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DEEP LEARNING ALGORITHM-BASED ELECTROCARDIOGRAM FEATURE EXTRACTION METHOD, APPARATUS, SYSTEM, DEVICE, AND CLASSIFICATION METHOD

机译:基于深度学习算法的心电图特征提取方法,装置,系统,装置和分类方法

摘要

A deep learning algorithm-based electrocardiogram feature extraction method, an apparatus, a system, a device, and a classification method. The deep learning algorithm-based electrocardiogram feature extraction method comprises the following steps: randomly capturing a segment of continuous electrocardiogram signals in a 12-lead electrocardiogram to be processed, the electrocardiogram signals comprising at least two cardiac cycles (S1); and inputting the captured electrocardiogram signals into a feature extraction model in the form of pictures, and extracting electrocardiogram signal features, the feature extraction model being obtained by means of training on the basis of a ResNet mode, or an Inception model, or an Inception-ResNet model (S2). According to the deep learning algorithm-based electrocardiogram feature extraction method, the apparatus, the system, the device, and the classification method, incompleteness caused by artificial design of features can be reduced, thereby improving the accuracy and diversity of deep learning algorithm-based electrocardiogram feature extraction.
机译:基于深度学习算法的心电图特征提取方法,装置,系统,设备和分类方法。基于深度学习算法的心电图特征提取方法包括以下步骤:随机捕获要处理的12导联心电图中的连续心电图信号的一部分,该心电图信号包括至少两个心动周期(S1);并将捕获的心电图信号输入图片形式的特征提取模型中,并提取心电图信号特征,该特征提取模型是通过基于ResNet模式或Inception模型或Inception- ResNet模型(S2)。根据基于深度学习算法的心电图特征提取方法,装置,系统,装置和分类方法,可以减少由于特征的人工设计而导致的不完整性,从而提高了基于深度学习算法的准确性和多样性。心电图特征提取。

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