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  4. Methods Machine Learning Methods for Classifying Eeg Based on Rhythmic and Morphological Features

Methods Machine Learning Methods for Classifying Eeg Based on Rhythmic and Morphological Features

SISN.
2025;
Volume 18
: pp. 113 - 128
Authors:
  1. Lyubomyr Mosiy
  2. Andriy Sverstiuk
1
Ternopil Ivan Puluj National Technical University, Department of Computer Science, Ukraine
2
Ternopil Ivan Puluj National Technical University, Department of Computer Science, Ukraine; Ternopil Ivan Horbachevsky National Medical University, Department of Medical Informatics, Ukraine

The article presents an experimental study of the effectiveness of machine learning methods for classifying electrocardiographic signals by rhythmic and morphological features using information tech- nology based on the mathematical apparatus of cyclic random processes. The problem of automated detection of atrial arrhythmias is considered, particularly atrial fibrillation and atrial flutter, which are characterized by complex changes in both ECG wave morphology and cardiac cycle time intervals.

Four anomaly detection algorithms were investigated for classifying pathological conditions: OneClassSVM with radial basis function, IsolationForest, Local Outlier Factor (LOF), and Elliptic- Envelope. The impact of data preprocessing methods (StandardScaler for standardization and PCA for dimensionality reduction) on classification accuracy was analyzed. Experimental results showed that preprocessing is critically important for morphological disorder classification, increasing accuracy from 50-83 % to 100 % for atrial fibrillation. The LOF algorithm demonstrated the most stable results (83-100%) for different types of pathologies. For rhythm disorder classification, the IsolationForest, LOF, and EllipticEnvelope methods showed equally high efficiency (89 %), while preprocessing did not lead to significant improvement in results.

electrocardiographic signal modeling
information technology
cyclic discrete random process
amplitude-temporal characteristics
temporal rhythm function
amplitude variability
signal classification
artificial intelligence (AI)
machine learning system (MLS)
  1. Abdul Razak, S. F., Sayed Ismail, S. N. M., Yogarayan, S., Azli Abdullah, M. F., Kamis, N. H., & Abdul Aziz, A. (2023). Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver's Physiological Condition. Civil Engineering Journal, 9(9), 2272–2285. https://doi.org/10.28991/cej-2023-09-09-013
  2. Agrawal, R. K., Sewani, R. R., Delen, D., & Benjamin, B. (2022). A machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitude. Healthcare Analytics, 2, 100121. https://doi.org/10.1016/j.health.2022.100121
  3. Coronado-Reyes, O. I., Téllez-Anguiano, A. C., Castro-Pimentel, L. A., & Gutierrez-Gnecchi, J. A. (2025). Non- invasive Hyperglycemia Detection via Electrocardiogram Using Discrete Wavelet Transform and Machine Learning. Cureus. https://doi.org/10.7759/cureus.80548
  4. Duong, L. T., Doan, T. T. H., Chu, C. Q., & Nguyen, P. T. (2023). Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications, 225, 120107. https://doi.org/10.- 1016/j.eswa.2023.120107
  5. Gupta, T. R., & Nandhini, D. U. (2023). Learning Techniques of ECG Arrhythmia Classification: A Review. In 2023 1st International Conference on Cognitive Computing and Engineering Education (ICCCEE) (pp. 1–5). IEEE. https://doi.org/10.1109/icccee55951.2023.10424554
  6. Hassaballah, M., Wazery, Y. M., Ibrahim, I. E., & Farag, A. (2023). ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems. Bioengineering, 10(4), 429. https://doi.org/10.3390/bioengineering10040429
  7. Janbhasha, S., & Bhavanam, S. N. (2023). A Comparative Analysis of the Feature Selection Process Using Deep Learning Methods for Arrhythmia. In 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (Vol. 119, pp. 1–6). IEEE. https://doi.org/10.- 1109/sceecs57921.2023.10063070
  8. Koubaa, H., Boujelben, M., Hdidane, M., & Kallel, R. (2024). An ECG Data Feature Selection Comparison: Machine Learning and Expert Doctors. In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA) (pp. 1–5). IEEE. https://doi.org/10.1109/aiccsa63423.2024.10912630  
  9. Kumar, S., & Veer, K. (2023). Evaluation of Current Trends in Biomedical Applications Using Soft Computing. Current Bioinformatics, 18(9), 693–714. https://doi.org/10.2174/1574893618666230706112826
  10. Kumar M., A., & Chakrapani, A. (2022). Classification of ECG signal using FFT based improved Alexnet classifier. PLOS ONE, 17(9), e0274225. https://doi.org/10.1371/journal.pone.0274225
  11. Lytvynenko Ya.V. (2017). The method of segmentation of stochastic cyclic signals for the problems of their processing and modeling. Journal of Hydrocarbon Power Engineering, Oil and Gas Measurement and Testing. 4(2), 93–103.
  12. Nurmaini, S., Tondas, A. E., Darmawahyuni, A., Rachmatullah, M. N., Effendi, J., Firdaus, F., & Tutuko, B. (2021). Electrocardiogram signal classification for automated delineation using bidirectional long short-term memory. Informatics in Medicine Unlocked, 22, 100507. https://doi.org/10.1016/j.imu.2020.100507
  13. Philip, A. M., & Hemalatha, S. (2023). A Performance Analysis Of Advanced Machine Learning Techniques For Arrhythmia Detection By Categorizing Ecg Signals. In 2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD) (pp. 7–12). IEEE. https://doi.org/10.1109/icteasd57136.2023.10585040
  14. Rath, A., Mishra, D., & Panda, G. (2022). Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique. Frontiers in Big Data, 5. https://doi.org/10.3389/fdata.2022.1021518
  15. Reethunandh, H R, Y., Venkata Sandeep, T. S., K R, S. N., & Chandrashekar, H. M. (2023). Classification of ECG Arrhythmia Using a Convolution Neural Network. In 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES) (pp. 1–8). IEEE. https://doi.org/10.1109/icsses 58299.2023.10200579
  16. Śmigiel, S., Topoliński, T., Ledziński, D., & Andrysiak, T. (2024). The ECG Signal Monitoring System Using Machine Learning Methods and LoRa Technology. Pomiary Automatyka Robotyka, 28(2), 21–36. https://doi.org/10.14313/par_252/21
  17. Sverstiuk A., & Mosiy L. (2025). Information technology for electrocardiographic signal analysis based on mathematical models of temporal and amplitude variability. Computer Systems and Information Technologies, (2), 36–44. https://doi.org/10.31891/csit-2025-2-4
  18. Sverstiuk А., & Mosiy, L. (2025). Інформаційна технологія опрацювання та аналізу електрокардіосигналів з врахуванням їх морфологічних та ритмічних ознак. Computer-integrated technologies: education, science, production, (60), 40-52. https://doi.org/10.36910/6775-2524-0560-2025-60-04
  19. Ullah, A., & Khan, M. S. (2024). Classification of Sleep Stages Using Single-Channel ECG Signals: A Comparative Analysis of Machine Learning and Deep Learning Methods. In 2024 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1–6). IEEE. https://doi.org/10.- 1109/icspcc62635.2024.10770354
  20. Wasimuddin, M., Elleithy, K., Abuzneid, A., Faezipour, M., & Abuzaghleh, O. (2020). Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey. IEEE Access, 8, 177782–177803. https://doi.org/10.1109/access.2020.3026968
  21. Wasimuddin, M., Elleithy, K., Abuzneid, A., Faezipour, M., & Abuzaghleh, O. (2021). Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling. Electronics, 10(2),170. https://doi.org/10.3390/electronics10020170
  22. Yildirim, B. E., Taskiran, M., & Nur Bekiroglu, K. (2024). Ensemble Learning Methodologies for Electrocardiogram Analysis: A Comparative Study. In 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) (pp. 1–6). IEEE. https://doi.org/10.1109/iitcee59897.2024.10467494
  23. Yue, Y., Chen, C., Liu, P., Xing, Y., & Zhou, X. (2021). Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques. Sensors, 21(16), 5302. https://doi.org/10.3390/s21165302