This study investigates how well time collection analysis may be used by system-studying algorithms to diagnose migraines. Through the use of various algorithms and current statistical resources, such as EEG activity and affected person histories, the mission will develop a predictive model to identify the start of migraine signs and symptoms, allowing for prompt and early management for sufferers. The results will help to compare how the algorithms affect migraine accuracy predictions and how well they forecast migraine presence early enough for preventative interventions. Furthermore, studies may be conducted to examine the model's ability to be employed in real-time patient monitoring and to identify actionable inputs from the algorithms. This work presents novel machine learning algorithms software for time series analysis of functions such as temperature, heart rate, and EEG indications, which can be used to identify migraines. The paper delves into the idea of utilizing machine learning algorithms to identify migraine styles, examines the pre-processing procedures to accurately arrange the indications, and provides the results of a study conducted to evaluate the efficacy of the solution. The observation's results show that the suggested diagnostic framework is capable of accurately identifying and categorizing migraines, enabling medical professionals to recognize the warning indications of migraine and predict when an attack would begin. The examination demonstrates the possibility of devices learning algorithms to correctly and accurately diagnose migraines, but more research is necessary to obtain more detailed information about this situation.
Keywords
Pre-Processing, Diagnosing Migraines, Machine Learning, Diverse Algorithms, Statistical Resources.
H. Göker, “Automatic detection of migraine disease from EEG signals using bidirectional long-short term memory deep learning model,” Signal, Image and Video Processing, vol. 17, no. 4, pp. 1255–1263, Aug. 2022, doi: 10.1007/s11760-022-02333-w.
Z. Aslan, “Deep Convolutional Neural Network-Based Framework in the Automatic Diagnosis of Migraine,” Circuits, Systems, and Signal Processing, vol. 42, no. 5, pp. 3054–3071, Dec. 2022, doi: 10.1007/s00034-022-02265-3.
N. Zhang et al., “Application of EEG in migraine,” Frontiers in Human Neuroscience, vol. 17, Feb. 2023, doi: 10.3389/fnhum.2023.1082317.
F. Orhanbulucu, F. Latifoğlu, and R. Baydemir, “A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect,” Diagnostics, vol. 13, no. 11, p. 1887, May 2023, doi: 10.3390/diagnostics13111887.
C. Jamunadevi, J. Bharanitharan, S. Deepa, and T. J. P. Antony, “Brain Tumor Prediction from EEG Signal using Machine Learning Algorithm,” 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Jul. 2023, doi: 10.1109/icesc57686.2023.10193098.
H. Göker and M. Tosun, “Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals,” Neural Computing and Applications, vol. 35, no. 32, pp. 23617–23630, Sep. 2023, doi: 10.1007/s00521-023-08971-6.
G. Yogarajan et al., “EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network,” Scientific Reports, vol. 13, no. 1, Oct. 2023, doi: 10.1038/s41598-023-44318-w.
S.-B. Wong, Y. Tsao, W.-H. Tsai, T.-S. Wang, H.-C. Wu, and S.-S. Wang, “Application of bidirectional long short-term memory network for prediction of cognitive age,” Scientific Reports, vol. 13, no. 1, Nov. 2023, doi: 10.1038/s41598-023-47606-7.
M. Parajuli, A. W. Amara, and M. Shaban, “Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson’s disease,” PLOS ONE, vol. 18, no. 8, p. e0286506, Aug. 2023, doi: 10.1371/journal.pone.0286506.
S. García-Ponsoda, J. García-Carrasco, M. A. Teruel, A. Maté, and J. Trujillo, “Feature engineering of EEG applied to mental disorders: a systematic mapping study,” Applied Intelligence, vol. 53, no. 20, pp. 23203–23243, Jul. 2023, doi: 10.1007/s10489-023-04702-5.
K. Mitrović, I. Petrušić, A. Radojičić, M. Daković, and A. Savić, “Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data,” Frontiers in Neurology, vol. 14, Jun. 2023, doi: 10.3389/fneur.2023.1106612.
N. G and M. V. Karki, “Deep Learning Techniques for Parkinson’s Detection Using EEG Signals Analysis,” 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Sep. 2023, doi: 10.1109/nmitcon58196.2023.10275844.
S. A. Saeedinia, M. R. Jahed-Motlagh, and A. Tafakhori, “Evaluating the Efficacy of EEG Features and Data Fusion in Migraine Diagnosis,” Aug. 2023, doi: 10.21203/rs.3.rs-3265602/v1.
C. Surianarayanan, J. J. Lawrence, P. R. Chelliah, E. Prakash, and C. Hewage, “Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review,” Sensors, vol. 23, no. 6, p. 3062, Mar. 2023, doi: 10.3390/s23063062.
H. Göker, “Welch Spectral Analysis and Deep Learning Approach for Diagnosing Alzheimer’s Disease from Resting-State EEG Recordings,” Traitement du Signal, vol. 40, no. 1, pp. 257–264, Feb. 2023, doi: 10.18280/ts.400125.
S. M. Dubey, B. Kanwer, G. Tiwari, and N. Sharma, “Classification for EEG Signals Using Machine Learning Algorithm,” Artificial Intelligence of Things, pp. 336–353, Dec. 2023, doi: 10.1007/978-3-031-48774-3_24.
Zulfikar Aslan, “A NEW COMPUTATIONAL APPROACH FRAMEWORK FOR THE DIAGNOSIS OF ALZHEIMER'S DISEASE,” The International Journal of Energy and Engineering Sciences, vol. 8. no. 1, pp.19-39, 2023.
N. Kasabov, “Diagnostic biomarker discovery from brain EEG data with LSTM, reservoir-SNN and NeuCube: Methods and a pilot study on epilepsy vs migraine,” Jun. 2023, doi: 10.36227/techrxiv.23514486.v1.
Y. Wang et al., “Progress of researches on machine learning combined with neuroimaging in the field of acupuncture,” Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), Aug. 2023, doi: 10.1117/12.2687519.
This work was supported by the National Research Foundation of Korea.
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT).(NO.NRF-2021R1A2C1012827) in (2023).
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Hye Kyeong Ko
Hye Kyeong Ko
Department of Computer Science and Engineering, Sungkyul University, Gyeonggi-do, Republic of Korea.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Hye Kyeong Ko, “Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms”, Journal of Machine and Computing, pp. 170-180, January 2024. doi: 10.53759/7669/jmc202404016.
