In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians’ confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.

中文翻译：

---

使用决策树和门控网络融合模型识别心律失常

近年来，基于深度学习（DNN）的方法在检测心律失常方面取得了跨越式的突破，作为算术能力的成本效益，数据规模已经突破了临界点。然而，这些方法无法为建模决策提供基础限制了临床医生对这些方法的信心。在本文中，设计了一个门循环单元（GRU）和决策树融合模型，简称（T-GRU），旨在探索心律失常识别问题并提高深度学习方法的可信度。融合模型多路径处理时频域特征引入决策树概率分析频域特征、GRU模型参数正则化和权重控制，提高决策树模型输出权重。MIT-BIH 心律失常数据库用于验证。结果表明，低频段特征主导了模型预测。该融合模型的准确率为98.31%、敏感性为96.85%、特异性为98.81%、精密度为96.73%，表明其具有较高的可靠性和临床意义。