[论文解读] Deep Recurrent Neural Networks for ECG Signal Denoising

Karol Antczak|arXiv (Cornell University)|Jul 30, 2018

ECG Monitoring and Analysis参考文献 31被引用 81

一句话总结

本文提出一种用于ECG信号的深度递归去噪神经网络，该网络使用合成数据进行预训练，并在真实数据上进行微调，以在高噪声信号上超越参考方法。

ABSTRACT

Electrocardiographic signal is a subject to multiple noises, caused by various factors. It is therefore a standard practice to denoise such signal before further analysis. With advances of new branch of machine learning, called deep learning, new methods are available that promises state-of-the-art performance for this task. We present a novel approach to denoise electrocardiographic signals with deep recurrent denoising neural networks. We utilize a transfer learning technique by pretraining the network using synthetic data, generated by a dynamic ECG model, and fine-tuning it with a real data. We also investigate the impact of the synthetic training data on the network performance on real signals. The proposed method was tested on a real dataset with varying amount of noise. The results indicate that four-layer deep recurrent neural network can outperform reference methods for heavily noised signal. Moreover, networks pretrained with synthetic data seem to have better results than network trained with real data only. We show that it is possible to create state-of-the art denoising neural network that, pretrained on artificial data, can perform exceptionally well on real ECG signals after proper fine-tuning.

研究动机与目标

• 动机：需要对受多种噪声干扰的ECG信号进行有效去噪。
• 提出一种四层的深度递归去噪神经网络架构。
• 通过在合成ECG数据上进行预训练并在真实数据上微调来研究迁移学习。
• 在真实带噪ECG数据上评估该方法，并与参考方法进行比较。

提出的方法

• 使用四层深度递归神经网络对ECG信号进行去噪。
• 在由动态ECG模型生成的合成数据上对网络进行预训练。
• 在真实ECG数据上对预训练网络进行微调。
• 评估合成预训练对真实信号性能的影响。
• 将去噪性能与参考方法进行比较，特别是在高噪声信号下。

实验结果

研究问题

• RQ1 深度递归去噪网络是否能在高噪ECG信号上超越参考方法？
• RQ2 在合成ECG数据上的预训练是否在对真实ECG信号进行微调后提升去噪性能？
• RQ3 合成数据的质量和迁移学习对真实世界ECG去噪结果有何影响？

主要发现

• 四层深度递归网络在高噪ECG信号上可超越参考方法。
• 用合成数据进行预训练往往比仅在真实数据上训练取得更好的结果。
• 该方法表明有可能创建一个在人工数据上预训练、对真实信号进行微调的最先进去噪网络。