QRS复合波检测技术综述  

Review on technologies for QRS complex detection

作　　者：胡丹琴 蔡文杰[1] HU Danqin;CAI Wenjie(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学医疗器械与食品学院,上海200093

出　　处：《中国医学物理学杂志》2020年第9期1208-1212,共5页Chinese Journal of Medical Physics

基　　金：国家自然科学基金(31830042)。

摘　　要：心电图是诊断各种心脏疾病的一个重要手段,而准确识别QRS复合波也是多种自动化心电图分析方法的一个前提。检测QRS复合波的传统方法主要有差分阈值算法、双阈值检测算法、经验模态分解法、小波变换算法等,这些算法的主要步骤包括对心电信号进行预处理、特征提取和检测等,对心电信号质量要求比较高,且通用性不是很强。相对于传统方法检测QRS复合波,人工智能的发展特别是深度学习的出现为QRS复合波检测提供一种新的方法,利用深度学习可自主提取QRS复合波特征信息,从而进行精准定位,相比传统方法,鲁棒性更好,对信号质量不佳的数据检测效果更好。本研究主要对用于QRS复合波预处理以及检测的技术进行综述,并对检测技术的发展进行展望。Electrocardiogram(ECG)is an important way to diagnose various heart diseases,and the accurate identification of QRS complex is required for automatic electrocardiogram analysis.The traditional methods for detecting QRS complex mainly include differential threshold method,detection algorithm based on double-threshold,empirical mode decomposition method,wavelet transform algorithm,etc.The main steps of these algorithms contain preprocessing,feature extraction and detection.The traditional methods have a poor generality and a high requirement of ECG signal quality.Compared with traditional methods for detecting QRS complex,the development of artificial intelligence,especially the emergence of deep learning,provides a new method for QRS complex detection.Deep learning can be used to independently extract QRS complex feature information,thereby realizing precise positioning.Compared with traditional methods,deep learning has a better robustness and a better detection effect for the data with poor signal quality.Herein the technologies used in the preprocessing and detection of QRS complex detection are reviewed,and the future developments are discussed.

关 键 词：心电图 QRS复合波 深度神经网络 综述 

分 类 号：R318[医药卫生—生物医学工程]