Arrhythmia is a condition characterized by perturbation of the regular rhythm of the heart. The development of computerized self-diagnostic systems for the detection of these arrhythmias is very popular. Thanks to the machine learning models included in these systems, which eliminate the need for visual inspection of long electrocardiogram (ECG) recordings. In order to design a reliable, generalizable and highly accurate model, large number of subjects and arrhythmia classes are to be included in the training and testing phases of the model. In this study, an ECG dataset containing more than 10,000 subject records was used to train and diagnose arrhythmia. A deep neural network (DNN) model was used on the data set during the extraction of the features of the ECG inputs. Feature maps obtained from hierarchically placed layers in DNN were fed to various shallow classifiers. Principal component analysis (PCA) technique was used to reduce the high dimensions of feature maps. In addition to the morphological features obtained with DNN, various ECG features obtained from lead-II for rhythmic information are fused to increase the performance. Using the ECG features, an accuracy of 90.30% has been achieved. Using only deep features, this accuracy was increased to 97.26%. However, the accuracy was increased to 98.00% by fusing both deep and ECG-based features. Another important research subject of the study is the examination of the features obtained from DNN network both on a layer basis and at each training step. The findings show that the more abstract features obtained from the last layers of the DNN network provide high performance in shallow classifiers, and weight updates of DNN network also increases the performance of these classifiers. Hence, the study presented important finding of fusion of deep features and shallow classifiers to improve the performance of the proposed system.

心律失常是一种以心脏正常节律紊乱为特征的病症。用于检测这些心律失常的计算机化自我诊断系统的开发非常受欢迎。由于这些系统中包含机器学习模型，因此无需对长心电图 (ECG) 记录进行目视检查。为了设计一个可靠、可推广和高度准确的模型，模型的训练和测试阶段将包括大量的受试者和心律失常类别。在这项研究中，一个包含超过 10,000 条受试者记录的 ECG 数据集用于训练和诊断心律失常。在提取 ECG 输入特征的过程中，对数据集使用了深度神经网络 (DNN) 模型。从 DNN 中分层放置的层获得的特征图被馈送到各种浅层分类器。主成分分析（PCA）技术被用来减少特征图的高维。除了使用 DNN 获得的形态特征外，还融合了从第 II 导联获得的各种心电图特征以获取节律信息以提高性能。使用心电图功能，准确率达到了 90.30%。仅使用深度特征，此准确率提高到 97.26%。然而，通过融合深度特征和基于心电图的特征，准确度提高到 98.00%。该研究的另一个重要研究主题是检查从 DNN 网络获得的基于层和每个训练步骤的特征。研究结果表明，从 DNN 网络的最后一层获得的更抽象的特征在浅分类器中提供了高性能，并且 DNN 网络的权重更新也提高了这些分类器的性能。因此，该研究提出了融合深层特征和浅层分类器以提高所提出系统的性能的重要发现。