Document Type : Reseach Article
10.57647/j.mjee.2025.1901.14
Abstract
Heroin is a highly addictive drug with devastating effects on various parts of the body, including the digestive system, nervous system, and mental health, and it can lead to premature death. One of the most destructive impacts of heroin use is on the brain. Electroencephalograms (EEG) indicate the brain’s activity in the physiological and psychological states of heroin addicts. Identifying distinguishing features is crucial for processing these signals and determining the differences between the EEGs of healthy individuals and addicts. The frequency and time domain features extracted from different channels of EEG vary, but identifying distinguishing features can aid in better analysis of these signals. This article uses the Davies-Bouldin criterion to determine distinguishing frequency and time domain features. EEGs of heroin addicts (15 individuals) and healthy individuals (15 individuals) were extracted from 16 different channels. The distinguishing feature with the lowest Davies-Bouldin index value was selected. The results of this study show that in
people addicted to heroin, the frequency power spectrum in the upper alpha subband of the O1 channel has decreased. Additionally, approximate entropy is increased in the Cz channel of heroin addicts. To evaluate the distinguishing features, support vector machine classification has been used to distinguish addicts from healthy individuals. The sensitivity and accuracy of distinguishing an addicted person from a healthy person in the approximate entropy feature are 91.50% and 91.81%, respectively, and in the power spectrum feature in the upper alpha subband of the O1 channel, they are 95.92% and 92.40%, respectively. Compared to other studies, the obtained results confirm the distinction and superiority of these features in terms of precision and accuracy. According to the results, the analysis of frequency and time domain features of brain signals can help to better understand the effects of heroin consumption on brain activity. This study may help provide solutions to improve the treatment and prevention of heroin addiction.
Keywords
- [1] Corominas-Roso, I. Ibern, M. Capdevila, R. Ramon, C. Roncero, J. Ramos-Quiroga, “Benefits of EEG-neurofeedback on the modulation of impulsivity in a sample of cocaine and heroin long-term abstinent inmates: a pilot study”, International Journal of Offender Therapy and Comparative Criminology, vol. 64, no. 12, pp. 1275-1298, 2020, https://doi.org/10.1177/0306624X2090470.4.
- [2] Marvi, J. Haddadnia, M.R.F. Bordbar, “Evaluation of drug abuse on brain function using power spectrum analysis of electroencephalogram signals in methamphetamine, opioid, cannabis, and multi-drug abuser groups”, Journal of Biomedical Physics and Engineering, vol. 13, no. 2, pp. 181-192, March/April 2023,https://doi.org/10.31661/jbpe.v0i0.2210-1550.
- [3] R. Yousefi, A. Dehghani, H. Taghaavifar. "Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks." Frontiers in Human Neuroscience vol 17, Article Number 1174104,2023.
- [4] N. Dar, M.U. Akram, R. Yuvaraj, S.G. Khawaja, M. Murugappan, “EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning”, Computers in Biology and Medicine, vol. 144, Article Number: 105327, 2022, https://doi.org/10.1016/j.compbiomed.2022.105327.
- [5] M. Hira, D.F. Gillies, “A review of feature selection and feature extraction methods applied on microarray data”, Advances in Bioinformatics, vol. 2015, Article Number: 98363, June 2015, https://doi.org/10.1155/2015/198363.
- [6] V. Wankar, P. Shah, R. Sutar, "Feature extraction and selection methods for motor imagery EEG signals: A review", Proceeding of the IEEE/I2C2, pp. 1-9, Coimbatore, India, June 2017, doi: 10.1109/I2C2.2017.8321831.
- [7] Bolón-Canedo, N. Sánchez-Maroño, A. Alonso-Betanzos, J.M. Benítez, F. Herrera, “A review of microarray datasets and applied feature selection methods”, Information Sciences, vol. 282, pp. 111-135, Oct. 2014, https://doi.org/10.1016/j.ins.2014.05.042.
- [8] Chen, Y. Cai, A. Li, Y. Su, K. Jiang, “EEG feature selection method based on maximum information coefficient and quantum particle swarm”, Scientific Reports, vol. 13, Article Number: 14515, Sept. 2023, https://doi.org/10.1038/s41598-023-41682-5.
- [9] Sharma, H.K. Meena, "Emerging trends in EEG signal processing: A systematic review", SN Computer Science, vol. 5, no. 4, Article Number: 415, April 2024, https://doi.org/10.1007/s42979-024-02773-w.
- Jain, R. Raja, S. Srivastava, P.C. Sharma, J. Gangrade, R. Manoj, "Analysis of EEG signals and data acquisition methods: a review", Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, vol. 12, no. 1, Feb. 2024, https://doi.org/10.1080/21681163.2024.2304574.
- Aziz, J. Dirkaoui, M. Ertel, M. Chakkouch, F. Elomari, "The contribution of numerical EEG analysis for the study and understanding of addictions with substances", International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, 2023, https://doi.org/10.14569/IJACSA.2023.0140534.
- Pandria, L. Kovatsi, A.B. Vivas, P.D. Bamidis. "Resting-state abnormalities in heroin-dependent individuals", Neuroscience, vol. 378, pp. 113-145, May 2018, https://doi.org/10.1016/j.neuroscience.2016.11.018.
- Montazeri, M. R., Yousefi, K, Shojaei, & G. Shahgholianm, “Fast adaptive fuzzy terminal sliding mode control of synergistic movement of the hip and knee joints (air-stepping) using functional electrical stimulation: A simulation study”, Biomedical Signal Processing and Control, vol. 66, pp. 102445, https://doi.org/10.1016/j.bspc.2021.102445.
- T, Zeng, S. Li, L. Wu, Z. Feng, X. Fan, J. Yuan, X. Wang, Meng, H. Ma, G. Zeng, C. Kang, J. Yang, "A Comparison study of impulsiveness, cognitive function, and P300 components between gamma-hydroxybutyrate and heroin-addicted patients: preliminary findings", Frontiers in Human Neuroscience, vol. 16, Article Number: 16:835922, April 2022, https://doi.org/10.3389/fnhum.2022.835922.
- Zeng, B. Yang, X. Gu, Y. Li, X. Xia, S. Gao. "CNN-based EEG classification method for drug use detection", Proceedings of the ICCPR, pp. 418-423, Beijing China, Nov. 2022, https://doi.org/10.1145/3581807.3581868.
- Liang, Y. Hao, Z. Xu, N. Li and Q. Zhao, "Identifying abstinent heroin addicts on the basis of single channel’s ERP and behavioral data in the gambling task", Proceeding of the IEEE/BIBM, pp. 650-656, Seoul, Korea (South), Dec. 2020, doi: 10.1109/BIBM49941.2020.9313164.
- Y. Wang, R. Kydd, T.A. Wouldes, M. Jensen, B.R. Russell, “Changes in resting EEG following methadone treatment in opiate addicts”, Clinical Neurophysiology, vol. 126, no. 5, pp. 943-950, 2015, https://doi.org/10.1016/j.clinph.2014.08.021.
- Shourie, M. Firoozabadi, K. Badie, “Neurofeedback training protocols based on spectral EEG feature subset and channel selection for performance enhancement of novice visual artists”, Biomedical Signal Processing and Control, vol. 43, pp. 117-129, 2018, https://doi.org/10.1016/j.bspc.2018.02.017.
- Jurewicz, K. Paluch, E. Kublik, J. Rogala, M. Mikicin, A. Wróbel, “EEG-neurofeedback training of beta band (12–22 Hz) affects alpha and beta frequencies–A controlled study of a healthy population”, Neuropsychologia, vol. 108, pp. 13-24, 2018, https://doi.org/10.1016/j.neuropsychologia.2017.11.021.
- H. Franken, C.J. Stam, V.M. Hendriks, W.V.D. Brink, “Neurophysiological evidence for abnormal cognitive processing of drug cues in heroin dependence”, Psychopharmacology, vol. 170, pp. 205-212, 2003, doi: https://doi.org/10.1007/s00213-003-1542-7.
- Hu, Q. Dong, Y. Hao, Q. Zhao, J. Shen, F. Zheng, “Effective brain network analysis with resting-state EEG data: A comparison between heroin abstinent and non-addicted subjects”, Journal of Neural Engineering, vol. 14, no. 4, Article Paper: 046002, May 2017, doi: 10.1088/1741-2552/aa6c6f.
- M. Liu, M. Lucas, F. Badami, W. Wu, A. Etkin, T.F. Yuan, "Cortical plasticity differences in substance use disorders", Fundamental Research, 2023, https://doi.org/10.1016/j.fmre.2023.02.015.
- Turnip, K.D. Esti, M.F. Amri, A.I. Simbolon, M.A. Suhendra, S. IsKandar, F.F. Wirakusumah, "Detection of drug effects on brain activity using EEG-P300 with similar stimuli”, IOP Conference Series: Materials Science and Engineering, Article Number: 012042, June 2017, doi: 10.1088/1757-899X/220/1/012042.
- Motlagh, F. Ibrahim, R. Rashid, T. Seghatoleslam, H. Habil, “Investigation of brain electrophysiological properties among heroin addicts: quantitative eeg and event‐related potentials”, Journal of Neuroscience Research, vol. 95, no. 8, pp. 1633-1646, 2017, doi: https://doi.org/10.1002/jnr.23988.
- Turnip, S.M. Agung, E.K. Dwi, L.S. Faza, W.L. Simon, A.S. Siti, N.I. Arifah, S.P. Daniel," Methadone effects on frontal brain lobe based EEG-P300 waves in drug rehabilitation patients", Proceedings of the ICAE, pp. 5-11, Jan. 2020, https://doi.org/10.5220/0010350600050011.
- Motlagh, F. Ibrahim, R. Rashid, N. Shafiabady, T. Seghatoleslam, H. Habil, “Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents”, Psychopharmacology, vol. 235, pp. 3273-3288, 2018, https://doi.org/10.1007/s00213-018-5035-0.
- Y. Badr, E.A. Gad, A.A. Mubarak, Y.A. El-Heneedy, A.M. Ibrahim, A.A.E. Belal, F.A.E. Deep, "Impact of tramadol and heroin abuse on electroencephalography structure and cognitive functions", Middle East Current Psychiatry, vol. 30, no. 1, Article Number: 92, Nov. 2023, https://doi.org/10.1186/s43045-023-00365-7.
- Wang, R. Peng, Q. Liu, H. Peng, “A hybrid classification to detect abstinent heroin-addicted individuals using EEG microstates”, IEEE Trans. on Computational Social Systems, vol. 9, no. 3, pp. 700-709, 2022, doi: 10.1109/TCSS.2021.3135425.
- H. Franken, C.J. Stam, V. M. Hendriks, W.V.D. Brink, “Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients”, Neuropsychobiology, vol. 49, no. 2, pp. 105-110, 2004, https://doi.org/10.1159/000076419.
- Luo, R. Yang, W. Yang, C. Duan, Y. Deng, J. Zhang, J. Chen, J. Liu, “Increased amplitude of low-frequency fluctuation in right angular gyrus and left superior occipital gyrus negatively correlated with heroin use”, Frontiers in Psychiatry, vol. 11, pp. 492, 2020, doi: https://doi.org/10.3389/fpsyt.2020.00492.
- M. Davydov, A.G. Polunina, “Heroin abusers' performance on the Tower of London Test relates to the baseline EEG alpha2 mean frequency shifts”, Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 28, no. 7, pp. 1143-1152, 2004, doi: https://doi.org/10.1016/j.pnpbp.2004.06.006.
- G. Polunina, D.M. Davydov, “EEG spectral power and mean frequencies in early heroin abstinence”, Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 28, no. 1, pp. 73-82, 2004, https://doi.org/10.1016/j.pnpbp.2003.09.022..
- Seif, M.R. Yousefi, N. Behzadfar, “EEG Spectral Power Analysis: A Comparison Between Heroin Dependent and Control Groups”, Clinical EEG and Neuroscience, vol. 53, no. 4, pp. 307-315, 2022, doi: https://doi.org/10.1177/15500594221089366.
- N.S. Kbah, N.K. Al-Qazzaz, S.H. Jaafer, M.K. Sabir, "Epileptic EEG activity detection for children using entropy-based biomarkers", Neuroscience Informatics, vol. 2, no. 4, Article Number: 100101, Dec. 2022, https://doi.org/10.1016/j.neuri.2022.100101.
- Cacciotti, C. Pappalettera, F. Miraglia, P.M. Rossini, F. Vecchio, "EEG entropy insights in the context of physiological aging and Alzheimer’s and Parkinson’s diseases: A comprehensive review", GeroScience, pp. 1-21, May 2024, https://doi.org/10.1007/s11357-024-01185-1.
- Bandt, “A new kind of permutation entropy used to classify sleep stages from invisible EEG microstructure”, Entropy, vol. 19, no. 5, pp. 197, 2017, doi: https://doi.org/10.3390/e19050197.
- S. Amer, S.B. Belhaouari, "EEG signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review", IEEE Access, vol. 11, pp. 143116-143142, Dec. 2023, doi: 10.1109/ACCESS.2023.3341419.
- R. Acharya, H. Fujita, V.K. Sudarshan, S. Bhat, J.E. Koh, “Application of entropies for automated diagnosis of epilepsy using EEG signals: A review” Knowledge-based systems, vol. 88, pp. 85-96, 2015, https://doi.org/10.1016/j.knosys.2015.08.004.
- S. Marks, K.V. Saboo, Ç. Topçu, M. Lech, T.P. Thayib, P. Nejedly, V. Kremen, G.A. Worrell, M.T. Kucewicz, “Independent dynamics of low, intermediate, and high frequency spectral intracranial EEG activities during human memory formation”, NeuroImage, vol. 245, Article Number: 118637, 2021, https://doi.org/10.1016/j.neuroimage.2021.118637.
- R. Yousefi, A. Dehghani, S. Golnejad, M. M. Hosseini, “Comparing EEG-based epilepsy diagnosis using neural networks and wavelet transform.” Applied Sciences, vol. 13, no. 18, Article Number 10412, 2023.
- Moaveninejad, V. D'Onofrio, F. Tecchio, F. Ferracuti, S. Iarlori, A. Monteriù, C. Porcaro, "Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface", Computer Methods and Programs in Biomedicine, vol. 244, Article Number: 107944, Feb. 2024, https://doi.org/10.1016/j.cmpb.2023.107944.
- Puri, S. Nalbalwar, A. Nandgaonkar, A. Wagh. "EEG-based diagnosis of alzheimer's disease using kolmogorov complexity", Advances in Intelligent Systems and Computing, vol. 1354, pp. 157-165, July 2021, https://doi.org/10.1007/978-981-16-2008-9_15.
- Lal, A.V. Chikkankod, L. Longo, “Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography”, Neural Comput and Applic, vol. 36, pp. 8257–8280, 2024, https://doi.org/10.1007/s00521-024-09521-4.
- Motamedi-Fakhr, M. Moshrefi-Torbati, M. Hill, C.M. Hill, P.R. White, “Signal processing techniques applied to human sleep EEG signals- A review”, Biomedical Signal Processing and Control, vol. 10, pp. 21-33, 2014, https://doi.org/10.1016/j.bspc.2013.12.003.
- Liu, Y. Chen, G. Fraga-González, V. Szpak, J. Laverman, R.W. Wiers, K.R. Ridderinkhof, "Resting-state EEG, substance use and abstinence after chronic use: A systematic review", Clinical EEG and Neuroscience, vol. 53, no. 4, pp. 344-366, July 2022, https://doi.org/10.1016/j.pscychresns.2022.111447.
- Alper, R.J. Chabot, A.H. Kim, L.S. Prichep, E.R. John, "Quantitative EEG correlates of crack cocaine dependence", Psychiatry Research: Neuroimaging, vol. 35, no. 2, pp. 95-105, Dec. 1990, https://doi.org/10.1016/0925-4927(90)90013-V.
- Shen, H. Niu, Q. Xia, B. Zou, Y. Zheng, Y. Zhao, L. Li, X. Liu, L. Zhang, "Automatic epileptic tissue localization through spatial pattern clustering of high frequency activity", IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 981-990, 2023, https://doi.org/10.1109/TNSRE.2023.3237226.
- Talukdar, S.M. Hazarika, J.Q. Gan, "Adaptation of Common Spatial Patterns based on mental fatigue for motor-imagery BCI", Biomedical Signal Processing and Control, vol. 58, Article Number: 101829, April 2020, https://doi.org/10.1016/j.bspc.2019.101829.
- Tobeiha, N. Behzadfar, M.R. Yousefi-Najafabadi, H. Mahdavi-Nasab, G. Shahgholian, "Choosing the distinguishing frequency feature of people addicted to heroin from healthy while resting", Signal and Data Processing, vol. 19, no. 3, pp. 49-64, Dec. 2022, https://doi.org/10.52547/jsdp.19.3.49.
- H. Franken, C.J. Stam, V. M. Hendriks, W.V.D. Brink, "Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients", Neuropsychobiology, vol. 49, no. 2, pp. 105-110, June 2004, https://doi.org/10.1159/000076419.
- Luo, R. Yang, W. Yang, C. Duan, Y. Deng, J. Zhang, J. Chen, J. Liu, “Increased amplitude of low-frequency fluctuation in right angular gyrus and left superior occipital gyrus negatively correlated with heroin use”, Frontiers in Psychiatry, vol. 11, Article Number: 492, 2020, https://doi.org/ https://doi.org/10.3389/fpsyt.2020.00492.
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