From Noise to Knowledge: Investigating Pre-processing Techniques for Accurate Diagnosis of Heart and Brain Diseases
Abstract
Healthcare data may or may not contain sensitive information. When it comes to keeping the data anywhere, it is quite easy, but when it comes to keeping the data systematically, the retrieval time becomes the lowest, the task is tedious. Due to unhealthy lifestyle, the number of patients is diagnosed with heart and brain diseases. A non-invasive method such as electrocardiogram (ECG) & Electroencephalogram (EEG) are used to monitor the health of the heart and brain respectively. ECG signals play a critical role in diagnosing various heart diseases like CAD, arrhythmia, whereas EEG signal is used to diagnose Alzheimer, epilepsy etc. at the initial stage, and a person’s life can be saved by delivering an appropriate medication on time. Both ECG & EEG is a non-stationary signal, it has to be analysed within a time limit; otherwise, it is of no use. These signals are also impacted by different types of noise and artifacts, which necessitates the development of effective preprocessing techniques to enhance signal quality and extract relevant information. These preprocessing techniques are used to improve the interpretation of signals and helps in finding the abnormal patterns in the signals. The precision of diagnosing the heart and brain disease heavily depends on the preprocessed data. In this paper, common preprocessing techniques of ECG signals and EEG signals are discussed, which provides the useful insights in ECG & EEG signals for diagnosing heart and brain diseases. It also helps researchers and clinicians to choose suitable methods to enhance the accuracy of diagnosis.
References
• Al-Hindawi A. M. and Qassem M. (2024). A new adaptive filter for EEG signal processing based on theta band enhancement. Biomed. Signal Process. Control.
• Celin, S., & Vasanth, K. (2018). ECG signal classification using various machine learning techniques. Journal of medical systems, 42(12), 241.
• Chowdary, M. T. C., Kumar, C. L., Kavitha, N. R., & Devi, D. A. (2024). Noise cancellation of electrocardiogram signal using moving average filter. In Disruptive technologies in Computing and Communication Systems (pp. 352-357). CRC Press.
• Jabeur, T. B., Bashier, E., Sandhu, Q., Bwalya, K. J., & Joshua, A. (2024). Comprehensive Noise and Artifact Removal from ECG Signals Using Wavelet, Variational Mode Decomposition and Nonlocal Means Algorithms. Mathematical Modelling of Engineering Problems, 11(8).
• Lai, C. Q., Ibrahim, H., Abdullah, M. Z., Abdullah, J. M., Suandi, S. A., & Azman, A. (2018, April). Artifacts and noise removal for electroencephalogram (EEG): A literature review. In 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 326-332). IEEE.
• Lakehal, M. R., & Ferdi, Y. (2024). Baseline wander and power line interference removal from physiological signals using fractional notch filter optimized through genetic algorithm. Arabian Journal for Science and Engineering, 49(12), 16647-16667.
• Malghan, P. G., & Hota, M. K. (2020). A review on ECG filtering techniques for rhythm analysis. Research on Biomedical Engineering, 36, 171-186.
• Marzog, H. A., & Abd, H. J. (2022). Machine learning ECG classification using wavelet scattering of feature extraction. Applied Computational Intelligence and Soft Computing, 2022(1), 9884076.
• Prakash, V., & Kumar, D. (2024). An efficient approach for denoising EOG artifact through optimal wavelet selection. International Journal of Information Technology, 16(1), 279-292.
• Samara singh K. and Wong W(2024). Filtering techniques for ECG preprocessing in smart healthcare applications. Advances in Biomedical Signal Processing, Springer, pp. 345–358.
• Usman, S. M., Khalid, S., & Aslam, M. H. (2020). Epileptic seizures prediction using deep learning techniques. Ieee Access, 8, 39998-40007.
• Chaddad, A., Wu, Y., Kateb, R., & Bouridane, A. (2023). Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques. Sensors, 23(14), 6434.
• Chuang, C. H., Chang, K. Y., Huang, C. S., & Bessas, A. M. (2024). Art: Artifact removal transformer for reconstructing noise-free multichannel electroencephalographic signals. arXiv preprint arXiv:2409.07326.
• Gregory, K., George, K., & George, H. (2016, October). An EEG pre-processing technique for the fast recognition of motor imagery movements. In 2016 IEEE biomedical circuits and systems conference (BioCAS) (pp. 90-94). IEEE.
• Houssein, E. H., Kilany, M., & Hassanien, A. E. (2017). ECG signals classification: a review. International Journal of Intelligent Engineering Informatics, 5(4), 376-396.
• Jambukia, S. H., Dabhi, V. K., & Prajapati, H. B. (2015, March). Classification of ECG signals using machine learning techniques: A survey. In 2015 international conference on advances in computer engineering and applications (pp. 714-721). IEEE.
• Jovicic, E., Jovic, A., & Cifrek, M. (2024, May). Impact of EEG Signal Preprocessing Methods on Machine Learning Models for Affective Disorders. In 2024 47th MIPRO ICT and Electronics Convention (MIPRO) (pp. 1139-1144). IEEE.
• Link1: Dataset link: https://www.physionet.org/content/mitdb/1.0.0/, accessed on 14 April 2025.
• Link2: Dataset link: https://doi.org/10.6084/m9.figshare.5450293, accessed on 14 April 2025.
• Malakouti, S. M. (2023). Heart disease classification based on ECG using machine learning models. Biomedical Signal Processing and Control, 84, 104796.
• Syama, S., Sweta, G. S., Kavyasree, P. I. K., & Reddy, K. J. M. (2019, August). Classification of ECG signal using machine learning techniques. In 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC) (pp. 122-128). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Sustainable Development Through AI, ML and IoT
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.