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Automatic detection of migraine disease from EEG signals using bidirectional long-short term memory deep learning model

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Abstract

Migraine is a neurological disease defined by recurrent attacks of headache accompanied by nausea and vomiting, which causes autonomic nervous system disturbance, episodes of severe pain, are prolonged, and can have a major impact on quality of life. In this study, a deep learning model was proposed to assist expert judgment in the automatic detection of migraine using electroencephalography (EEG) signals. The dataset consists of EEG signals recorded from 21 healthy and 18 migraine volunteers. Feature vectors were created by calculating the power densities values of the frequencies between 1 and 49 Hz of the EEG signals using the Welch method. The performances of bidirectional long-short term memory (BiLSTM) deep learning algorithm and random forest, support vector machine, and linear discriminant analysis machine learning algorithms in EEG-based migraine classification tasks were compared using the created feature vectors. The algorithm with the highest performance is the BiLSTM (95.99%) deep learning algorithm using 128 channels. The study is a rare attempt in which a deep learning model is used in the effective diagnosis of automatic migraine by analyzing multi-channel EEG signals and provides evidence for the superiority of deep learning algorithms. In comparison to state-of-the-art investigations, more accuracy was achieved. With this model, the accuracy was increased by 6.3%.

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Data availability

The EEG signals used in this study are publicly available datasets. The dataset was downloaded from: “https://kilthub.cmu.edu/articles/dataset/Ultra_high-density_EEG_recording_of_interictal_migraine_and_ controls_sensory_and_rest/12636731" [14].

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Electrical Electronics Engineering, Faculty of Simav Technology, Kutahya Dumlupinar University, Kutahya, 43500, Turkey

    Hanife Göker

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  1. Hanife Göker
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HG: designing concepts of the study, application of data analysis and interpretation, implementation of methods and writing the manuscript.

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Correspondence to Hanife Göker.

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Göker, H. Automatic detection of migraine disease from EEG signals using bidirectional long-short term memory deep learning model. SIViP 17, 1255–1263 (2023). https://doi.org/10.1007/s11760-022-02333-w

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  • DOI: https://doi.org/10.1007/s11760-022-02333-w

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