基于形态特征提取的急性下壁心肌梗死BiLSTM网络辅助诊断算法  

Acute Inferior Myocardial Infarction Detection Algorithm Based on BiLSTM Network of Morphological Feature Extraction

作　　者：徐文畅 何文明[2] 游斌权 郭宇 洪凯程 陈雨行 许素玲[2] 陈晓禾 XU Wenchang;HE Wenming;YOU Binquan;GUO Yu;HONG Kaicheng;CHEN Yuhang;XU Suling;CHEN Xiaohe(Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Science,Suzhou 215163,China;The Affiliated Hospital of Medical School,Ningbo University,Ningbo 315211,China;Suzhou Kowloon Hospital,Shanghai Jiao Tong University School of Medicine,Suzhou 215021,China)

机构地区：[1]中国科学院苏州生物医学工程技术研究所,苏州215163 [2]宁波大学医学院附属医院,宁波315211 [3]上海交通大学医学院附属苏州九龙医院,苏州215021

出　　处：《电子与信息学报》2021年第9期2561-2568,共8页Journal of Electronics & Information Technology

基　　金：国家重点研发计划(2017YFC1001803);浙江省医药卫生重大科技计划项目(WKJ-ZJ-2012)。

摘　　要：急性下壁心肌梗死是一种病发急、进展快、致死率高的心脏疾病,该文提出一种新颖的基于形态特征提取的BiLSTM神经网络分类的急性下壁心肌梗死辅助诊断算法,可大幅度提高医生对急性下壁心肌梗死疾病的诊断效率并有助于及时确诊。算法包括:对胸痛中心数据库心拍信号进行降噪及心拍分割;根据临床心内科医学诊断指南提取了12导联波形距离特征和分导联波形幅值特征;依据提取的特征搭建LSTM与BiLSTM神经网络进行心拍的分类识别;使用PTB公开数据库和胸痛中心数据库多临床中心进行交叉验证。实验结果表明,加入胸痛中心真实临床数据后,基于形态特征提取BiLSTM神经网络的急性下壁心肌梗死辅助诊断算法准确率达到99.72%,精度达到99.53%,灵敏度达到100.00%,同时F1-Score达到99.76。该算法比其他现有算法准确率提高至少1%,该项研究具有非常重要的临床应用价值。Acute inferior myocardial infarction is a kind of heart disease with rapid progression and high mortality. In order to improve the diagnosis efficiency for inferior myocardial infarction, a novel algorithm for automatic detection of inferior myocardial infarction based on Bi-directional Long Short-Term Memory(BiLSTM) network of morphological feature extraction is proposed. Based on the clinical ECG signals of the cardiology center, noise is reduced and every heartbeat is segmented. According to the cardiology clinical guidelines and signal analysis, 12 lead waveform distance features and single lead waveform amplitude features are extracted. Additionally, the neural network structure of Long Short-Term Memory(LSTM) and BiLSTM are built from to the extracted features. It is cross-validated by Physikalisch-Technische Bundesanstalt(PTB)public database and chest pain center database, the accuracy reaches 99.72%, the precision and sensitivity reach 99.53% and 100%. At the same time, the F1-Score reaches 99.76. Furthermore, experimental results demonstrated that the accuracy of the novel algorithm is still 1% higher than that of other existing algorithms after adding the chest pain center database.

关 键 词：心电图 人工智能 双向长短期记忆神经网络 形态特征 心肌梗死 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]