KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Peng Lu,Hao Xi,Chao Gao,Yang Gao,Yabin Zhang,Liwei Chen,Xiaobo Mao,Bing Zhou,Hongpo Zhang,Hossein Fotouhi

doi:10.1155/2021/6684954

Abstract

Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep neural networks have become a popular method to efficiently and accurately simulate nonlinear patterns of ECG data in a data-driven manner but require more resources. Therefore, it is crucial to design deep learning (DL) algorithms that are more suitable for resource-constrained mobile devices. In this paper, KecNet, a lightweight neural network construction scheme based on domain knowledge, is proposed to model ECG data by effectively leveraging signal analysis and medical knowledge. To evaluate the performance of KecNet, we use the Association for the Advancement of Medical Instrumentation (AAMI) protocol and the MIT-BIH arrhythmia database to classify five arrhythmia categories. The result shows that the ACC, SEN, and PRE achieve 99.31%, 99.45%, and 98.78%, respectively. In addition, it also possesses high robustness to noisy environments, low memory usage, and physical interpretability advantages. Benefiting from these advantages, KecNet can be applied in practice, especially wearable and lightweight mobile devices for arrhythmia classification.

Highlights

Arrhythmias are the most common cardiovascular disease and the leading cause of stroke and sudden cardiac death
Numerous clinical studies have demonstrated the importance of long-term rhythm features for detecting arrhythmia associated with many diseases such as tachycardia, atrial fibrillation, and premature beats. is motivated us to study a new solution to arrhythmia classification based on a long period of continuous ECG signals
We propose a lightweight end-to-end solution for resource-constrained mobile devices that leverages domain knowledge to optimize neural networks for arrhythmia classification

Summary

Introduction

Arrhythmias are the most common cardiovascular disease and the leading cause of stroke and sudden cardiac death. Electrocardiogram (ECG) is a common tool for detecting arrhythmias because of its noninvasive and easy-to-perform nature. Because of the randomness of arrhythmia onset, it is necessary for patients to be monitored for a long period, causing the difficulty of processing the resulting large number of ECG data. Most of the existing algorithms for automatic ECG recognition of arrhythmia are based on the assessment of morphological features of single or fewer heartbeats. Numerous clinical studies have demonstrated the importance of long-term rhythm features for detecting arrhythmia associated with many diseases such as tachycardia, atrial fibrillation, and premature beats. Is motivated us to study a new solution to arrhythmia classification based on a long period of continuous ECG signals Numerous clinical studies have demonstrated the importance of long-term rhythm features for detecting arrhythmia associated with many diseases such as tachycardia, atrial fibrillation, and premature beats. is motivated us to study a new solution to arrhythmia classification based on a long period of continuous ECG signals

Objectives

Methods

Conclusion