EKG OPARTE NA IOT: HYBRYDOWE PODEJŚCIE CNN-BILSTM DO KLASYFIKACJI ZAWAŁÓW MIĘŚNIA SERCOWEGO

[1] Acharya U. R. et al.: A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine 89, 2017, 389–396.
DOI: https://doi.org/10.1016/j.compbiomed.2017.08.022   Google Scholar

[2] Acharya U. R. et al.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences 415, 2017, 190–198.
DOI: https://doi.org/10.1016/j.ins.2017.06.027   Google Scholar

[3] ANSI/AAMI EC57. Association for the Advancement of Medical Instrumentation and Others, Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms 1998 (1998).
  Google Scholar

[4] Benjamin E. J. et al.: Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation 139(10), 2019, e56-e528.
  Google Scholar

[5] Bisong E.: Building machine learning and deep learning models on Google cloud platform. Apress, Berkeley 2019.
DOI: https://doi.org/10.1007/978-1-4842-4470-8   Google Scholar

[6] Bousseljot R., Kreiseler D., Schnabel A.: Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet, 1995, 317–318.
DOI: https://doi.org/10.1515/bmte.1995.40.s1.317   Google Scholar

[7] Douzas G., Bacao F., Last F.: Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information sciences 465, 2018, 1–20.
DOI: https://doi.org/10.1016/j.ins.2018.06.056   Google Scholar

[8] Fan X. et al.: A novel hybrid network of fusing rhythmic and morphological features for atrial fibrillation detection on mobile ECG signals. Neural Computing and Applications 32(12), 2020, 8101–8113.
DOI: https://doi.org/10.1007/s00521-019-04318-2   Google Scholar

[9] Gao J. et al.: An effective LSTM recurrent network to detect arrhythmia on imbalanced ECG dataset. Journal of healthcare engineering 1, 2019, 6320651.
DOI: https://doi.org/10.1155/2019/6320651   Google Scholar

[10] Goldberger A. L. et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 2000, e215-e220.
DOI: https://doi.org/10.1161/01.CIR.101.23.e215   Google Scholar

[11] Guo L., Sim G., Matuszewski B.: Inter-patient ECG classification with convolutional and recurrent neural networks. Biocybernetics and Biomedical Engineering 39(3), 2019, 868–879.
DOI: https://doi.org/10.1016/j.bbe.2019.06.001   Google Scholar

[12] Guth J. et al.: Comparison of IoT platform architectures: A field study based on a reference architecture. Cloudification of the Internet of Things – CIoT. IEEE, 2016.
DOI: https://doi.org/10.1109/CIOT.2016.7872918   Google Scholar

[13] Kachuee M., Fazeli S., Sarrafzadeh M.: ECG heartbeat classification: A deep transferable representation. IEEE international conference on healthcare informatics – ICHI. IEEE, 2018.
DOI: https://doi.org/10.1109/ICHI.2018.00092   Google Scholar

[14] Kiranyaz S., Ince T., Gabbouj M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE transactions on biomedical engineering 63(3), 2015, 664–675.
DOI: https://doi.org/10.1109/TBME.2015.2468589   Google Scholar

[15] Hossin M., Sulaiman M. N.: A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process 5(2), 2015, 1.
DOI: https://doi.org/10.5121/ijdkp.2015.5201   Google Scholar

[16] Makhir A. et al.: Comprehensive Cardiac Ischemia Classification Using Hybrid CNN-Based Models. International Journal of Online and Biomedical Engineering – iJOE 20(3), 2024, 2024, 154–165.
DOI: https://doi.org/10.3991/ijoe.v20i03.45769   Google Scholar

[17] Mark R. G. et al.: An annotated ECG database for evaluating arrhythmia detectors. IEEE Transactions on Biomedical Engineering 29(8), 1982.
  Google Scholar

[18] Marti H. H., Risau W.: Angiogenesis in ischemic disease. Thrombosis and haemostasis 82(S 01), 1999, 44–52.
DOI: https://doi.org/10.1055/s-0037-1615552   Google Scholar

[19] Moody G. B., Mark R. G.: The impact of the MIT-BIH arrhythmia database. IEEE engineering in medicine and biology magazine 20(3), 2001, 45–50.
DOI: https://doi.org/10.1109/51.932724   Google Scholar

[20] Rautaharju P. M., Surawicz B., Gettes L. S.: AHA/ACCF/HRS recom-mendations for the standardization and interpretation of the electrocardiogram: part IV: the ST segment, T and U waves, and the QT interval: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation 119(10), 2009, e241-e250.
DOI: https://doi.org/10.1161/CIRCULATIONAHA.108.191096   Google Scholar

[21] Singh S. et al.: Classification of ECG arrhythmia using recurrent neural networks. Procedia Computer Science 132, 2018, 1290–1297.
DOI: https://doi.org/10.1016/j.procs.2018.05.045   Google Scholar

[22] Tan K. F., Chan K. L., Choi K.: Detection of the QRS complex, P wave and T wave in electrocardiogram. First International Conference Advances in Medical Signal and Information Processing (IEE Conf. Publ. No. 476). IET, 2000.
DOI: https://doi.org/10.1049/cp:20000315   Google Scholar

[23] Wu M. et al.: A study on arrhythmia via ECG signal classification using the convolutional neural network. Frontiers in computational neuroscience 14, 2021, 564015.
DOI: https://doi.org/10.3389/fncom.2020.564015   Google Scholar

[24] Yildirim Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in biology and medicine 96, 2018, 189–202.
DOI: https://doi.org/10.1016/j.compbiomed.2018.03.016   Google Scholar

[25] Zhao R. et al.: Machine health monitoring with LSTM networks. 10th International Conference on Sensing Technology – ICST. IEEE, 2016.
DOI: https://doi.org/10.1109/ICSensT.2016.7796266   Google Scholar

[26] Nurmaini S. et al.: An automated ECG beat classification system using deep neural networks with an unsupervised feature extraction technique. Applied sciences 9(14), 2019, 2921.
DOI: https://doi.org/10.3390/app9142921   Google Scholar