DOI QR코드

DOI QR Code

Neural Networks-Based Method for Electrocardiogram Classification

  • 투고 : 2023.09.05
  • 발행 : 2023.09.30

초록

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

키워드

참고문헌

  1. PANCHENKO, T.V., BUDICHENKO, V.O. (2016): Real-time Health Monitoring via ECG Analysis, "Artificial Intelligence", No. 4 (74), pp. 98-100.
  2. CLIFFORD, G.D., LIU, Ch., MOODY, B., LEHMAN, L.H., SILVA, I., LI, Q., JOHNSON, A.E., MARK, R.G. (2017): AF Classification from a Short Single Lead ECG Recording: The PhysioNet - Computing in Cardiology Challenge 2017, "Computing in Cardiology", pp.1-4, doi: 10.22489/CinC.2017.065-469.
  3. CHEN, D., LI, D., XU, X., YANG, R., NG, S.-K. (2021): Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG, 10 p. https://arxiv.org/pdf/2101.07535.pdf
  4. YAVORSKYI, A., TYSHCHENKO, B., PANCHENKO, T. (2021): Efficient ECG Analysis with High F1 Score and Low Computation Complexity, "Proc. 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2021)", pp. 348-352.
  5. PANCHENKO, T., YAVORSKYI, A., HU, ZH. (2022): Electrocardiogram Effective Analysis Based on the Random Forest Model with Preselected Parameters, "Lecture Notes on Data Engineering and Communications Technologies", Vol. 135, pp. 137-145.
  6. YAVORSKYI, A. (2021): Real-Time Analysis and Processing of Cardiogram Signals, "Bulletin of Taras Shevchenko National University of Kyiv, Series Physics & Mathematics", No. 1, pp. 108-113.
  7. YAVORSKYI, A., PANCHENKO, T., TYSHCHENKO, B. (2021): ECG Analysis with High Precision and Recall, "Proc. Problems of Decision Making under Uncertainties (PDMU-2021)", pp. 78-79.
  8. GOLDBERGER, A., AMARAL, L., GLASS, L., HAUSDORFF, J., IVANOV, P.C., MARK, R., MIETUS, J.E., MOODY, G.B., PENG, C.K., STANLEY, H.E. (2000): PhysioBank, Physio-Toolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, # 101 (23), pp. e215-e220, https://physionet.org/content/challenge-2017.
  9. MOODY, G.B., MARK, R.G. (1983): A new method for detecting atrial fibrillation using R-R intervals, "Computers in Cardiology", No. 10, pp. 227-230.
  10. MOODY, G.B., MARK, R.G. (2001): The impact of the MIT-BIH Arrhythmia Database, "IEEE Engineering in Medicine and Biology Magazine", Vol. 20, No. 3, pp. 45-50.
  11. BUTTERWORTH, S. (1930). On the Theory of Filter Amplifiers, "Experimental Wireless and the Wireless Engineer", No. 7, pp. 536-541.
  12. WU, L., XIE, X., WANG, Y. (2021): ECG Enhancement and R-Peak Detection Based on Window Variability, "Healthcare" (Basel), 9 (2), P. 227; doi: 10.3390/healthcare9020227.
  13. YAVORSKY, A., PANCHENKO, T. (2022): Effective Methods for Heart Disease Detection via ECG Analyses, "International Journal of Computer Science and Network Security", 22 (5), pp. 127-134.