Electrocardiography (ECG) signals can be considered as multi-variable time-series. The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mechanism inside the classifier model can be found in current approaches, leading to difficulties in identifying complex ECG forms. In this paper, we proposed a novel deep learning model named Spectral Cross-domain neural network (SCDNN) with a new block called Soft-adaptive threshold spectral enhancement (SATSE), to simultaneously reveal the key information embedded in spectral and time domains inside the neural network. More precisely, the domain-cross information is captured by a general Convolutional neural network (CNN) backbone, and different information sources are merged by a self-adaptive mechanism to mine the connection between time and spectral domains. In SATSE, the knowledge from time and spectral domains is extracted via the Fast Fourier Transformation (FFT) with soft trainable thresholds in modified Sigmoid functions. The proposed SCDNN is tested with several classification tasks implemented on the public ECG databases \textit{PTB-XL} and \textit{MIT-BIH}. SCDNN outperforms the state-of-the-art approaches with a low computational cost regarding a variety of metrics in all classification tasks on both databases, by finding appropriate domains from the infinite spectral mapping. The convergence of the trainable thresholds in the spectral domain is also numerically investigated in this paper. The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains. The repository can be found: https://github.com/DL-WG/SCDNN-TS

翻译：心电图（ECG）信号可被视为多变量时间序列。基于特征工程或深度学习技术的现有最先进ECG数据分类方法，在机器学习系统中将谱域和时域分开处理。当前方法中缺乏分类器模型内部的谱时域通信机制，导致难以识别复杂的心电波形。本文提出了一种名为谱域交叉神经网络（SCDNN）的新型深度学习模型，并引入称为软自适应阈值谱增强（SATSE）的新模块，以同时揭示神经网络中嵌入于谱域和时域的关键信息。具体而言，域间交叉信息通过通用卷积神经网络（CNN）主干网络捕获，而不同信息源则通过自适应机制融合以挖掘时域与谱域之间的关联。在SATSE中，通过快速傅里叶变换（FFT）结合改进Sigmoid函数中的软可训练阈值，提取时域和谱域的知识。所提出的SCDNN在公共心电图数据库PTB-XL和MIT-BIH上实现的多个分类任务中进行了测试。通过从无限谱映射中寻找合适的域，SCDNN在所有分类任务中均以较低计算成本优于现有最先进方法，并针对两个数据库的多种指标表现更佳。本文还对谱域中可训练阈值的收敛性进行了数值研究。SCDNN的稳健性能为从时域和谱域中跨深度学习模型挖掘知识提供了新视角。代码仓库地址：https://github.com/DL-WG/SCDNN-TS