Electrophysiological signals can effectively reflect various physiological states of human body, and provide favorable basis for medical diagnosis. However, the correct analysis of electrophysiological signals requires professional medical diagnosis experience. With the rapid development of artificial intelligence, intelligent diagnosis methods based on deep learning are gradually applied in the medical field in order to reduce the dependence of diagnosis results on medical experience. Deep learning has made remarkable achievements in the field of image processing, through which deeper information can be extracted than through time-series signals. Therefore, this paper proposes a method of 2DECG diagnosis based on Faster R-CNN (Faster Region-based Convolutional Neural Network). First, the time-series ECG signal is transformed into two-dimensional curve. Then, the Faster R-CNN model based on beat is obtained by using dataset training. Finally, three kinds of ECG diseases are diagnosed by the Faster R-CNN model. The test results show that compared with the effect of one-dimensional CNN, the method proposed in this paper has high diagnosis accuracy and can help doctors to diagnose diseases more intuitively.

中文翻译：

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基于2DECG模型的智能诊断方法

电生理信号能有效反映人体的各种生理状态，为医学诊断提供有利依据。但是，正确分析电生理信号需要专业的医学诊断经验。随着人工智能的快速发展，基于深度学习的智能诊断方法逐渐应用于医疗领域，以减少诊断结果对医疗经验的依赖。深度学习在图像处理领域取得了令人瞩目的成就，通过它可以提取比通过时间序列信号更深的信息。因此，本文提出了一种基于Faster R-CNN（Faster Region-based Convolutional Neural Network）的2DECG诊断方法。第一的，时间序列心电信号转化为二维曲线。然后通过数据集训练得到基于beat的Faster R-CNN模型。最后，通过 Faster R-CNN 模型诊断出三种心电图疾病。测试结果表明，与一维CNN的效果相比，本文提出的方法具有较高的诊断准确率，可以帮助医生更直观地诊断疾病。