Software College, Northeastern University, Shenyang 110169, China.
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Sensors (Basel). 2020 Mar 12;20(6):1579. doi: 10.3390/s20061579.
Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.
自动检测心律失常对于心血管疾病的早期预防和诊断具有重要意义。基于专家知识的传统特征工程方法缺乏多维和多视角的信息抽象和数据表示能力，因此传统的心律失常检测模式识别研究无法取得满意的结果。最近，随着深度学习技术的增加，基于深度神经网络的 ECG 数据自动特征提取已被广泛讨论。为了利用不同方案之间的互补优势，本文提出了一种基于 ECG 信号多分辨率表示 (MRR) 的心律失常检测方法。该方法利用四个不同的最新深度神经网络作为四个通道模型，用于 ECG 向量表示学习。基于深度学习的表示与 ECG 的手工特征一起形成了 MRR，作为下游分类策略的输入。在大型 ECG 数据集多标签分类的实验结果证实，所提出方法的 F1 得分为 0.9238，分别比每个通道模型高 1.31%、0.62%、1.18%和 0.6%。从架构角度来看，该方法具有高度可扩展性，可以作为心律失常识别的示例。
