Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels.
翻译:摘要:当前,远程健康监测领域因其在日常生活中的显著用途而备受关注。这一进展导致了电生理信号数据收集量的增加。由于这一进步,心电图(ECG)信号监测已成为医疗领域的一项重要任务。ECG通过分析心脏生理和异常发挥着重要作用。然而,这些信号受到多种噪声的影响,如电极运动、基线漂移和白噪声等,这影响了诊断的准确性。因此,滤除ECG信号中的噪声成为了一项重要任务。目前,深度学习方案在信号滤波任务中被广泛应用,因为它们具有有效的特征学习架构。本文提出了一种基于深度自编码器模块的ECG信号滤波深度学习方案。根据这个方案,数据通过编码器和解码器层进行处理,通过消除噪声进行重构。所提出的深度学习架构使用修改后的ReLU函数来提高特征学习,因为标准的ReLU无法适应巨大的变化。此外,还加入了跳过连接,该连接保留编码器层的关键特征,同时将这些特征映射到解码器层。类似地,还包括了注意模型,该模型执行通道和空间注意,使用通道和平均池化操作生成鲁棒映射,从而提高学习性能。该方法在公开的MIT-BIH数据集上进行了测试,该数据集添加了不同类型的噪声,例如电极运动、基线走样和运动伪影,并在不同的SNR级别下进行了测试。