTIFYING RHYTHMS IN A SINGLE-LEAD ELECTROCARDIOGRAM (ECG) SIGNAL

机译：级联二进制分类器，用于识别单导心电图（ECG）信号中的节律

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#$%^&*AU2018200751A120190404.pdf#####5 ABSTRACT A CASCADED BINARY CLASSIFIER FOR IDENTIFYING RHYTHMS IN A SINGLE-LEAD ELECTROCARDIOGRAM (ECG) SIGNAL Current technologies analyze electrocardiogram (ECG) signals for a long 10 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. 15 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 20 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. 25 [To be published with FIG. 3] 24I M LINO. 01 311CCLS. J Acquiring, via one or more hardware processors, a single-lead electrocardiogram (ECG) signal that is recorded for a - 202 predefined time interval Applying in real-time, via the one or more hardware processors, a spectrogram based noisy data removal technique on the - 204 acquired single-lead ECG signal to obtain a clean ECG signal Extracting one or more features from the clean ECG signal - 206 Selecting, using an optimum feature selection technique, one or more optimum features from the one or more extracted - 208 features Identifying based on the selected one or more optimum features, using a binary cascade classifier, at least one of one or more normal rhythms, a first set of abnormal rhythms, and a 210 second set of abnormal rhythms in at least one of the single lead electrocardiogram (ECG) signal, and the clean ECG signal FIG. 2
机译：＃$％^＆* AU2018200751A120190404.pdf #####5摘要用于识别节奏的级联二进制分类器单芯心电图（ECG）信号中当前的技术可以长时间分析心电图（ECG）信号10个持续时间，这并不总是实际的情况。此外，目前的情况仅在正常和房颤（AF）之间执行二进制分类，而除了AF之外，还有许多异常的节律。常规系统/方法有其自身的局限性，并且可能会误分类ECG信号，从而导致不平衡的多标签分类问题。15本公开的实施例提供了以下系统和方法：强大，更有效地对节奏进行分类，例如正常，AF，其他通过执行频谱图来检查异常的节律和嘈杂的ECG记录基于噪声的去除，可从获得的单引线中获得干净的ECG信号ECG信号，在分类的每个层上的最佳特征选择从提取的特征池中提取20个最佳特征，并进行多层级联二进制分类器，用于识别干净ECG信号每一层的节奏分类器。[将与图25一起发布。 3]24我是LINO。 01 311CCLS。 Ĵ通过一个或多个硬件处理器获得单头心电图（ECG）信号记录为-202预定义的时间间隔通过一个或多个硬件处理器实时应用，-204上基于频谱图的噪声数据去除技术采集单导ECG信号以获得干净的ECG信号从干净的ECG信号中提取一个或多个特征-206使用最佳特征选择技术选择一个一个或多个提取出的一个或多个最佳特征-208特征根据所选的一个或多个最佳标识功能，使用二元级联分类器，至少之一或更多正常节奏，第一组异常节奏和210至少其中一个中的第二组异常节律前导心电图（ECG）信号和干净的ECG信号图。 2

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