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.
翻译:电动心电图信号受到多种因素造成的多种噪音的影响。 因此,在进一步分析之前,这种电动心电图信号是一种标准做法,即将这种信号密封起来。 随着新机器学习分支的推进,称为深层学习,有新的方法可以保证这项任务的先进性能。 我们提出了一种新颖的方法,用深层反复出现的神经网络来进行隐蔽电动心电图信号; 我们利用由动态ECG模型生成的合成数据对网络进行预先培训,并用真实数据对其进行微调。 我们还调查了合成培训数据对网络性能对真实信号的影响。 提议的方法是在真实的数据集中测试的, 噪音程度各异。 结果表明, 四层深层经常神经网络可以超越高度无线信号的参考方法。 此外, 使用合成数据的网络似乎比仅用真实数据训练的网络有更好的结果。 我们表明,有可能创建先进的节能神经网络,在对人工数据进行预先培训之前,能够在适当调整后对实际ECG信号进行特别良好的测试。