Abstract
The World Health Organization reported that more than 264 and 80 million patients worldwide suffer from depression and alcoholism, respectively. Depression and alcoholism might cause severe negative repercussions on a patient’s life and relationships, such as self-harm and suicide. A person can lead a normal life after these brain disorders are timely and accurately diagnosed and cured. In order to recognize the brain’s activity and identify different mental disorders, Electroencephalography (EEG) is often employed. The EEG signals in our study are separated into rhythms in the empirical wavelet transform domain, and then linear and nonlinear features are extracted. Significant features are selected by a feature selection method, and the output of the feature selection method is fed into a classifier. In this paper, a fast and effective diagnostic tool is proposed to detect and recognize depression and alcoholism disorders. The proposed diagnostic tool is built on the Salp Swarm Algorithm and the Tree Growth Algorithm as feature selection methods and Cascade Forward Neural Network and Feed-forward Neural Network classifiers. The diagnostic tool is evaluated on two datasets for depression and alcoholism, and the results show that the classification accuracies are 100% and 99.58% for depression and alcoholism, using 10-fold cross-validation strategy, respectively. The proposed diagnostic tool can be used in hospitals and clinics for fast and accurate detection of depression and alcoholism. In addition, we introduce a novel depression diagnostic index and alcoholism diagnostic index, which can be used as biomarkers for healthcare provider to diagnose depression and alcoholism without using machine learning approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H., Subha, D.P.: Automated EEG-based screening of depression using deep convolutional neural network. Comput. Methods Programs Biomed. 161, 103–113 (2018)
Acharya, U.R., Sree, S.V., Chattopadhyay, S., Suri, J.S.: Automated diagnosis of normal and alcoholic EEG signals. Int. J. Neural Syst. 22(03), 1250011 (2012)
Acharya, U.R., et al.: A novel depression diagnosis index using nonlinear features in EEG signals. Eur. Neurol. 74(1–2), 79–83 (2015)
Agrawal, P., Abutarboush, H.F., Ganesh, T., Mohamed, A.W.: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019). IEEE Access 9, 26766–26791 (2021)
Ahmadlou, M., Adeli, H., Adeli, A.: Fractality analysis of frontal brain in major depressive disorder. Int. J. Psychophysiol. 85(2), 206–211 (2012)
Akbari, H., Sadiq, M.T., Rehman, A.U.: Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Inf. Sci. Syst. 9, 1–15 (2021)
Akbari, H., et al.: Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. Appl. Acoust. 179, 108078 (2021)
Anuragi, A., Sisodia, D.S.: Empirical wavelet transform based automated alcoholism detecting using EEG signal features. Biomed. Signal Process. Control 57, 101777 (2020)
Anuragi, A., Sisodia, D.S., Pachori, R.B.: Automated alcoholism detection using Fourier-Bessel series expansion based empirical wavelet transform. IEEE Sens. J. 20(9), 4914–4924 (2020)
Bachmann, M., Lass, J., Suhhova, A., Hinrikus, H.: Spectral asymmetry and Higuchi’s fractal dimension measures of depression electroencephalogram. Comput. Math. Methods Med. 2013, 251638 (2013)
Bachmann, M., et al.: Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Comput. Methods Programs Biomed. 155, 11–17 (2018)
Bae, Y., Yoo, B.W., Lee, J.C., Kim, H.C.: Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism. Physiol. Meas. 38(5), 759 (2017)
Cai, H., Sha, X., Han, X., Wei, S., Hu, B.: Pervasive EEG diagnosis of depression using deep belief network with three-electrodes EEG collector. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1239–1246. IEEE (2016)
Dokeroglu, T., Deniz, A., Kiziloz, H.E.: A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing. 494, 269–296 (2022)
Farsi, L., Siuly, S., Kabir, E., Wang, H.: Classification of alcoholic EEG signals using a deep learning method. IEEE Sens. J. 21(3), 3552–3560 (2020)
Faust, O., Ang, P.C.A., Puthankattil, S.D., Joseph, P.K.: Depression diagnosis support system based on EEG signal entropies. J. Mech. Med. Biol. 14(03), 1450035 (2014)
Faust, O., Yu, W., Kadri, N.A.: Computer-based identification of normal and alcoholic EEG signals using wavelet packets and energy measures. J. Mech. Med. Biol. 13(03), 1350033 (2013)
Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput. Methods Programs Biomed. 109(3), 339–345 (2013)
Knott, V., Mahoney, C., Kennedy, S., Evans, K.: EEG power, frequency, asymmetry and coherence in male depression. Psych. Res. Neuroimaging 106(2), 123–140 (2001)
Korani, W., Mouhoub, M.: Review on nature-inspired algorithms. Oper. Res. Forum. 2, 1–26 (2021). https://doi.org/10.1007/s43069-021-00068-x
Liao, S.C., Wu, C.T., Huang, H.C., Cheng, W.T., Liu, Y.H.: Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors 17(6), 1385 (2017)
McHugh, R.K., Weiss, R.D.: Alcohol use disorder and depressive disorders. Alcohol Res. Curr. Rev. 40(1), 1–8 (2019)
Mehla, V.K., Singhal, A., Singh, P.: A novel approach for automated alcoholism detection using Fourier decomposition method. J. Neurosci. Methods 346, 108945 (2020)
Mumtaz, W., **a, L., Ali, S.S.A., Yasin, M.A.M., Hussain, M., Malik, A.S.: Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed. Signal Process. Control 31, 108–115 (2017)
Patidar, S., Pachori, R.B., Upadhyay, A., Acharya, U.R.: 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 (2017)
Prop**, P., Krüger, J., Mark, N.: Genetic disposition to alcoholism. An EEG study in alcoholics and their relatives. Human Genet. 59, 51–59 (1981)
Puthankattil, S.D., Joseph, P.K.: Classification of EEG signals in normal and depression conditions by ANN using RWE and signal entropy. J. Mech. Med. Biol. 12(04), 1240019 (2012)
Sadiq, M.T., Akbari, H., Siuly, S., Li, Y., Wen, P.: Alcoholic EEG signals recognition based on phase space dynamic and geometrical features. Chaos Solitons Fractals 158, 112036 (2022)
Sharma, M., Achuth, P., Deb, D., Puthankattil, S.D., Acharya, U.R.: An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals. Cogn. Syst. Res. 52, 508–520 (2018)
Sharma, M., Deb, D., Acharya, U.R.: A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals. Appl. Intell. 48, 1368–1378 (2018)
Sharma, M., Sharma, P., Pachori, R.B., Acharya, U.R.: Dual-tree complex wavelet transform-based features for automated alcoholism identification. Int. J. Fuzzy Syst. 20, 1297–1308 (2018)
Thilagaraj, M., Rajasekaran, M.P.: An empirical mode decomposition (EMD)-based scheme for alcoholism identification. Pattern Recogn. Lett. 125, 133–139 (2019)
Upadhyay, R., Padhy, P., Kankar, P.: Alcoholism diagnosis from EEG signals using continuous wavelet transform. In: 2014 Annual IEEE India Conference (INDICON), pp. 1–5. IEEE (2014)
Zhong, S., Ghosh, J.: HMMs and coupled HMMs for multi-channel EEG classification. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN 2002 (Cat. No. 02CH37290), vol. 2, pp. 1154–1159. IEEE (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Akbari, H., Korani, W. (2024). Early Detection of Depression and Alcoholism Disorders by EEG Signal. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-8141-0_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8140-3
Online ISBN: 978-981-99-8141-0
eBook Packages: Computer ScienceComputer Science (R0)