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EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

Year 2022, Volume: 13 Issue: 2, 213 - 220, 28.06.2022
https://doi.org/10.24012/dumf.1092569

Abstract

Alzheimer beyindeki bozulmalardan kaynaklı bilişsel ve davranışsal eksiklikler gibi semptomlarla kendini gösteren önemli bir nörolojik hastalıktır. Alzheimer hastalığının kesin bir tedavi yöntemi bulunmamaktadır. Ancak hastalığın erken teşhisi ile hastalığın ilerlemesinin yavaşlatılması amaçlanmaktadır. Bu durum hastanın yaşam standartlarının korunmasında önem arz etmektedir. Ayrıca hastalığın tam olarak teşhisi deneyimli bir uzman tarafından değerlendirilecek olan maliyetli testler ve yorucu bir teşhis aşaması gerektirmektedir. Bu motivasyonla önerilen yöntemle Alzheimer hastalığının EEG sinyallerinden otomatik olarak gerçekleştirilmesini amaçlayan yeni bir bilgisayar destekli tanı sistemi sunulmaktadır. Sunulan çalışmada öncelikle ham EEG verilerine önişlem uygulanarak var olan gürültüler giderilmiştir. Sonraki aşamada ise her bir kanaldan alınan verilere dalgacık dönüşümü uygulandıktan sonra istatistiksel özellikler hesaplanmıştır. Elde edilen özelliklerin k-en yakın komşu (kNN) sınıflandırıcısı ile sınıflandırılmasıyla sağlıklı katılımcılar ile Alzheimer hastası katılımcılar 91.12% doğrulukla ayırt edilmiştir.

References

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  • [2] L. R. Trambaiolli, N. Spolaôr, A. C. Lorena, R. Anghinah, and J. R. Sato, “Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease,” Clin. Neurophysiol., vol. 128, no. 10, pp. 2058–2067, 2017.
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  • [5] S. J. Ruiz-Gómez et al., “Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment,” Entropy, vol. 20, no. 1, p. 35, 2018.
  • [6] J. P. Amezquita-Sanchez, N. Mammone, F. C. Morabito, S. Marino, and H. Adeli, “A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals,” J. Neurosci. Methods, vol. 322, pp. 88–95, 2019.
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  • [8] K. D. Tzimourta et al., “EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions,” Brain Sci., vol. 9, no. 4, p. 81, 2019.
  • [9] M. S. Safi and S. M. M. Safi, “Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters,” Biomed. Signal Process. Control, vol. 65, p. 102338, 2021.
  • [10] U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Syst. Appl., vol. 38, no. 10, pp. 13475–13481, 2011.
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  • [12] A. M. Pineda, F. M. Ramos, L. E. Betting, and A. S. L. O. Campanharo, “Quantile graphs for EEG-based diagnosis of Alzheimer’s disease,” PLoS One, vol. 15, no. 6, p. e0231169, 2020.
  • [13] B. R. Bakshi, “Multiscale PCA with application to multivariate statistical process monitoring,” AIChE J., vol. 44, no. 7, pp. 1596–1610, 1998.
  • [14] P. Jahankhani, V. Kodogiannis, and K. Revett, “EEG signal classification using wavelet feature extraction and neural networks,” in IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06), 2006, pp. 120–124.
  • [15] H. U. Amin et al., “Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques,” Australas. Phys. \& Eng. Sci. Med., vol. 38, no. 1, pp. 139–149, 2015.
  • [16] R. C. Gonzalez, Digital image processing. Pearson education india, 2009.
  • [17] N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat., vol. 46, no. 3, pp. 175–185, 1992.
  • [18] W. H. Organization and others, Dementia: a public health priority. World Health Organization, 2012.
  • [19] J. Jeong, “EEG dynamics in patients with Alzheimer’s disease,” Clin. Neurophysiol., vol. 115, no. 7, pp. 1490–1505, 2004.
  • [20] C. Patterson and others, “World alzheimer report 2018,” 2018.
  • [21] J. Dauwels, F. Vialatte, and A. Cichocki, “Diagnosis of Alzheimer’s disease from EEG signals: where are we standing?,” Curr. Alzheimer Res., vol. 7, no. 6, pp. 487–505, 2010.
  • [22] A. Alberdi, A. Aztiria, and A. Basarab, “On the early diagnosis of Alzheimer’s Disease from multimodal signals: A survey,” Artif. Intell. Med., vol. 71, pp. 1–29, 2016.
  • [23] R. Cassani, M. Estarellas, R. San-Martin, F. J. Fraga, and T. H. Falk, “Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment,” Dis. Markers, vol. 2018, 2018.
  • [24] B. Oltu, M. F. Ak\csahin, and S. Kibaro\uglu, “A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection,” Biomed. Signal Process. Control, vol. 63, p. 102223, 2021.
Year 2022, Volume: 13 Issue: 2, 213 - 220, 28.06.2022
https://doi.org/10.24012/dumf.1092569

Abstract

References

  • [1] V. Bairagi, “EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet based features,” Int. J. Inf. Technol., vol. 10, no. 3, pp. 403–412, 2018.
  • [2] L. R. Trambaiolli, N. Spolaôr, A. C. Lorena, R. Anghinah, and J. R. Sato, “Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease,” Clin. Neurophysiol., vol. 128, no. 10, pp. 2058–2067, 2017.
  • [3] R. H. Blank, “Alzheimer’s Disease and Other Dementias: An Introduction,” in Social \& Public Policy of Alzheimer’s Disease in the United States, Springer, 2019, pp. 1–26.
  • [4] N. N. Kulkarni and V. K. Bairagi, “Extracting salient features for EEG-based diagnosis of Alzheimer’s disease using support vector machine classifier,” IETE J. Res., vol. 63, no. 1, pp. 11–22, 2017.
  • [5] S. J. Ruiz-Gómez et al., “Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment,” Entropy, vol. 20, no. 1, p. 35, 2018.
  • [6] J. P. Amezquita-Sanchez, N. Mammone, F. C. Morabito, S. Marino, and H. Adeli, “A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals,” J. Neurosci. Methods, vol. 322, pp. 88–95, 2019.
  • [7] N. Kulkarni, “Use of complexity based features in diagnosis of mild Alzheimer disease using EEG signals,” Int. J. Inf. Technol., vol. 10, no. 1, pp. 59–64, 2018.
  • [8] K. D. Tzimourta et al., “EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions,” Brain Sci., vol. 9, no. 4, p. 81, 2019.
  • [9] M. S. Safi and S. M. M. Safi, “Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters,” Biomed. Signal Process. Control, vol. 65, p. 102338, 2021.
  • [10] U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Syst. Appl., vol. 38, no. 10, pp. 13475–13481, 2011.
  • [11] B. Hjorth, “EEG analysis based on time domain properties,” Electroencephalogr. Clin. Neurophysiol., vol. 29, no. 3, pp. 306–310, 1970.
  • [12] A. M. Pineda, F. M. Ramos, L. E. Betting, and A. S. L. O. Campanharo, “Quantile graphs for EEG-based diagnosis of Alzheimer’s disease,” PLoS One, vol. 15, no. 6, p. e0231169, 2020.
  • [13] B. R. Bakshi, “Multiscale PCA with application to multivariate statistical process monitoring,” AIChE J., vol. 44, no. 7, pp. 1596–1610, 1998.
  • [14] P. Jahankhani, V. Kodogiannis, and K. Revett, “EEG signal classification using wavelet feature extraction and neural networks,” in IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06), 2006, pp. 120–124.
  • [15] H. U. Amin et al., “Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques,” Australas. Phys. \& Eng. Sci. Med., vol. 38, no. 1, pp. 139–149, 2015.
  • [16] R. C. Gonzalez, Digital image processing. Pearson education india, 2009.
  • [17] N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat., vol. 46, no. 3, pp. 175–185, 1992.
  • [18] W. H. Organization and others, Dementia: a public health priority. World Health Organization, 2012.
  • [19] J. Jeong, “EEG dynamics in patients with Alzheimer’s disease,” Clin. Neurophysiol., vol. 115, no. 7, pp. 1490–1505, 2004.
  • [20] C. Patterson and others, “World alzheimer report 2018,” 2018.
  • [21] J. Dauwels, F. Vialatte, and A. Cichocki, “Diagnosis of Alzheimer’s disease from EEG signals: where are we standing?,” Curr. Alzheimer Res., vol. 7, no. 6, pp. 487–505, 2010.
  • [22] A. Alberdi, A. Aztiria, and A. Basarab, “On the early diagnosis of Alzheimer’s Disease from multimodal signals: A survey,” Artif. Intell. Med., vol. 71, pp. 1–29, 2016.
  • [23] R. Cassani, M. Estarellas, R. San-Martin, F. J. Fraga, and T. H. Falk, “Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment,” Dis. Markers, vol. 2018, 2018.
  • [24] B. Oltu, M. F. Ak\csahin, and S. Kibaro\uglu, “A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection,” Biomed. Signal Process. Control, vol. 63, p. 102223, 2021.
There are 24 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Zülfikar ASLAN 0000-0002-2706-5715

Early Pub Date June 28, 2022
Publication Date June 28, 2022
Submission Date March 24, 2022
Published in Issue Year 2022 Volume: 13 Issue: 2

Cite

IEEE Z. ASLAN, “EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi”, DUJE, vol. 13, no. 2, pp. 213–220, 2022, doi: 10.24012/dumf.1092569.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456