ArticlePhysical and engineering sciences in medicine2021
Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques.
Article in Physical and engineering sciences in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A novel approach for migraine detection using localized component filtering and electroencephalographic spectral asymmetry index.Current journal of neurology · 2024Article
- A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.PloS one · 2024Article
- A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect.Diagnostics (Basel, Switzerland) · 2023Article
- Application of EEG in migraine.Frontiers in human neuroscience · 2023Review
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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.
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