摘要
针对心电信号(ECG)传统分类方法效率较低的问题,该文提出一种基于自适应快速S变换(AFST)和XGBoost的心电信号精确快速分类方法。该方法首先通过快速定位算法确定心电信号特征频率点,再根据特征频率点自适应调节S变换窗宽因子,增强S变换的时频分辨率的同时避免迭代计算,大大减少运行时间。其次,基于自适应快速S变换的时频矩阵提取12个特征量来表征5种心电信号的特征信息,特征向量维数低,识别能力强。最后,利用XGBoost算法对特征向量进行识别。MIT-BIH心律失常数据库和患者实测数据验证表明,该方法显著地缩短了分类时间,对5种心电信号的分类准确率分别为99.59%和97.32%,适用于实际检测系统中心律失常疾病的快速诊断。
Considering the low efficiency of traditional ElectroCardioGram(ECG)classification methods,an accurate and fast ElectroCardioGram classification method based on Adaptive Fast S-Transform(AFST)and XGBoost is proposed.Firstly,the main feature points of the ECG signals are determined through a fast positioning algorithm,and then the S-Transform window width factor is adjusted adaptively according to the main feature points to enhance the time-frequency resolution of the S-transform while avoiding iterative calculation and reducing the running time greatly;Secondly,based on the time-frequency matrix of AFST,12 eigenvalues are extracted to represent the characteristic information of 5 kinds of ECG signals,with low eigenvector dimension and strong recognition ability.Finally,XGBoost is used to identify the eigenvectors.The experimental studies based on the MIT-BIH arrhythmia database and the verification of patient measurement data show that,with the proposed method,the classification time of ECG signals is significantly shortened and classification accuracy of 99.59%,97.32%is obtained respectively,which is suitable for the rapid diagnosis of abnormal diseases in the center rate of the actual detection system.
作者
袁莉芬
李松
尹柏强
李兵
佐磊
YUAN Lifen;LI Song;YIN Baiqiang;LI Bing;ZUO Lei(School of Electrical and Automation Engineering,Hefei University of Technology,Hefei 230009,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2023年第4期1464-1474,共11页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61971175)
中央高校基本科研业务费(JZ2019YYPY0025)。
