Advanced Search
Volume 43 Issue 9
Sep.  2021
Turn off MathJax
Article Contents
Wenchang XU, Wenming HE, Binquan YOU, Yu GUO, Kaicheng HONG, Yuhang CHEN, Suling XU, Xiaohe CHEN. Acute Inferior Myocardial Infarction Detection Algorithm Based on BiLSTM Network of Morphological Feature Extraction[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2561-2568. doi: 10.11999/JEIT200480
Citation: Wenchang XU, Wenming HE, Binquan YOU, Yu GUO, Kaicheng HONG, Yuhang CHEN, Suling XU, Xiaohe CHEN. Acute Inferior Myocardial Infarction Detection Algorithm Based on BiLSTM Network of Morphological Feature Extraction[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2561-2568. doi: 10.11999/JEIT200480

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

doi: 10.11999/JEIT200480
Funds:  The National Key Research and Development Project (2017YFC1001803), The Major Science and Technology Program for Medicine and Health in Zhejiang Province (WKJ-ZJ-2012)
  • Received Date: 2020-06-15
  • Rev Recd Date: 2020-12-16
  • Available Online: 2021-01-05
  • Publish Date: 2021-09-16
  • 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.
  • loading
  • [1]
    ANAND S S and YUSUF S. Stemming the global tsunami of cardiovascular disease[J]. The Lancet, 2011, 377(9765): 529–532. doi: 10.1016/S0140-6736(10)62346-X
    [2]
    RAJPURKAR P, HANNUN A Y, HAGHPANAHI M, et al. Cardiologist-level arrhythmia detection with convolutional neural networks[J]. arXiv preprint arXiv: 1707.01836, 2017.
    [3]
    OH S L, NG E Y K, TAN R S, et al. Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types[J]. Computers in Biology and Medicine, 2019, 105: 92–101. doi: 10.1016/j.compbiomed.2018.12.012
    [4]
    CHAUHAN S and VIG L. Anomaly detection in ECG time signals via deep long short-term memory networks[C]. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Paris, France, 2015.
    [5]
    MARTIS R J, ACHARYA U R, and MIN L C. ECG beat classification using PCA, LDA, ICA and discrete wavelet transform[J]. Biomedical Signal Processing and Control, 2013, 8(5): 437–448. doi: 10.1016/j.bspc.2013.01.005
    [6]
    HARALDSSON H, EDENBRANDT L, and OHLSSON M. Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks[J]. Artificial Intelligence in Medicine, 2004, 32(2): 127–136. doi: 10.1016/j.artmed.2004.01.003
    [7]
    DOHARE A K, KUMAR V, and KUMAR R. Detection of myocardial infarction in 12 lead ECG using support vector machine[J]. Applied Soft Computing, 2018, 64: 138–147. doi: 10.1016/j.asoc.2017.12.001
    [8]
    ACHARYA U R, FUJITA H, SUDARSHAN V K, et al. Automated detection and localization of myocardial infarction using electrocardiogram: A comparative study of different leads[J]. Knowledge-Based Systems, 2016, 99: 146–156. doi: 10.1016/j.knosys.2016.01.040
    [9]
    GOLDBERGER A L, AMARAL L A, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): E215–E220. doi: 10.1161/01.CIR.101.23.e215
    [10]
    KONG Dongdong, ZHU Junjiang, WU Shangshi, et al. A novel IRBF-RVM model for diagnosis of atrial fibrillation[J]. Computer Methods and Programs in Biomedicine, 2019, 177: 183–192. doi: 10.1016/j.cmpb.2019.05.028
    [11]
    中华医学会心血管病学分会, 中华心血管病杂志编辑委员会. 急性ST段抬高型心肌梗死诊断和治疗指南[J]. 中华心血管病杂志, 2015, 43(5): 380–393. doi: 10.3760/cma.j.issn.0253-3758.2015.05.003

    Chinese Medical Association division of Cardiology and Editorial Board of The Chinese Journal of Cardiovascular Medicine. Guidelines for the diagnosis and treatment of acute ST-segment elevation myocardial infarction[J]. Chinese Journal of Cardiology, 2015, 43(5): 380–393. doi: 10.3760/cma.j.issn.0253-3758.2015.05.003
    [12]
    CHEN Riqing, HUANG Yingsong, and WU Jian. Multi-window detection for P-wave in electrocardiograms based on bilateral accumulative area[J]. Computers in Biology and Medicine, 2016, 78: 65–75. doi: 10.1016/j.compbiomed.2016.09.012
    [13]
    MINCHOLÉ A, JAGER F, and LAGUNA P. Discrimination between ischemic and artifactual ST segment events in Holter recordings[J]. Biomedical Signal Processing and Control, 2010, 5(1): 21–31. doi: 10.1016/j.bspc.2009.09.001
    [14]
    柯丽, 王丹妮, 杜强, 等. 基于卷积长短时记忆网络的心律失常分类方法[J]. 电子与信息学报, 2020, 42(8): 1990–1998. doi: 10.11999/JEIT190712

    KE Li, WANG Danni, DU Qiang, et al. Arrhythmia classification based on convolutional long short term memory network[J]. Journal of Electronics &Information Technology, 2020, 42(8): 1990–1998. doi: 10.11999/JEIT190712
    [15]
    SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85–117. doi: 10.1016/j.neunet.2014.09.003
    [16]
    田生伟, 周兴发, 禹龙, 等. 基于双向LSTM的维吾尔语事件因果关系抽取[J]. 电子与信息学报, 2018, 40(1): 200–208. doi: 10.11999/JEIT170402

    TIAN Shengwei, ZHOU Xingfa, YU Long, et al. Causal relation extraction of Uyghur events based on bidirectional long short-term memory model[J]. Journal of Electronics &Information Technology, 2018, 40(1): 200–208. doi: 10.11999/JEIT170402
    [17]
    SHARMA L D and SUNKARIA R K. Myocardial infarction detection and localization using optimal features based lead specific approach[J]. IRBM, 2020, 41(1): 58–70. doi: 10.1016/j.irbm.2019.09.003
    [18]
    SAFDARIAN N, DABANLOO N J, and ATTARODI G. A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal[J]. Journal of Biomedical Science and Engineering, 2014, 7(10): 818–824. doi: 10.4236/jbise.2014.710081
    [19]
    SHARMA L D and SUNKARIA R K. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach[J]. Signal, Image and Video Processing, 2018, 12(2): 199–206. doi: 10.1007/s11760-017-1146-z
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(6)

    Article Metrics

    Article views (1417) PDF downloads(104) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return