论文标题
基于转移学习的集合体系结构，用于ECG信号分类
A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification
论文作者
论文摘要
心电图信号的手动解释和分类既缺乏准确性和可靠性。这些连续的时间序列信号在表示为基于CNN的分类的图像时更有效。这里使用连续的小波变换过滤器获取相应的图像。在实现最佳结果时，通用CNN体系结构缺乏足够的精度，并且运行时间也更高。为了解决这个问题，我们提出了一种基于学习的模型的合奏方法，以对心电图进行分类。在我们的工作中，两种修改的VGG-16型号和一个具有添加功能提取层和成像网重的InceptionResnetv2模型正在用作骨架。集合后，我们报告的准确性增加了6.36％，比以前的基于MLP的算法的算法增加了6.36％。与Physionet数据集进行5倍的交叉验证后，我们的模型的精度为99.98％。
Manual interpretation and classification of ECG signals lack both accuracy and reliability. These continuous time-series signals are more effective when represented as an image for CNN-based classification. A continuous Wavelet transform filter is used here to get corresponding images. In achieving the best result generic CNN architectures lack sufficient accuracy and also have a higher run-time. To address this issue, we propose an ensemble method of transfer learning-based models to classify ECG signals. In our work, two modified VGG-16 models and one InceptionResNetV2 model with added feature extracting layers and ImageNet weights are working as the backbone. After ensemble, we report an increase of 6.36% accuracy than previous MLP-based algorithms. After 5-fold cross-validation with the Physionet dataset, our model reaches an accuracy of 99.98%.
