论文标题
使用DCT层的自动编码器实时无线ECG衍生的呼吸率估算
Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer
论文作者
论文摘要
在本文中，我们使用具有DCT层的自动编码器提出了无线ECG衍生的呼吸率（RR）估计。无线可穿戴系统记录了受试者的ECG数据和呼吸速率，取决于ECG数据基线水平的变化。对使用无线可穿戴系统获得的ECG数据进行的直接傅立叶分析可能会导致由于呼吸不平的结果而导致不正确的结果。为了提高估计精度，我们提出了一个神经网络，该神经网络使用新型离散余弦变换（DCT）层来降低数据并脱离数据。 DCT层在转换域中具有可训练的权重和软阈值。在我们的数据集中，我们使用带有DCT层的新型神经网络改善了基于傅立叶分析的方法的平均平方误差（MSE）和平均绝对误差（MAE）。
In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wireless wearable system may lead to incorrect results due to uneven breathing. To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data. The DCT layer has trainable weights and soft-thresholds in the transform domain. In our dataset, we improve the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the Fourier analysis-based approach using our novel neural network with the DCT layer.
