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Su ve Tükürük Yutma İmgelemesinde Doğal ve İndüklenmiş Durumların EEG Tabanlı Karşılaştırması

Year 2025, Volume: 16 Issue: 3, 599 - 610
https://doi.org/10.24012/dumf.1675408

Abstract

Yutma güçlüğü (disfaji), bireyler için yemek yeme ve sıvı tüketimini ağrılı ve stresli bir deneyim hâline getirebilir. Bu çalışma, yutma imgelemesinin sinirsel karşılıklarını araştırarak sürecin elektrofizyolojik açıdan daha iyi anlaşılmasını sağlamayı ve rehabilitasyon stratejilerinin geliştirilmesine katkıda bulunmayı amaçlamaktadır. Bu doğrultuda üç deneysel paradigma tasarlanmıştır: (i) doğal su yutma, (ii) görsel uyarıcı ile indüklenmiş tükürük yutma ve (iii) bir yudum suyun görsel uyarıcı ile indüklenmiş yutulması. Her bir koşul, görsel ipuçlarıyla yönlendirilen hem gerçek yutma hem de motor imgeleme görevlerini içermektedir. EEG verileri, her bir koşul için 15 tekrar olmak üzere, 30 katılımcıdan (15 erkek, 15 kadın) 16 kanaldan kaydedilmiştir. Gürültü giderme ve sinyal parçalama gibi önişleme adımlarının ardından, spektral ağırlık merkezi, ortalama ve medyan frekans ile bant güçleri gibi toplam 176 frekans alanı özelliği çıkarılmıştır. Özelliklerin normalliği Shapiro-Wilk testi ile değerlendirilmiştir. Normal dağılım gösteren özellikler (11 kanaldan elde edilen spektral ağırlık merkezi) tekrarlayan ölçümler için tek yönlü ANOVA ile, normal dağılmayan özellikler ise Friedman testi ile analiz edilmiştir. İstatistiksel analiz sonuçları, tüm özelliklerin %76,7’sinin üç farklı yutma durumu arasında anlamlı farklılık gösterdiğini ortaya koymuştur. Bu bulgular, EEG tabanlı özelliklerin farklı yutma imgeleme türleri arasında etkili bir şekilde ayrım yapabileceğini ve disfaji rehabilitasyonuna yönelik beyin-bilgisayar arayüzü uygulamaları için önemli bir potansiyel sunduğunu göstermektedir.

References

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  • [22] I. Jestrović, J. L. Coyle, S. Perera, and E. Sejdić, “Influence of attention and bolus volume on brain organization during swallowing,” Brain Struct. Funct., vol. 223, no. 2, pp. 749–761, 2018, doi: 10.1007/s00429-017-1535-7.
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  • [24] A. Alexandropoulou, E. Magkouti, A. Despoti, N. Leventakis, and S. Nanas, “Studying the swallow using surface electroencephalography: A systematic review,” Health Res. J., vol. 9, no. 2, pp. 63–75, 2023, doi: 10.12681/healthresj.33617.
  • [25] N. M. Y. B. W. Razali, “Power comparison of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests,” J. Stat. Model. Anal., vol. 2, no. 1, pp. 21–33, 2011.
  • [26] I. Jestrović, J. L. Coyle, and E. Sejdić, “Decoding human swallowing via electroencephalography: A state-of-the-art review,” J. Neural Eng., vol. 12, no. 5, p. 051001, 2015, doi: 10.1088/1741-2560/12/5/051001.
  • [27] S. McWeeny and E. S. Norton, “Understanding event-related potentials (ERPs) in clinical and basic language and communication disorders research: A tutorial,” Int. J. Lang. Commun. Disord., vol. 55, no. 5, pp. 649–663, 2020, doi: 10.1111/1460-6984.12535.
  • [28] C. Ertekin, N. Yüceyar, and I. Aydoğdu, “Clinical and electrophysiological evaluation of dysphagia in myasthenia gravis,” J. Neurol. Neurosurg. Psychiatry, vol. 65, no. 6, pp. 848–856, 1998, doi: 10.1136/jnnp.65.6.848.
  • [29] Y. Beckmann et al., “Electrophysiological evaluation of dysphagia in the mild or moderate patients with multiple sclerosis: A concept of subclinical dysphagia,” Dysphagia, vol. 30, no. 3, pp. 331–338, 2015, doi: 10.1007/s00455-015-9598-1.
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EEG-Based Comparative Study of Brain Activity during Imagined Natural and Induced Water and Saliva Swallowing

Year 2025, Volume: 16 Issue: 3, 599 - 610
https://doi.org/10.24012/dumf.1675408

Abstract

Dysphagia often makes eating and drinking painful, stressful, and socially isolating, potentially leading to malnutrition, dehydration, weight loss, and respiratory infections. In this study, the relationship between swallowing and brain signals was examined to contribute to the electrophysiological understanding of the imagination of swallowing and rehabilitation of dysphagia patients. To examine the swallowing event, three different experiments were conducted. The experiments included (i) natural water swallowing, (ii) swallowing saliva in an induced manner, and (iii) swallowing a sip of water in an induced manner. Visual cues on a computer monitor were used to induce the perception of swallowing and imagination. EEG data from 16 channels obtained during 15 trials of these experimental paradigms from 30 subjects (15 men) were subjected to different processes such as noise removal, selection of signal segments corresponding to the imagination of swallowing, extraction of frequency domain features, and statistical analysis. Eleven features such as spectral centroid, mean and median frequency, delta, theta, alpha and beta band powers, and relative band powers obtained from 16 channels (a total of 176 features) were first subjected to the Shapiro-Wilks normality test individually. As a result of this test, the statistical analyses were carried out with the help of repeated measures one-way ANOVA test for the features with normal distribution (spectral centroid from 11 channels), and the Friedman test for the features with non-normal distribution (spectral centroid from the remaining 5 channels and all other features from 16 channels). As a result of these tests, it is seen that 76.7% of all features yield statistically significant differences between 3 different swallowing approaches. We suggest that identifying discriminative EEG-based features could significantly contribute to the development of novel brain-machine interface applications for dysphagia rehabilitation.

Ethical Statement

This study was approved by the Erciyes University Ethics Committee on July 12, 2023, under approval number 2023/461.

References

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  • [2] J. M. Dudik, A. Kurosu, J. L. Coyle, and E. Sejdić, “Dysphagia and its effects on swallowing sounds and vibrations in adults,” Biomed. Eng. Online, vol. 17, no. 1, 2018, doi: 10.1186/s12938-018-0501-9.
  • [3] P. M. Bath, H. S. Lee, and L. F. Everton, “Swallowing therapy for dysphagia in acute and subacute stroke,” Stroke, 2019, doi: 10.1161/STROKEAHA.118.024299.
  • [4] P. Leslie, M. J. Drinnan, I. Zammit-Maempel, J. L. Coyle, G. A. Ford, and J. A. Wilson, “Cervical auscultation synchronized with images from endoscopy swallow evaluations,” Dysphagia, vol. 22, no. 4, pp. 290–298, 2007, doi: 10.1007/s00455-007-9084-5.
  • [5] S. E. Langmore, “Evaluation of oropharyngeal dysphagia: Which diagnostic tool is superior?,” Curr. Opin. Otolaryngol. Head Neck Surg., vol. 11, no. 6, pp. 485–489, 2003, doi: 10.1097/00020840-200312000-00014.
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  • [12] H. Namazi, E. Aghasian, and T. S. Ala, “Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia,” Technol. Health Care, vol. 27, no. 3, pp. 231–242, 2019, doi: 10.3233/THC-181497.
  • [13] J. Chouinard, “Dysphagia in Alzheimer disease: A review,” J. Nutr. Health Aging, vol. 4, no. 4, pp. 214–217, 2000.
  • [14] H. Azuma and T. Akechi, “EEG correlates of quality of life and associations with seizure without awareness and depression in patients with epilepsy,” Neuropsychopharmacol. Rep., vol. 42, no. 3, pp. 281–289, 2022, doi: 10.1002/npr2.12276.
  • [15] Ö. Türk and M. S. Özerdem, “Epilepsy detection by using scalogram based convolutional neural network from EEG signals,” Brain Sci., vol. 9, no. 5, p. 115, 2019, doi: 10.3390/brainsci9050115.
  • [16] S. Abenna, M. Nahid, H. Bouyghf, and B. Ouacha, “An enhanced motor imagery EEG signals prediction system in real-time based on delta rhythm,” Biomed. Signal Process. Control, vol. 79, 2023, Art. no. 104210, doi: 10.1016/j.bspc.2022.104210.
  • [17] A. Al-Saegh, S. A. Dawwd, and J. M. Abdul-Jabbar, “Deep learning for motor imagery EEG-based classification: A review,” Biomed. Signal Process. Control, vol. 63, p. 102172, 2021, doi: 10.1016/j.bspc.2020.102172.
  • [18] A. S. C. Caldas et al., “Motor imagery and swallowing: a systematic literature review,” Rev. CEFAC, vol. 20, no. 2, pp. 249–258, 2018, doi: 10.1590/1982-0216201820214317.
  • [19] S. SH, N. CV, and D. CRO, “Motor imagery and swallowing: Introduction to literature and discussion of research needs in dysphagia,” Health Care Curr. Rev., vol. 6, no. 1, 2018, doi: 10.4172/2375-4273.1000218.
  • [20] H. Yang et al., “Detection of motor imagery of swallow with model adaptation: Swallow or tongue?,” in Proc. 5th Int. Brain-Computer Interface Meeting, 2013, doi: 10.3217/978-3-85125-260-6-56.
  • [21] H. Yang et al., “On the correlations of motor imagery of swallow with motor imagery of tongue movements and actual swallow,” in Proc. Int. Conf. Neural Inf. Process., 2016, pp. 481–488, doi: 10.1007/978-981-10-0207-6_55.
  • [22] I. Jestrović, J. L. Coyle, S. Perera, and E. Sejdić, “Influence of attention and bolus volume on brain organization during swallowing,” Brain Struct. Funct., vol. 223, no. 2, pp. 749–761, 2018, doi: 10.1007/s00429-017-1535-7.
  • [23] M. L. Huckabee, L. Deecke, M. P. Cannito, H. J. Gould, and W. Mayr, “Cortical control mechanisms in volitional swallowing: The Bereitschaftspotential,” Brain Topogr., vol. 16, no. 1, pp. 3–17, 2003, doi: 10.1023/A:1025671914949.
  • [24] A. Alexandropoulou, E. Magkouti, A. Despoti, N. Leventakis, and S. Nanas, “Studying the swallow using surface electroencephalography: A systematic review,” Health Res. J., vol. 9, no. 2, pp. 63–75, 2023, doi: 10.12681/healthresj.33617.
  • [25] N. M. Y. B. W. Razali, “Power comparison of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests,” J. Stat. Model. Anal., vol. 2, no. 1, pp. 21–33, 2011.
  • [26] I. Jestrović, J. L. Coyle, and E. Sejdić, “Decoding human swallowing via electroencephalography: A state-of-the-art review,” J. Neural Eng., vol. 12, no. 5, p. 051001, 2015, doi: 10.1088/1741-2560/12/5/051001.
  • [27] S. McWeeny and E. S. Norton, “Understanding event-related potentials (ERPs) in clinical and basic language and communication disorders research: A tutorial,” Int. J. Lang. Commun. Disord., vol. 55, no. 5, pp. 649–663, 2020, doi: 10.1111/1460-6984.12535.
  • [28] C. Ertekin, N. Yüceyar, and I. Aydoğdu, “Clinical and electrophysiological evaluation of dysphagia in myasthenia gravis,” J. Neurol. Neurosurg. Psychiatry, vol. 65, no. 6, pp. 848–856, 1998, doi: 10.1136/jnnp.65.6.848.
  • [29] Y. Beckmann et al., “Electrophysiological evaluation of dysphagia in the mild or moderate patients with multiple sclerosis: A concept of subclinical dysphagia,” Dysphagia, vol. 30, no. 3, pp. 331–338, 2015, doi: 10.1007/s00455-015-9598-1.
  • [30] B. Labeit et al., “The assessment of dysphagia after stroke: State of the art and future directions,” Lancet Neurol., vol. 22, no. 7, pp. 590–600, 2023, doi: 10.1016/S1474-4422(23)00153-9.
  • [31] C. G. Ashley Fox, “Using motor imagery therapy to improve movement efficiency and reduce fall injury risk,” J. Nov. Physiother., vol. 3, no. 6, p. 186, 2013, doi: 10.4172/2165-7025.1000186.
  • [32] T. Singer, P. Fahey, and K. P. Y. Liu, “The efficacy of imagery in the rehabilitation of people with Parkinson’s disease: Protocol for a systematic review and meta-analysis,” Syst. Rev., vol. 11, no. 1, p. 47, 2022, doi: 10.1186/s13643-022-02041-z.
  • [33] A. M. Ladda, F. Lebon, and M. Lotze, “Using motor imagery practice for improving motor performance – A review,” Brain Cogn., vol. 150, p. 105705, 2021, doi: 10.1016/j.bandc.2021.105705.
  • [34] H. Yang et al., “Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection,” J. Neural Eng., vol. 11, no. 3, p. 035016, 2014, doi: 10.1088/1741-2560/11/3/035016.
  • [35] S. E. Kober and G. Wood, “Changes in hemodynamic signals accompanying motor imagery and motor execution of swallowing: A near-infrared spectroscopy study,” Neuroimage, vol. 93, no. Pt 1, pp. 1–10, 2014, doi: 10.1016/j.neuroimage.2014.02.019.
  • [36] H. Yang, K. K. Ang, C. Wang, K. S. Phua, and C. Guan, “Neural and cortical analysis of swallowing and detection of motor imagery of swallow for dysphagia rehabilitation—A review,” in Prog. Brain Res., vol. 228, pp. 271–295, 2016, doi: 10.1016/bs.pbr.2016.03.014.
  • [37] H. Yang, C. Guan, K. K. Ang, C. C. Wang, K. S. Phua, and J. Yu, “Dynamic initiation and dual-tree complex wavelet feature-based classification of motor imagery of swallow EEG signals,” in Proc. Int. Joint Conf. Neural Netw. (IJCNN), 2012, pp. 1–7, doi: 10.1109/IJCNN.2012.6252603.
  • [38] H. Yang et al., “Feature consistency-based model adaptation in session-to-session classification: A study using motor imagery of swallow EEG signals,” in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), 2013, pp. 5438–5441, doi: 10.1109/EMBC.2013.6609528.
  • [39] S. E. Kober, D. Grössinger, and G. Wood, “Effects of motor imagery and visual neurofeedback on activation in the swallowing network: A real-time fMRI study,” Dysphagia, vol. 34, no. 6, pp. 763–775, 2019, doi: 10.1007/s00455-019-09985-w.
There are 39 citations in total.

Details

Primary Language English
Subjects Computational Neuroscience, Neurosciences (Other), Rehabilitation Engineering, Neural Engineering, Biomedical Engineering (Other)
Journal Section Articles
Authors

Sevgi Gökçe Aslan 0000-0001-9425-1916

Early Pub Date September 30, 2025
Publication Date October 9, 2025
Submission Date April 13, 2025
Acceptance Date August 6, 2025
Published in Issue Year 2025 Volume: 16 Issue: 3

Cite

IEEE S. Gökçe Aslan, “EEG-Based Comparative Study of Brain Activity during Imagined Natural and Induced Water and Saliva Swallowing”, DUJE, vol. 16, no. 3, pp. 599–610, 2025, doi: 10.24012/dumf.1675408.