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EEG-based deep learning model for the automatic detection of clinical depression

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Abstract

Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the EEG of a depressed individual. There are several strategies for automated depression diagnosis, but they all have flaws, which make the diagnostic task inaccurate. In this paper, a deep model is designed in which an integration of Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is implemented for the detection of depression. CNN and LSTM are used to learn the local characteristics and the EEG signal sequence, respectively. In the deep learning model, filters in the convolution layer are convolved with the input signal to generate feature maps. All the extracted features are given to the LSTM for it to learn the different patterns in the signal, after which the classification is performed using fully connected layers. LSTM has memory cells to remember the essential features for a long time. It also has different functions to update the weights during training. Testing of the model was done by random splitting technique and obtained 99.07% and 98.84% accuracies for the right and left hemispheres EEG signals, respectively.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India

    Pristy Paul Thoduparambil, Anna Dominic & Surekha Mariam Varghese

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  1. Pristy Paul Thoduparambil
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  2. Anna Dominic
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  3. Surekha Mariam Varghese
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Correspondence to Pristy Paul Thoduparambil.

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Data used in this research is taken from the database made available under the Public Domian Dedication and License v1.0, as part of a Brain Initiative Seed Award supported by the University of New Mexico. All participants provided written informed consent that was approved by the University of Arizona.

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Thoduparambil, P.P., Dominic, A. & Varghese, S.M. EEG-based deep learning model for the automatic detection of clinical depression. Phys Eng Sci Med 43, 1349–1360 (2020). https://doi.org/10.1007/s13246-020-00938-4

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