Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).

中文翻译：

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从ECG信号到图像：基于变换的深度学习方法

激进性心脏病与室性心律失常（VA）有关。心室快速性心律失常是一种不规则且快速的心律，它是由心室中不适当的电脉冲引起的。不同类型的心律不齐与不同的模式相关联，可以被识别。心电图（ECG）是用于解释和记录ECG信号的主要分析工具。心电图信号是非线性的，难以解释和分析。我们提出了一种新的深度学习方法来检测VA。最初，ECG信号被转换为以前未完成的图像。后来，这些图像被规范化并用于训练AlexNet，VGG-16和Inception-v3深度学习模型。执行转移学习以训练模型并从不同的输出层提取深层特征。之后，通过串联方法融合特征，并使用启发式熵计算方法选择最佳特征。最后，将监督学习分类器用于最终特征分类。在MIT-BIH数据集上对结果进行评估，并达到97.6％的准确度（使用立方支持向量机作为最终分类器）。