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

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...

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

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.