Pacing Electrocardiogram Detection With Memory-Based Autoencoder and Metric Learning

Zhaoyang Ge; Huiqing Cheng; Zhuang Tong; Lihong Yang; Bing Zhou; Zongmin Wang

doi:10.3389/fphys.2021.727210

Pacing Electrocardiogram Detection With Memory-Based Autoencoder and Metric Learning

Front Physiol. 2021 Dec 17:12:727210. doi: 10.3389/fphys.2021.727210. eCollection 2021.

Authors

Zhaoyang Ge^{1

2}, Huiqing Cheng^{1

2}, Zhuang Tong³, Lihong Yang⁴, Bing Zhou^{1

2}, Zongmin Wang^{1

2}

Affiliations

¹ School of Information Engineering, Zhengzhou University, Zhengzhou, China.
² Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China.
³ Big Data Center of Clinical Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁴ Department of Cardio-Pulmonary Function, Henan Provincial People's Hospital, Zhengzhou, China.

Abstract

Remote ECG diagnosis has been widely used in the clinical ECG workflow. Especially for patients with pacemaker, in the limited information of patient's medical history, doctors need to determine whether the patient is wearing a pacemaker and also diagnose other abnormalities. An automatic detection pacing ECG method can help cardiologists reduce the workload and the rates of misdiagnosis. In this paper, we propose a novel autoencoder framework that can detect the pacing ECG from the remote ECG. First, we design a memory module in the traditional autoencoder. The memory module is to record and query the typical features of the training pacing ECG type. The framework does not directly feed features of the encoder into the decoder but uses the features to retrieve the most relevant items in the memory module. In the training process, the memory items are updated to represent the latent features of the input pacing ECG. In the detection process, the reconstruction data of the decoder is obtained by the fusion features in the memory module. Therefore, the reconstructed data of the decoder tends to be close to the pacing ECG. Meanwhile, we introduce an objective function based on the idea of metric learning. In the context of pacing ECG detection, comparing the error of objective function of the input data and reconstructed data can be used as an indicator of detection. According to the objective function, if the input data does not belong to pacing ECG, the objective function may get a large error. Furthermore, we introduce a new database named the pacing ECG database including 800 patients with a total of 8,000 heartbeats. Experimental results demonstrate that our method achieves an average F1-score of 0.918. To further validate the generalization of the proposed method, we also experiment on a widely used MIT-BIH arrhythmia database.

Keywords: attention mechanism; autoencoder; electrocardiogram signals; heartbeat arrhythmias detection; metric learning.