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Advanced Cognitive State Analysis of Insomnia Using Computational Architecture for Modeling Thought and Awareness Disruption

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Dr. Amir Reza Khosravi ¹ , Dr. Sara Mohammadi ¹ ,

⁴ Department of Electrical Engineering, University of Tehran, Tehran, Iran

⁴ Faculty of Computer Science, Sharif University of Technology, Tehran, Iran

Abstract

Insomnia is increasingly recognized not only as a sleep disorder but as a complex neurocognitive dysregulation involving persistent alterations in thought patterns, emotional processing, and awareness stability. Traditional clinical models emphasize behavioral and physiological dimensions; however, recent advances in computational psychiatry suggest that insomnia can be effectively conceptualized as a dynamic system of disrupted cognitive states. This study proposes an advanced computational architecture for modeling cognitive state transitions in insomnia, with a focus on thought intrusion, attentional bias, and awareness fragmentation.

Drawing on formal psychopathology modeling frameworks (Haslbeck et al., 2022) and neurocomputational causal modeling approaches (Pereira et al., 2021), the proposed architecture integrates multi-layered cognitive variables to simulate insomnia-related cognitive instability. The model incorporates repetitive negative thinking dynamics (Lancee et al., 2015), attentional bias mechanisms (Milkins et al., 2016), and cognitive behavioral therapy response pathways (Trauer et al., 2015).

Additionally, this research situates computational insomnia modeling within broader machine learning and deep learning paradigms, including adaptive learning systems used in cybersecurity and behavioral prediction domains (Akram et al., 2024; Cheng et al., 2024). The findings highlight the potential of computational architectures in predicting insomnia severity, mapping cognitive disruptions, and optimizing therapeutic interventions.

The study concludes that insomnia can be effectively represented as a nonlinear cognitive-state transition system, where awareness instability emerges from interacting cognitive, emotional, and attentional subsystems.

Keywords

Insomnia modeling, cognitive state analysis, computational psychiatry, awareness disruption

References

M. M. Abualhaj, M. Al-Zyoud, A. Alsaaidah, A.Abu-Shareha, and S.Al-Khatib, “Enhancing malware detection through self-union feature selection usingfirefly algorithm with random forest classification,”International Journal of Intelligent Engineering and Systems, vol. 17, no. 4, pp. 376-389, 2024

N. Akram et al., “Online recruitment fraud (ORF) detection using deep learning approaches,”IEEE Access, vol. 12, pp. 109388-109408, 2024

A. Ballesio et al., “Does cognitive behaviour therapy for insomnia reduce repetitive negative thinking and sleep-related worry beliefs? A systematic review and meta-analysis,”Sleep Medicine Reviews, vol. 55, 2021

Y. Cheng, A. Vijayaraj, K. S.Pokkuluri, T. Salehnia, A. Montazerolghaem,and R. Rateb, “Vehicular fog resource allocation approach for VANETs based on deep adaptive reinforcement learning combined with heuristic information,”IEEE Access, vol. 12, pp. 139056-139075, 2024

C. A. Espie, “Revisiting the psychobiological inhibition model: A conceptual framework for understanding and treating insomnia using cognitive and behavioural therapeutics (CBTx),”Journal of Sleep Research, 2023

J. M. B. Haslbeck, O. Ryan,D. J. Robinaugh, L. J. Waldorp, and D. Borsboom, “Modeling psychopathology: From data models to formal theories,”Psychological Methods, vol. 27, pp. 930–957, 2022

E. S. Izmailova, J. A. Wagner, and E. D. Perakslis, “Wearable devices in clinical trials: hype and hypothesis,”Clinical Pharmacology & Therapeutics, vol. 104, no. 1, pp. 42–52, Apr. 2018

M. Jansson-Fröjmark, A. G. Harvey, and I. K. Flink, “Psychometric properties of the insomnia catastrophizing scale (ICS) in a large community sample,”Cognitive Behaviour Therapy, vol. 49, no. 2, pp. 120–136, 2020

D. A. Kalmbach et al., “A randomized controlled trial of digital cognitive behavioral therapy for insomnia in pregnant women,”Sleep Medicine, vol. 72, pp. 82–92, 2020

J. Lancee, M. C. Eisma, K. B. V.Zanten, and M. Topper, “When thinking impairs sleep: trait, daytime and nighttime repetitive thinking in insomnia,”Behavioral Sleep Medicine, vol. 15, no. 1, pp. 53–69, Dec. 2015

J. Lancee, S. L. Yasiney, R. S. Brendel, M. Boffo, P. J. F. Clarke, and E. Salemink, “Attentional bias modification training for insomnia: a doubleblind placebo controlled randomized trial,”PLoS One, vol. 12, no. 4, 2017

M. Lauriola et al., “A correlational analysis of the relationships among intolerance of uncertainty, anxiety sensitivity, subjective sleep quality, and insomnia symptoms,”International Journal of Environmental Research and Public Health, vol. 16, no. 18, 2019

L. J. Mitchell, L. Bisdounis, A. Ballesio, X. Omlin, and S. D. Kyle, “The impact of cognitive behavioural therapy for insomnia on objective sleep parameters: A meta-analysis and systematic review,”Sleep Medicine Reviews, vol. 47, Jun. 2019

B. Milkins, L. Notebaert, C. MacLeod, and P. J. F. Clarke, “The potential benefits of targeted attentional bias modification on cognitive arousal and sleep quality in worry-related sleep disturbance,”Clinical Psychological Science, vol. 4, no. 6, pp. 1015–1027, Jun. 2016

I. Pereira, S. Frässle, J. Heinzle, D. Schöbi, C. T. Do, M. Gruber, K.E. Stephan, “Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities,”NeuroImage, vol. 245, 2021

R. Rateb, A. Aziz, and R. Ahmad, “Formal modeling and analysis of social support recipient preferences,”Journal of Telecommunication, Electronic and Computer Engineering,vol. 9, no. 3–5, pp. 69–75, 2017

R. Rateb, A. Aziz, and R. Ahmad, “Designing anautomated social support assignment over social networks,”inThe 7th International Conferenceon Postgraduate Education (ICPE-7),pp. 52–62, 2016

M. J. Sateia, “International classification of sleep disorders-third edition: highlights and modifications,”Chest, vol. 146, no. 5, pp. 1387–1394, 2014

A. J. Shallcross, and P. D. Visvanathan, “Mindfulness-based cognitive therapy for insomnia,”Mindfulness-Based Cognitive Therapy,pp. 19–29, 2016

E. Siedlecka, and T. F. Denson, “Experimental methods for inducing basic emotions: a qualitative review,”Emotion Review, vol. 11, no. 1, pp. 87–97, 2019

J. M. Trauer, M. Y. Qian, J. S. Doyle, S. M. Rajaratnam, and D. Cunnington, “Cognitive behavioral therapy for chronic insomnia: a systematic review and meta-analysis,”Annals of internal medicine, vol. 163, no. 3, pp. 191–204, 2015

M. K. Uhrig et al., “Emotion elicitation: a comparison of pictures and films,”Frontiers in Psychology, vol. 7, Feb. 2016

Y. Y. Wang et al., “Mindfulness-based interventions for insomnia: a meta-analysis of randomized controlled trials,”Behavioral Sleep Medicine, vol. 18, no. 1, pp. 1–9, 2020

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