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Year 2024, Volume: 42 Issue: 3, 795 - 804, 12.06.2024

Abstract

References

  • Reference List
  • 1- Aboalayon KAI, Faezipour M, Almuhammadi WS, Moslehpour S. Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation. Entropy 2016;18:272.
  • 2- Patel AK, Reddy V, Shumway KR, Araujo JF. Physiology, sleep stages. Available at: https://www.ncbi.nlm.nih.gov/books/NBK526132/. Accessed on May 13, 2024.
  • 3- Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, et al. A pervasive approach to EEG-based depression detection. Complexity 2018;2018:5238028.
  • 4- Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A. Computer-aided diagnosis of depression using EEG signals. Eur Neurol 2015;73:329–336.
  • 5- Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A. Computer-aided diagnosis of depression using EEG signals. Eur Neurol 2015;73:329–336.
  • 6- Bachmann M, Lass J, Hinrikus H. Single channel EEG analysis for detection of depression. Biomed Signal Process Control 2017;31:391–397.
  • 7- Deslandes AC, de Moraes H, Pompeu FA, Ribeiro P, Cagy M, Capitão C, et al. Electroencephalographic frontal asymmetry and depressive symptoms in the elderly. Biol Psychol 2008;79:317– 322.
  • 8- Hinrikus H, Suhhova A, Bachmann M, Aadamsoo K, Võhma U, Pehlak H, et al. Spectral features of EEG in depression. Biomed Tech (Berl) 2010;55:155–161.
  • 9- Debener S, Beauducel A, Nessler D, Brocke B, Heilemann H, Kayser J. Is resting anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology 2000;41:31–7.
  • 10- Koles ZJ, Lind JC, Flor-Henry P. A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated men with schizophrenia. Psychiatry Res 2004;130:171– 190.
  • 11- Nissen C, Feige B, Nofzinger EA, Voderholzer U, Berger M, Riemann D. EEG slow wave activity regulation in major depression. Somnologie 2006;10:36–42.
  • 12- Hu B, Majoe D, Ratcliffe M, Qi Y, Zhao Q, Peng H, et al. EEG-based cognitive interfaces for ubiquitous applications: Developments and challenges. IEEE Intell Syst 2011;26:46–53.
  • 13- Coan JA, Allen JJ. Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol 2004;67:7–49.
  • 14- Grin-Yatsenko VA, Baas I, Ponomarev VA, Kropotov JD. Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clin Neurophysiol 2010;121:281–289.
  • 15- Kwan Y, Baek C, Chung S, Kim TH, Choi S. Resting-state quantitative EEG characteristics of insomniac patients with depression. Int J Psychophysiol 2018;124:26–32.
  • 16- Shen J, Zhang X, Wang G, Ding Z, Hu B. An improved empirical mode decomposition of electroencephalogram signals for depression detection. IEEE Trans Affect Comput 2022;13:262–271.
  • 17- Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol 2012;85:206–211.
  • 18- 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;12:1240019.
  • 19 - 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;44:175–181.
  • 20- Faust O, Ang PAC, Puthankattil SD, Joseph PK. Depression diagnosis support system based on EEG signal entropies. J Mech Med Biol 2014;14:1450035.
  • 21- Bachmann M, Lass J, Suhhova A, Hinrikus H. Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram. Comput Math Methods Med 2013;2013:251638.
  • 22- Mandelbrot BB. The Fractal Geometry of Bature. 2nd ed. New York: Times Books; 1982.
  • 23- Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Phys D: Nonlinear Phenom 1988;31:277–283.
  • 24- Marwan N, Thiel M, Nowaczyk NR. Cross recurrence plot based synchronization of time series. Nonlinear Process Geophys 2002;9:325–331.
  • 25- Marwan N, Romano MC, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Phys Rep 2007;438:237–329.
  • 26- Chua KC, Chandran V, Acharya UR, Lim CM. Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry. J Med Eng Technol 2008;32:263–72.
  • 27- Chua KC, Chandran V, Acharya UR, Lim CM. Cardiac state diagnosis using higher order spectra of heart rate variability. J Med Eng Technol 2008;32:145–155.
  • 28 Chua KC, Chandran V, Acharya UR, Lim CM. Application of higher order spectra to identify epileptic EEG. J Med Syst 2011;35:1563–1571.
  • 29 Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:H2039–H2049.
  • 30- Zhang C, Wang H, Wang H, Wu MH. EEG-based expert system using complexity measures and probability density function control in alpha sub-band. Integr Comput Aid Eng 2013;20:391– 405.
  • 31- Pincus SM, Keefe DL. Quantification of hormone pulsatility via an approximate entropy algorithm. Am J Physiol 1992;262:E741–E754.
  • 32- Wolf A, Swift JB, Swinney HL, Vastano JA. Determining Lyapunov exponents from a time series. Phys D: Nonlinear Pheno 1985;16:285–317.
  • 33- Dangel S, Meier PF, Moser HR, Plibersek S, Shen Y. Time series analysis of sleep EEG. Comput Assist Phys 1999;14:93–95.
  • 34- Lee JM, Kim DJ, Kim IY, Park KS, Kim SI. Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data. Comput Biol Med 2002;32:37–47. 35- Yuan Q, Zhou W, Yuan S, Li X, Wang J, Jia G. Epileptic EEG classification based on kernel sparse representation. Int J Neural Syst 2014;24:1450015.
  • 36- Osorio I. Automated seizure detection using EKG. Int J Neural Syst 2014;24:1450001.
  • 37- Acharya UR, SV, Bhat S, Adeli H, Adeli A. Computer-aided diagnosis of alcoholism-related EEG signals. Epilepsy Behav 2014;41:257–263.
  • 38- Hu M, Liang H. Perceptual suppression revealed by adaptive multi-scale entropy analysis of local field potential in monkey visual cortex. Int J Neural Syst 2013;23:1350005.
  • 39- Acharya UR, Yanti R, Zheng JW, Krishnan MM, Tan JH, Martis RJ, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 2013;23:1350009.
  • 40- Martis RJ, Acharya UR, Lim CM, Mandana KM, Ray AK, Chakraborty C. Application of higher order cumulant features for cardiac health diagnosis using ECG signals. Int J Neural Syst 2013;23:1350014.
  • 41- Rodríguez-Bermúdez G, García-Laencina PJ, Roca-Dorda J. Efficient automatic selection and combination of EEG features in least squares classifiers for motor imagery brain-computer interfaces. Int J Neural Syst 2013;23:1350015.
  • 42- Lin LC, Ouyang CS, Chiang CT, Yang RC, Wu RC, Wu HC. Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis. Int J Neural Syst 2014;24:1450023.
  • 43- Koppert M, Kalitzin S, Velis D, Lopes da Silva F, Viergever MA. Dynamics of collective multi-stability in models of multi-unit neuronal systems. Int J Neural Syst 2014;24:1430004.
  • 44- Cong F, Phan AH, Astikainen P, Zhao Q, Wu Q, Hietanen JK, et al. Multi-domain feature extraction for small event-related potentials through nonnegative multi-way array decomposition from low dense array EEG. Int J Neural Syst 2013;23:1350006.
  • 45- Fu R, Wang H. Detection of driving fatigue by using noncontact EMG and ECG signals measurement system. Int J Neural Syst 2014;24:1450006.
  • 46- Bauer PR, Kalitzin S, Zijlmans M, Sander JW, Visser GH. Cortical excitability as a potential clinical marker of epilepsy: A review of the clinical application of transcranial magnetic stimulation. Int J Neural Syst 2014;24:1430001.
  • 47- Aydin H, Ozgen F. Effect of imipramine on REM: Paradoxical or parallel? Eur Neuropsychopharmocol 1992;2:389–391.
  • 48- Ford DE, Cooper-Patrick L. Sleep disturbances and mood disorders: an epidemiologic perspective. Depress Anxiety 2001;14:3–6.
  • 49- Spiegelhalder K, Regen W, Nanovska S, Baglioni C, Riemann D. Comorbid sleep disorders in neuropsychiatric disorders across the life cycle. Curr Psychiatry Rep 2013;15:364.
  • 50- Luca A, Luca M, Calandra C. Sleep disorders and depression: Brief review of the literature, case report, and nonpharmacologic interventions for depression. Clin Interv Aging 2013;8:1033– 1039.
  • 51- Soehner AM, Kaplan KA, Harvey AG. Prevalence and clinical correlates of co-occurring insomnia and hypersomnia symptoms in depression. J Affect Disord 2014;167:93–97.
  • 52. Fawcett J, Scheftner WA, Fogg L, Clark DC, Young MA, Hedeker D, et al. Time-related predictors of suicide in major affective disorder. Am J Psychiatry 1990;147:1189–1194.
  • 53- Malik S, Kanwar A, Sim LA, Prokop LJ, Wang Z, Benkhadra K, et al. The association between sleep disturbances and suicidal behaviors in patients with psychiatric diagnoses: A systematic review and meta-analysis. Syst Rev 2014;3:18.
  • 54- Armitage R. Microarchitectural findings in sleep EEG in depression: Diagnostic implications. Biol Psychiatry 1995;37:72–84.
  • 55- Ozgen F, Aydin H, Gulcat Z. Sleep pattern in major depressives. J Sleep Res 2000;9:145.
  • 56- Armitage R, Roffwarg HP, Rush AJ, Calhoun JS, Purdy DG, Giles DE. Digital period analysis of sleep EEG in depression. Biol Psychiatry 1992;31:52–68.
  • 57- Röschke J, Mann K. The sleep EEG's microstructure in depression: Alterations of the phase relations between EEG rhythms during REM and NREM sleep. Sleep Med 2002;3:501–505.
  • 58- Estrada E, Nava P, Nazeran H, Behbehani K, Burk J, Lucas E. Itakura distance: A useful similarity measure between EEG and EOG signals in computer-aided classification of sleep stages. Conf Proc IEEE Eng Med Biol Soc 2005;2005:1189–1192.
  • 59- Eva OD, Lazar AM, Fira M. Normalized Itakura distance based discrimination used in a motor imagery brain computer interface paradigm. Available at: http://www.bulipi- eee.tuiasi.ro/archive/2015/fasc.4/p8_f4.pdf. Accessed on May 13, 2024.
  • 60- Gharbali AA, Najdi S, Fonseca JM. Investigating the contribution of distance-based features to automatic sleep stage classification. Comput Biol Med 2018;96:8–23.
  • 61- Itakura F. Minimum prediction residual principle applied to speech recognition. IEEE Trans Acoust 1975;23:67–72.
  • 62- Basseville M. Distance measures for signal processing and pattern recognition. Signal Process 1989;18:349–369.
  • 63. Muthuswamy J, Thakor NV. Spectral analysis methods for neurological signals. J Neurosci Methods 1998;83:1–14.
  • 64- Oikonomou VP, Tzallas AT, Fotiadis DI. A Kalman filter based methodology for EEG spike enhancement. Comput Methods Programs Biomed 2007;85:101–108.
  • 65- Pardey J, Roberts S, Tarassenko L. A review of parametric modelling techniques for EEG analysis. Med Eng Phys 1996;18:2–11.
  • 66- Penny WD, Roberts SJ. Dynamic models for nonstationary signal segmentation. Comput Biomed Res 1999;32:483–502.
  • 67- Olbrich E, Achermann P, Meier PF. Dynamics of human sleep EEG. Neurocomput 2003;52:857–862.
  • 68- Ozbek L, Sutcigil L, Aydın H, Yetkin S, Ozgen F. A statistical overview on sleep scoring. Ankara Univ Fac Sci Commun 2020;62;115–122.
  • 69- Ozbek L. Kalman Filtresi. 1st ed. Ankara: Akademisyen Kitabevi; 2017.
  • 70- Aliev FA, Ozbek L. Evaluation of convergence rate in the central limit theorem for the Kalman filter. IEEE Trans Autom Control 1990;44:1905–1909.
  • 71- Ozbek L, Efe M. An adaptive extended Kalman filter with application to compartment models. Commun Stat Simul Comput 2007;33:145–158.
  • 72- Steiger A, Pawlowski M. Depression and sleep. Int J Mol Sci 2019;20:607.

The evaluation of structural differences between the sleep EEGs of depressive and normal subjects by using itakura distance measure: A preliminary study

Year 2024, Volume: 42 Issue: 3, 795 - 804, 12.06.2024

Abstract

Electroencephalogram (EEG): It is used to diagnose, monitor, and manage neurophysiolog-ical disorders related to epilepsy and sleep disorders. The definition of sleep and wakeful-ness in polysomnography is also made with the EEG technique. The relationship between depression and sleep disturbances has been examined in many epidemiological and clinical studies. Clinical observations and studies suggest that the changes in sleep structure in de-pression are sensitive, even specific. This study aims to research the structural differences in sleep EEGs of healthy subjects and subjects with depressive disorder between their non-rap-id eye movement (NREM), non-rapid eye movement (N2), and rapid eye movement (REM) stages by using the Itakura Distance Measure. In comparison between the N2 and REM epochs of the healthy subjects, the distance is short. In the comparison between N2 and REM epochs of depressed subjects with each other and healthy subjects, the distance has been found to be large. The study indicates that the sleep EEG of the patients differs in the N2 stage as much as it does in REM.

References

  • Reference List
  • 1- Aboalayon KAI, Faezipour M, Almuhammadi WS, Moslehpour S. Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation. Entropy 2016;18:272.
  • 2- Patel AK, Reddy V, Shumway KR, Araujo JF. Physiology, sleep stages. Available at: https://www.ncbi.nlm.nih.gov/books/NBK526132/. Accessed on May 13, 2024.
  • 3- Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, et al. A pervasive approach to EEG-based depression detection. Complexity 2018;2018:5238028.
  • 4- Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A. Computer-aided diagnosis of depression using EEG signals. Eur Neurol 2015;73:329–336.
  • 5- Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A. Computer-aided diagnosis of depression using EEG signals. Eur Neurol 2015;73:329–336.
  • 6- Bachmann M, Lass J, Hinrikus H. Single channel EEG analysis for detection of depression. Biomed Signal Process Control 2017;31:391–397.
  • 7- Deslandes AC, de Moraes H, Pompeu FA, Ribeiro P, Cagy M, Capitão C, et al. Electroencephalographic frontal asymmetry and depressive symptoms in the elderly. Biol Psychol 2008;79:317– 322.
  • 8- Hinrikus H, Suhhova A, Bachmann M, Aadamsoo K, Võhma U, Pehlak H, et al. Spectral features of EEG in depression. Biomed Tech (Berl) 2010;55:155–161.
  • 9- Debener S, Beauducel A, Nessler D, Brocke B, Heilemann H, Kayser J. Is resting anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology 2000;41:31–7.
  • 10- Koles ZJ, Lind JC, Flor-Henry P. A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated men with schizophrenia. Psychiatry Res 2004;130:171– 190.
  • 11- Nissen C, Feige B, Nofzinger EA, Voderholzer U, Berger M, Riemann D. EEG slow wave activity regulation in major depression. Somnologie 2006;10:36–42.
  • 12- Hu B, Majoe D, Ratcliffe M, Qi Y, Zhao Q, Peng H, et al. EEG-based cognitive interfaces for ubiquitous applications: Developments and challenges. IEEE Intell Syst 2011;26:46–53.
  • 13- Coan JA, Allen JJ. Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol 2004;67:7–49.
  • 14- Grin-Yatsenko VA, Baas I, Ponomarev VA, Kropotov JD. Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clin Neurophysiol 2010;121:281–289.
  • 15- Kwan Y, Baek C, Chung S, Kim TH, Choi S. Resting-state quantitative EEG characteristics of insomniac patients with depression. Int J Psychophysiol 2018;124:26–32.
  • 16- Shen J, Zhang X, Wang G, Ding Z, Hu B. An improved empirical mode decomposition of electroencephalogram signals for depression detection. IEEE Trans Affect Comput 2022;13:262–271.
  • 17- Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. Int J Psychophysiol 2012;85:206–211.
  • 18- 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;12:1240019.
  • 19 - 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;44:175–181.
  • 20- Faust O, Ang PAC, Puthankattil SD, Joseph PK. Depression diagnosis support system based on EEG signal entropies. J Mech Med Biol 2014;14:1450035.
  • 21- Bachmann M, Lass J, Suhhova A, Hinrikus H. Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram. Comput Math Methods Med 2013;2013:251638.
  • 22- Mandelbrot BB. The Fractal Geometry of Bature. 2nd ed. New York: Times Books; 1982.
  • 23- Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Phys D: Nonlinear Phenom 1988;31:277–283.
  • 24- Marwan N, Thiel M, Nowaczyk NR. Cross recurrence plot based synchronization of time series. Nonlinear Process Geophys 2002;9:325–331.
  • 25- Marwan N, Romano MC, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Phys Rep 2007;438:237–329.
  • 26- Chua KC, Chandran V, Acharya UR, Lim CM. Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry. J Med Eng Technol 2008;32:263–72.
  • 27- Chua KC, Chandran V, Acharya UR, Lim CM. Cardiac state diagnosis using higher order spectra of heart rate variability. J Med Eng Technol 2008;32:145–155.
  • 28 Chua KC, Chandran V, Acharya UR, Lim CM. Application of higher order spectra to identify epileptic EEG. J Med Syst 2011;35:1563–1571.
  • 29 Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:H2039–H2049.
  • 30- Zhang C, Wang H, Wang H, Wu MH. EEG-based expert system using complexity measures and probability density function control in alpha sub-band. Integr Comput Aid Eng 2013;20:391– 405.
  • 31- Pincus SM, Keefe DL. Quantification of hormone pulsatility via an approximate entropy algorithm. Am J Physiol 1992;262:E741–E754.
  • 32- Wolf A, Swift JB, Swinney HL, Vastano JA. Determining Lyapunov exponents from a time series. Phys D: Nonlinear Pheno 1985;16:285–317.
  • 33- Dangel S, Meier PF, Moser HR, Plibersek S, Shen Y. Time series analysis of sleep EEG. Comput Assist Phys 1999;14:93–95.
  • 34- Lee JM, Kim DJ, Kim IY, Park KS, Kim SI. Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data. Comput Biol Med 2002;32:37–47. 35- Yuan Q, Zhou W, Yuan S, Li X, Wang J, Jia G. Epileptic EEG classification based on kernel sparse representation. Int J Neural Syst 2014;24:1450015.
  • 36- Osorio I. Automated seizure detection using EKG. Int J Neural Syst 2014;24:1450001.
  • 37- Acharya UR, SV, Bhat S, Adeli H, Adeli A. Computer-aided diagnosis of alcoholism-related EEG signals. Epilepsy Behav 2014;41:257–263.
  • 38- Hu M, Liang H. Perceptual suppression revealed by adaptive multi-scale entropy analysis of local field potential in monkey visual cortex. Int J Neural Syst 2013;23:1350005.
  • 39- Acharya UR, Yanti R, Zheng JW, Krishnan MM, Tan JH, Martis RJ, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 2013;23:1350009.
  • 40- Martis RJ, Acharya UR, Lim CM, Mandana KM, Ray AK, Chakraborty C. Application of higher order cumulant features for cardiac health diagnosis using ECG signals. Int J Neural Syst 2013;23:1350014.
  • 41- Rodríguez-Bermúdez G, García-Laencina PJ, Roca-Dorda J. Efficient automatic selection and combination of EEG features in least squares classifiers for motor imagery brain-computer interfaces. Int J Neural Syst 2013;23:1350015.
  • 42- Lin LC, Ouyang CS, Chiang CT, Yang RC, Wu RC, Wu HC. Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis. Int J Neural Syst 2014;24:1450023.
  • 43- Koppert M, Kalitzin S, Velis D, Lopes da Silva F, Viergever MA. Dynamics of collective multi-stability in models of multi-unit neuronal systems. Int J Neural Syst 2014;24:1430004.
  • 44- Cong F, Phan AH, Astikainen P, Zhao Q, Wu Q, Hietanen JK, et al. Multi-domain feature extraction for small event-related potentials through nonnegative multi-way array decomposition from low dense array EEG. Int J Neural Syst 2013;23:1350006.
  • 45- Fu R, Wang H. Detection of driving fatigue by using noncontact EMG and ECG signals measurement system. Int J Neural Syst 2014;24:1450006.
  • 46- Bauer PR, Kalitzin S, Zijlmans M, Sander JW, Visser GH. Cortical excitability as a potential clinical marker of epilepsy: A review of the clinical application of transcranial magnetic stimulation. Int J Neural Syst 2014;24:1430001.
  • 47- Aydin H, Ozgen F. Effect of imipramine on REM: Paradoxical or parallel? Eur Neuropsychopharmocol 1992;2:389–391.
  • 48- Ford DE, Cooper-Patrick L. Sleep disturbances and mood disorders: an epidemiologic perspective. Depress Anxiety 2001;14:3–6.
  • 49- Spiegelhalder K, Regen W, Nanovska S, Baglioni C, Riemann D. Comorbid sleep disorders in neuropsychiatric disorders across the life cycle. Curr Psychiatry Rep 2013;15:364.
  • 50- Luca A, Luca M, Calandra C. Sleep disorders and depression: Brief review of the literature, case report, and nonpharmacologic interventions for depression. Clin Interv Aging 2013;8:1033– 1039.
  • 51- Soehner AM, Kaplan KA, Harvey AG. Prevalence and clinical correlates of co-occurring insomnia and hypersomnia symptoms in depression. J Affect Disord 2014;167:93–97.
  • 52. Fawcett J, Scheftner WA, Fogg L, Clark DC, Young MA, Hedeker D, et al. Time-related predictors of suicide in major affective disorder. Am J Psychiatry 1990;147:1189–1194.
  • 53- Malik S, Kanwar A, Sim LA, Prokop LJ, Wang Z, Benkhadra K, et al. The association between sleep disturbances and suicidal behaviors in patients with psychiatric diagnoses: A systematic review and meta-analysis. Syst Rev 2014;3:18.
  • 54- Armitage R. Microarchitectural findings in sleep EEG in depression: Diagnostic implications. Biol Psychiatry 1995;37:72–84.
  • 55- Ozgen F, Aydin H, Gulcat Z. Sleep pattern in major depressives. J Sleep Res 2000;9:145.
  • 56- Armitage R, Roffwarg HP, Rush AJ, Calhoun JS, Purdy DG, Giles DE. Digital period analysis of sleep EEG in depression. Biol Psychiatry 1992;31:52–68.
  • 57- Röschke J, Mann K. The sleep EEG's microstructure in depression: Alterations of the phase relations between EEG rhythms during REM and NREM sleep. Sleep Med 2002;3:501–505.
  • 58- Estrada E, Nava P, Nazeran H, Behbehani K, Burk J, Lucas E. Itakura distance: A useful similarity measure between EEG and EOG signals in computer-aided classification of sleep stages. Conf Proc IEEE Eng Med Biol Soc 2005;2005:1189–1192.
  • 59- Eva OD, Lazar AM, Fira M. Normalized Itakura distance based discrimination used in a motor imagery brain computer interface paradigm. Available at: http://www.bulipi- eee.tuiasi.ro/archive/2015/fasc.4/p8_f4.pdf. Accessed on May 13, 2024.
  • 60- Gharbali AA, Najdi S, Fonseca JM. Investigating the contribution of distance-based features to automatic sleep stage classification. Comput Biol Med 2018;96:8–23.
  • 61- Itakura F. Minimum prediction residual principle applied to speech recognition. IEEE Trans Acoust 1975;23:67–72.
  • 62- Basseville M. Distance measures for signal processing and pattern recognition. Signal Process 1989;18:349–369.
  • 63. Muthuswamy J, Thakor NV. Spectral analysis methods for neurological signals. J Neurosci Methods 1998;83:1–14.
  • 64- Oikonomou VP, Tzallas AT, Fotiadis DI. A Kalman filter based methodology for EEG spike enhancement. Comput Methods Programs Biomed 2007;85:101–108.
  • 65- Pardey J, Roberts S, Tarassenko L. A review of parametric modelling techniques for EEG analysis. Med Eng Phys 1996;18:2–11.
  • 66- Penny WD, Roberts SJ. Dynamic models for nonstationary signal segmentation. Comput Biomed Res 1999;32:483–502.
  • 67- Olbrich E, Achermann P, Meier PF. Dynamics of human sleep EEG. Neurocomput 2003;52:857–862.
  • 68- Ozbek L, Sutcigil L, Aydın H, Yetkin S, Ozgen F. A statistical overview on sleep scoring. Ankara Univ Fac Sci Commun 2020;62;115–122.
  • 69- Ozbek L. Kalman Filtresi. 1st ed. Ankara: Akademisyen Kitabevi; 2017.
  • 70- Aliev FA, Ozbek L. Evaluation of convergence rate in the central limit theorem for the Kalman filter. IEEE Trans Autom Control 1990;44:1905–1909.
  • 71- Ozbek L, Efe M. An adaptive extended Kalman filter with application to compartment models. Commun Stat Simul Comput 2007;33:145–158.
  • 72- Steiger A, Pawlowski M. Depression and sleep. Int J Mol Sci 2019;20:607.
There are 72 citations in total.

Details

Primary Language English
Subjects Software Testing, Verification and Validation
Journal Section Research Articles
Authors

Levemt Özbek This is me 0000-0003-1018-3114

Levent Sütçigil This is me 0000-0001-8147-0124

Hamdullah Aydin This is me 0000-0002-3670-5040

Kazım Cihan Can This is me

Sinan Yetkin This is me

Asuhan Par This is me 0000-0003-3919-6955

Publication Date June 12, 2024
Submission Date September 14, 2022
Published in Issue Year 2024 Volume: 42 Issue: 3

Cite

Vancouver Özbek L, Sütçigil L, Aydin H, Can KC, Yetkin S, Par A. The evaluation of structural differences between the sleep EEGs of depressive and normal subjects by using itakura distance measure: A preliminary study. SIGMA. 2024;42(3):795-804.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/