生物医学工程研究2023，Vol.42Issue(4)：329-336,8.DOI:10.19529/j.cnki.1672-6278.2023.04.04
基于深度学习的心电信号特征点检测的算法研究
Research on ECG signal feature point detection algorithm based on deep learning
梁晓洪 1宋宁宁 1刘成友 1田书畅 1张华伟 1秦航1
作者信息
- 1. 南京医科大学附属南京医院(南京市第一医院),南京 210006
- 折叠
摘要
Abstract
In order to realize automatic,accurate and effective analysis of ECG data,we proposed a deep learning based ECG intel-ligent analysis model ECG SegNet to identify P waves,QRS complexes and T waves,and detect these waveforms' onsets and offsets.Firstly,the standard dilated convolution module was introduced into the encoder path to extract more ECG signal features.Then the bi-directional long term and short term memory was added to the encoding structure,to obtain numerous temporal features.In addition,the feature sets of each level in the encoder path were connected to the decoder part for multi-scale decoding to mitigate the information loss in the encoding process.Finally,the model was trained and tested on QT and LU databases respectively.On the QT database,the average F1 of P wave,QRS complex and T wave detection was 99.53%,99.82%,99.41%,respectively.On the LU database,the av-erage F1 of P wave,QRS complex and T wave detection was 94.74%,98.88%,97.53%,respectively.The results show that the model has good flexibility and reliability when applied to ECG signals detection,and it is a reliable method for analyzing ECG signals in real-time.关键词
心电信号/深度学习/编码解码结构/卷积神经网络/双向长短时记忆网络Key words
Electrocardiogram/Deep learning/Encoder-decoder structure/Convolution neural network/Bidirectional long short-term memory分类
医药卫生引用本文复制引用
梁晓洪,宋宁宁,刘成友,田书畅,张华伟,秦航..基于深度学习的心电信号特征点检测的算法研究[J].生物医学工程研究,2023,42(4):329-336,8.
