Identification of normal and depression EEG signals in variational mode decomposition domain
Tóm tắt
Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system.
Tài liệu tham khảo
World health organization depression key facts (2021). https://www.who.int/news-room/fact-sheets/detail/depression
Sadiq MT, Yu X, Yuan Z, Aziz MZ. Identification of motor and mental imagery eeg in two and multiclass subject-dependent tasks using successive decomposition index. Sensors. 2020;20(18):5283.
Yu X, Hu C, Zhang W, Zhou J, Ding Q, Sadiq MT, Fan Z, Yuan Z, Liu L. Feasibility evaluation of micro-optical coherence tomography (\(\mu \)oct) for rapid brain tumor type and grade discriminations: \(\mu \)oct images versus pathology. BMC Med Imaging. 2019;19(1):1–12.
Jafri GA, Rehman AU, Sadiq MT. Spectrum sensing and management in cooperative cognitive radio (2011).
Sadiq MT, Yu X, Yuan Z, Fan Z, Rehman AU, Li G, Xiao G. Motor imagery eeg signals classification based on mode amplitude and frequency components using empirical wavelet transform. IEEE Access. 2019;7:127678–92.
Şengür D, Siuly S. Efficient approach for eeg-based emotion recognition. Electron Lett. 2020;56(25):1361–4.
Akbari H, Sadiq MT. Detection of focal and non-focal eeg signals using non-linear features derived from empirical wavelet transform rhythms. Phys Eng Sci Med. 2021;44(1):157–71.
Sadiq MT, Akbari H, Rehman AU, Nishtar Z, Masood B, Ghazvini M, Too J, Hamedi N, Kaabar MKA. Exploiting feature selection and neural network techniques for identification of focal and nonfocal eeg signals in tqwt domain. J Healthcare Eng. 2021;2021:24.
Sadiq MT, Yu X, Yuan Z, Zeming F, Rehman AU, Ullah I, Li G, Xiao G. Motor imagery eeg signals decoding by multivariate empirical wavelet transform-based framework for robust brain-computer interfaces. IEEE Access. 2019;7:171431–51.
Sadiq MT, Aziz MZ, Almogren A, Yousaf A, Siuly S, Rehman AU. Exploiting pretrained cnn models for the development of an eeg-based robust bci framework. Comput Biol Med. 2022;1: 105242.
Durrani A M F, Rehman AU, Farooq A, Meo JA, Sadiq MT. An automated waste control management system (awcms) by using arduino. In: 2019 international conference on engineering and emerging technologies (ICEET). IEEE, 2019, pp. 1–6.
Sadiq MT, Akbari H, Siuly S, Yousaf A, Rehman AU. A novel computer-aided diagnosis framework for eeg-based identification of neural diseases. Comput Biol Med. 2021;138: 104922.
Akbari H, Sadiq MT, Siuly S, Li Y, Wen P. An automatic scheme with diagnostic index for identification of normal and depression EEG signals. In: International conference on health information science. Springer, 2021, pp. 59–70.
Sadiq MT, Siuly S, Rehman AU, Wang H. Auto-correlation based feature extraction approach for EEG alcoholism identification. In: International conference on health information science. Springer, 2021, pp. 47–58.
Akbari H, Ghofrani S, Zakalvand P, Sadiq MT. Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomed Signal Process Control. 2021;69: 102917.
Sadiq MT, Yu X, Yuan Z, Aziz MZ, Siuly S, Ding W. Toward the development of versatile brain-computer interfaces. IEEE Trans Artif Intell. 2021;2(4):314–28.
Sadiq MT, Yu X, Yuan Z. Exploiting dimensionality reduction and neural network techniques for the development of expert brain-computer interfaces. Expert Syst Appl. 2021;164: 114031.
Sadiq MT, Yu X, Yuan Z, Aziz MZ. Motor imagery bci classification based on novel two-dimensional modelling in empirical wavelet transform. Electron Lett. 2020;56(25):1367–9.
Puthankattil SD, Joseph PK. Classification of eeg signals in normal and depression conditions by ann using rwe and signal entropy. J Mech Med Biol. 2012;12(04):1240019.
Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol. 2012;85(2):206–11.
Ahmadlou M, Adeli H, Adeli A. Spatiotemporal analysis of relative convergence of eegs reveals differences between brain dynamics of depressive women and men. Clin EEG Neurosci. 2013;44(3):175–81.
Faust O, Ang PCA, Puthankattil SD, Joseph PK. Depression diagnosis support system based on eeg signal entropies. J Mech Med Biol. 2014;14(03):1450035.
Akbari H, Sadiq MT, Rehman AU. Classification of normal and depressed eeg signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Inf Sci Syst. 2021;9(1):1–15.
Akbari H, Sadiq MT, Payan M, Esmaili SS, Baghri H, Bagheri H. Depression detection based on geometrical features extracted from sodp shape of eeg signals and binary pso. Traitement Signal. 2021;38:1.
Akbari H, Sadiq MT, Rehman AU, Ghazvini M, Naqvi RA, Payan M, Bagheri H, Bagheri H. Depression recognition based on the reconstruction of phase space of eeg signals and geometrical features. Appl Acoust. 2021;179: 108078.
Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Puthankatti SD, Adeli A. A novel depression diagnosis index using nonlinear features in eeg signals. Eur Neurol. 2015;74(1–2):79–83.
Mumtaz W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS. Electroencephalogram (eeg)-based computer-aided technique to diagnose major depressive disorder (mdd). Biomed Signal Process Control. 2017;31:108–15.
Bairy GM, Lih OS, Hagiwara Y, Puthankattil SD, Faust O, Niranjan U, Acharya UR. Automated diagnosis of depression electroencephalograph signals using linear prediction coding and higher order spectra features. J Med Imaging Health Inf. 2017;7(8):1857–62.
Bachmann M, Lass J, Suhhova A, Hinrikus H. Spectral asymmetry and Higuchi’s fractal dimension measures of depression electroencephalogram. Comput Math Methods Med. 2013;2013:1.
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated eeg-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed. 2018;161:103–13.
Sharma M, Achuth P, Deb D, Puthankattil SD, Acharya UR. An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with eeg signals. Cogn Syst Res. 2018;52:508–20.
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. 2017;17(6):1385.
Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process. 2013;62(3):531–44.
Bhattacharyya A, Pachori RB. A multivariate approach for patient-specific eeg seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng. 2017;64(9):2003–15.
Yu X, Aziz MZ, Sadiq MT, Fan Z, Xiao G. A new framework for automatic detection of motor and mental imagery eeg signals for robust bci systems. IEEE Trans Instrum Meas. 2021;70:1–12.
Sadiq MT, Yu X, Yuan Z, Aziz MZ, Siuly S, Ding W. A matrix determinant feature extraction approach for decoding motor and mental imagery eeg in subject specific tasks. IEEE Trans Cogn Dev Syst. 2020;1:1–1.
Hussain W, Sadiq MT, Siuly S, Rehman AU. Epileptic seizure detection using 1 d-convolutional long short-term memory neural networks. Appl Acoust. 2021;177: 107941.
Fan Z, Jamil M, Sadiq MT, Huang X, Yu X. Exploiting multiple optimizers with transfer learning techniques for the identification of covid-19 patients. J Healthcare Eng. 2020;2020:1.
Akhter MP, Jiangbin Z, Naqvi IR, Abdelmajeed M, Sadiq MT. Automatic detection of offensive language for urdu and roman urdu. IEEE Access. 2020;8:91213–26.
Akhter MP, Jiangbin Z, Naqvi IR, Abdelmajeed M, Mehmood A, Sadiq MT. Document-level text classification using single-layer multisize filters convolutional neural network. IEEE Access. 2020;8:42689–707.