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Deep Convolutional Neural Network-Based Framework in the Automatic Diagnosis of Migraine

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Abstract

This study presents a computer-aided diagnosis (CAD) system that uses deep learning to diagnose migraine from electroencephalogram (EEG) signals. The proposed method converts EEG signals into scalogram images using the continuous wavelet transform method. Afterward, these scalogram images were given as input to the proposed convolutional neural network (CNN) deep learning network. With the proposed CNN deep learning model, 100% classification success has been achieved with very high accuracy compared to other related literature studies. Comparative analyses of the classification success of the proposed method and the classification performances obtained using state-of-the-art CNN models (VGG16, DenseNet121, ResNET101, and Xception) are presented. It has been observed that this study is superior to state-of-the-art CNN deep learning models compared with low computational complexity, simple network structure (less depth), and high classification performance. In addition, interpretable Gradient-weighted Class Activation Mapping (GradCAM) images were obtained to support the specialist in the diagnosis of migraine. In the study, it is revealed that there is a relationship between EEG recordings and migraine disease in terms of frequency components. When interpretable GradCAM images are examined, it is seen that mid- and high-frequency components are essential in distinguishing between migraine and healthy individuals. Considering the mentioned aspects, it is seen that this study has the potential to be used as a CAD system that can contribute to the expert opinion in distinguishing migraine patients and healthy controls.

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Availability of Data and Material

The data used in this study are taken from the publicly available data set. The data set is available at "https://kilthub.cmu.edu/articles/dataset/Ultra_highdensity_EEG_recording_of_interictal_migraine_ and controls_sensory_and_rest/12636731" [13].

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Acknowledgements

This study was supported by Gaziantep University Scientific Research Projects Commission. (Project No.: GTBMYO.GAP.22.04).

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  1. Technical Sciences Vocational School, Gaziantep University, Gaziantep, 27310, Turkey

    Zülfikar Aslan

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Aslan, Z. Deep Convolutional Neural Network-Based Framework in the Automatic Diagnosis of Migraine. Circuits Syst Signal Process 42, 3054–3071 (2023). https://doi.org/10.1007/s00034-022-02265-3

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