Abstract
Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.
Similar content being viewed by others
Data availability
The data used in this study are taken from the publicly available data set. Data set is available at “https://kilthub.cmu.edu/articles/dataset/Ultra_highdensity_EEG_recording_of_interictal_migraine_and_controls_sensory_and_rest/12636731” [19].
References
Foundation MR (2021) About Migraine (Migraine Research Foundation) Retrieved. https://migraineresearchfoundation.org/about-migraine/
Trust TM (2021) Facts and figures. https://www.migrainetrust.org/about-migraine/migraine-what-is-it/facts-figures/
Trust TM. Diagnosis. https://www.migrainetrust.org/living-with-migraine/seeking-medical-advice/diagnosis/#:~:text=There is no actual test,or other symptoms is taken
Al Ghayab HR, Li Y, Siuly S, Abdulla S (2019) A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification. J Neurosci Methods 312:43–52. https://doi.org/10.1016/j.jneumeth.2018.11.014
Yin Z, Dong Z, Lu X, Yu S, Chen X, Duan H (2015) A clinical decision support system for the diagnosis of probable migraine and probable tension-type headache based on case-based reasoning. J Headache Pain 16(1):1–9
Krawczyk B, Simić D, Simić S, Woźniak M (2013) Automatic diagnosis of primary headaches by machine learning methods. Cent Eur J Med 8(2):157–165. https://doi.org/10.1186/s10194-015-0512-x
Akben SB, Tuncel D, Alkan A (2016) Classification of multi-channel EEG signals for migraine detection. Biomed Res 27(3):743–748
Subasi A, Ahmed A, Aličković E, Hassan AR (2019) Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform. Biomed Signal Process Control 49:231–239. https://doi.org/10.1016/j.bspc.2018.12.011
Akben SB, Subasi A, Tuncel D (2012) Analysis of repetitive flash stimulation frequencies and record periods to detect migraine using artificial neural network. J Med Syst 36(2):925–931. https://doi.org/10.1007/s10916-010-9556-2
Liu J, Zhang C, Zhu Y, Ristaniemi T, Parviainen T, Cong F (2020) Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition. Comput Methods Programs Biomed 184:105120. https://doi.org/10.1016/j.cmpb.2019.105120
Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y (2021) A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft Comput 25(6):4571–4595. https://doi.org/10.1007/s00500-020-05465-8
Murugappan M, Alshuaib W, Bourisly AK, Khare SK, Sruthi S, Bajaj V (2020) Tunable Q wavelet transform based emotion classification in Parkinson’s disease using electroencephalography. PLoS One 15(11):e0242014. https://doi.org/10.1371/journal.pone.0242014
Patidar S, Pachori RB, Upadhyay A, Acharya UR (2017) An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Appl Soft Comput 50:71–78. https://doi.org/10.1016/j.asoc.2016.11.002
Patidar S, Panigrahi T (2017) Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control 34:74–80. https://doi.org/10.1016/j.bspc.2017.01.001
Patidar S, Pachori RB, Acharya UR (2015) Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowl Based Syst 82:1–10. https://doi.org/10.1016/j.knosys.2015.02.011
Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 137:247–259. https://doi.org/10.1016/j.cmpb.2016.09.008
Taran S, Bajaj V (2019) Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform. Neural Comput Appl 31(11):6925–6932. https://doi.org/10.1007/s00521-018-3531-0
Bajaj V, Taran S, Khare SK, Sengur A (2020) Feature extraction method for classification of alertness and drowsiness states EEG signals. Appl Acoust 163:107224. https://doi.org/10.1016/j.apacoust.2020.107224
Chaman Zar M, Haigh A, Grover S, Behrmann P (2020) Ultra high-density EEG recording of interictal migraine and controls: sensory and rest. Carnegie Mellon University. Dataset
Bakshi BR (1998) Multiscale PCA with application to multivariate statistical process monitoring. AIChE J 44(7):1596–1610. https://doi.org/10.1002/aic.690440712
Selesnick IW (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560–3575. https://doi.org/10.1109/TSP.2011.2143711
Patidar S, Pachori RB (2014) Classification of cardiac sound signals using constrained tunable-Q wavelet transform. Expert Syst Appl 41(16):7161–7170. https://doi.org/10.1016/j.eswa.2014.05.052
Khare SK, Bajaj V (2020) Constrained based tunable Q wavelet transform for efficient decomposition of EEG signals. Appl Acoust 163:107234. https://doi.org/10.1016/j.apacoust.2020.107234
He W, Zi Y, Chen B, Wu F, He Z (2015) Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mech Syst Signal Process 54:457–480. https://doi.org/10.1016/j.ymssp.2014.09.007
Huang H, Baddour N, Liang M (2017) Auto-OBSD: automatic parameter selection for reliable oscillatory behavior-based signal decomposition with an application to bearing fault signature extraction. Mech Syst Signal Process 86:237–259. https://doi.org/10.1016/j.ymssp.2016.10.007
Subasi A, Tuncer T, Dogan S, Tanko D, Sakoglu U (2021) EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier. Biomed Signal Process Control 68:102648. https://doi.org/10.1016/j.bspc.2021.102648
Narkhede S (2021) Understanding AUC-ROC curve. https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5
Wang Y-H, Yeh C-H, Young H-WV, Hu K, Lo M-T (2014) On the computational complexity of the empirical mode decomposition algorithm. Physica A Stat Mech Its Appl 400:159–167. https://doi.org/10.1016/j.physa.2014.01.020
Jackowski K, Jankowski D, Simić D, Simić S (2014) Migraine diagnosis support system based on classifier ensemble. In: International conference on ICT innovations, 2014, pp 329–339
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Aslan, Z. Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques. Phys Eng Sci Med 44, 1201–1212 (2021). https://doi.org/10.1007/s13246-021-01055-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-021-01055-6