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DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS

Year 2023, Volume: 28 Issue: 1, 141 - 152, 30.04.2023
https://doi.org/10.17482/uumfd.1142345

Abstract

Alzheimer's disease is a complex brain disease and is also the most common form of dementia that leads to impaired social and intellectual abilities. The disease only manifests itself with a simple forgetfulness, as the disease progresses, the patient forgets the recent events, cannot recognize his family members and close environment, and becomes in need of care in the last stage. Early detection is therefore crucial for medical intervention to prevent brain injury and prolong everyday functioning. In this study is aimed to detection of Alzheimer’s disease from EEG signals using the multitaper and ensemble learning methods. The dataset comprises of 24 healthy people and 24 Alzheimer's patients' EEG signals. 49 features were extracted by calculating the power spectral density (PSD) of the frequencies of the EEG signals between 1-49 Hz using the multitaper method. Then, the performances of AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost, and Bagging ensemble learning algorithms were compared. As a result of experiments, the Logit Boost algorithm has the highest performance. The algorithm has achieved a promising performance of 93.04% accuracy, 93.09% f1-score, 92.75% sensitivity, 93.43% precision, and 93.33% specificity.

References

  • 1. Agarwal, S. and Chowdary, C. R. (2020) A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection. Expert Systems with Applications, 146, 113160. doi: 10.1016/j.eswa.2019.113160
  • 2. Akcan, F. and Sertbaş, A. (2021) Topluluk öğrenmesi yöntemleri ile göğüs kanseri teşhisi. Electronic Turkish Studies, 16(2).512-527. doi: 10.7827/TurkishStudies
  • 3. Amezquita-Sanchez, J. P., Mammone, N., Morabito, F. C., Marino, S., and Adeli, H. (2019) A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. Journal of Neuroscience Methods, 322, 88-95. doi: 10.1016/j.jneumeth.2019.04.013
  • 4. Amini, M., Pedram, M. M., Moradi, A., and Ouchani, M. (2021) Diagnosis of Alzheimer’s disease by time-dependent power spectrum descriptors and convolutional neural network using EEG signal. Computational and Mathematical Methods in Medicine, 2021, 1-17. doi: 10.1155/2021/5511922
  • 5. Aslan, Z. (2022) EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(2), 213-220. doi: 10.24012/dumf.1092569
  • 6. Bairagi, V. (2018) EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet-based features. International Journal of Information Technology, 10(3), 403-412. doi: 10.1007/s41870-018-0165-5
  • 7. Balan, P. S. and Sunny, L. E. (2018) Survey on feature extraction techniques in image processing. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 6, 217-222. doi: 10.22214/ijraset.2018.3035
  • 8. Blennow, K. (2010) PL. 02.01 CSF biomarkers in Alzheimer's disease–use in clinical diagnosis and to monitor treatment effects. European Neuropsychopharmacology, (20), S159. doi: 10.1016/S0924-977X(10)70115-2
  • 9. Breiman, L. (1996). Bagging predictors. Machine Learning, 24:2, 123-140
  • 10. Cai, Q. and Jeong, Y. Y. (2020) Mitophagy in Alzheimer’s disease and other age-related neurodegenerative diseases. Cells, 9(1), 1-28. doi: 10.3390/cells9010150
  • 11. Pineda, A. M., Ramos, F. M., Betting, L. E., Campanharo, A. S. (2020). Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. Plos One, 15(6), e0231169. Data from: https://osf.io/s74qf/. doi: 10.1371/journal.pone.0231169
  • 12. Candy, J. V. (2019) Multitaper spectral estimation: An alternative to the welch periodogram approach (No. LLNL-TR-788954). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).
  • 13. Dong, X., Yu, Z., Cao, W., Shi, Y., and Ma, Q. (2020) A survey on ensemble learning. Frontiers of Computer Science, 14(2), 241-258. doi: 10.1007/s11704-019-8208-z
  • 14. Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A.,Bramanti, P., and De Cola, M. C. (2018) Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Medical Informatics and Decision Making, 18(1), 1-10. doi: 10.1186/s12911-018-0613-y
  • 15. Freund, Y., and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. doi: 10.1006/jcss.1997.1504
  • 16. Guo, J., Wang, Z., Liu, R., Huang, Y., Zhang, N., and Zhang, R. (2020) Memantine, donepezil, or combination therapy—What is the best therapy for Alzheimer’s disease? A network meta‐analysis. Brain and Behavior, 10(11), e01831, 1-13. doi: 10.1002/brb3.1831
  • 17. Güneç, K., Kasım, Ö., Tosun, M., and Büyükköroğlu, E. (2021) Estimation of pain threshold from EEG signals of subjects in physical therapy using long-short-term memory deep learning model. Uludağ University Journal of the Faculty of Engineering, 26(2), 447- 460. doi: 10.17482/uumfd.883100
  • 18. Klepl, D., He, F., Wu, M., De Marco, M., Blackburn, D. J., and Sarrigiannis, P. G. (2021) Characterising Alzheimer’s disease with EEG-based energy landscape analysis. IEEE Journal of Biomedical and Health Informatics, 26(3), 992-1000. doi: 10.1109/JBHI.2021.3105397
  • 19. Kulkarni, N. (2018) Use of complexity-based features in diagnosis of mild Alzheimer disease using EEG signals. International Journal of Information Technology, 10(1), 59-64. doi: 10.1007/s41870-017-0057-0
  • 20. Matloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., Abbas, S., and Soomro, T. R. (2021) Software defect prediction using ensemble learning: A systematic literature review. IEEE Access. 9, 98754-98771. doi: 10.1109/ACCESS.2021.3095559
  • 21. Özmen, N. G., Durmuş, E., and Sadreddini, Z. (2017) Müzik sınıflandırması beyin bilgisayar arayüzü uygulamaları için bir alternatif olabilir mi?. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 22(2), 11-22. doi: 10.17482/uumfd.335419
  • 22. Pineda, A. M., Ramos, F. M., Betting, L. E., and Campanharo, A. S. (2020) Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. Plos One, 15(6), e0231169. doi: 10.1371/journal.pone.0231169
  • 23. Rincy, T. N. and Gupta, R. (2020, February) Ensemble learning techniques and its efficiency in machine learning: A survey. In 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-6). IEEE. doi: 10.1109/IDEA49133.2020.9170675
  • 24. Rodrigues, P. M., Bispo, B. C., Garrett, C., Alves, D., Teixeira, J. P., and Freitas, D. (2021) Lacsogram: A new EEG tool to diagnose Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics, 25(9), 3384-3395. doi: 10.1109/JBHI.2021.3069789
  • 25. Ruiz-Gómez, S. J., Gómez, C., Poza, J., Gutiérrez-Tobal, G. C., Tola-Arribas, M. A., Cano, M., and Hornero, R. (2018) Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy, 20(1), 35. doi: doi.org/10.3390/e20010035
  • 26. Smailovic, U. and Jelic, V. (2019) Neurophysiological markers of Alzheimer’s disease: quantitative EEG approach. Neurology and Therapy, 8(2), 37-55. doi: 10.1007/s40120-019- 00169-0
  • 27. Thomson, D. J. and Vernon, F. L. (1998, November). Signal extraction via multitaper spectra of nonstationary data. In Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No. 98CH36284) (Vol. 1, pp. 271-275). IEEE. doi: 10.1109/ACSSC.1998.750869
  • 28. Tosun, M. (2021). Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Physical and Engineering Sciences in Medicine, 44(3), 693-702. doi: 10.1007/s13246-021-01018-x
  • 29. Upadhya, S. S., Cheeran, A. N., and Nirmal, J. H. (2018) Thomson Multitaper MFCC and PLP voice features for early detection of Parkinson disease. Biomedical Signal Processing and Control, 46, 293-301. doi: 10.1016/j.bspc.2018.07.019
  • 30. World Health Organization. (2021). Dementia. Access address: https://who.int/newsroom/ fact-sheets/detail/dementia. (Accessed in 20.05.2022).
  • 31. Yıldırım, P., Birant, K. U., Radevski, V., Kut, A., and Birant, D. (2018, May) Comparative analysis of ensemble learning methods for signal classification. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. doi: 10.1109/SIU.2018.8404601
  • 32. Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., and Saeed, J. (2020) A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction Journal of Applied Science and Technology Trends, 1(2), 56-70. doi: 10.38094/jastt1224

Multitaper ve Topluluk Öğrenme Yöntemlerinin Kullanılarak Elektroensefalografi (EEG) Sinyallerinden Alzheimer Hastalığının Tespiti

Year 2023, Volume: 28 Issue: 1, 141 - 152, 30.04.2023
https://doi.org/10.17482/uumfd.1142345

Abstract

Alzheimer hastalığı karmaşık bir beyin hastalığıdır, aynı zamanda sosyal ve entelektüel yeteneklerde bozulmaya yol açan demansın en yaygın şeklidir. Hastalık sadece basit bir unutkanlıkla kendini gösterir, hastalık ilerledikçe hasta son olayları unutur, ailesini ve yakın çevresini tanıyamaz, son aşamada bakıma muhtaç hale gelir. Bu nedenle erken teşhis, beyin hasarını azaltmak ve günlük işleyişi daha uzun süre korumak için tıbbi müdahalede önemli bir rol oynamaktadır. Bu çalışmada, multitaper ve topluluk öğrenme yöntemleri kullanılarak, EEG sinyallerinden Alzheimer hastalığının tespitinin yapılması amaçlanmıştır. Veriseti 24 sağlıklı bireyden ve 24 Alzheimer hastasından kaydedilen EEG sinyallerinden oluşmaktadır. EEG sinyallerinin 1-49 Hz arasındaki frekanslarının güç spektral yoğunluğu (PSD) multitaper yöntemi kullanılarak hesaplanarak, 49 öznitelik çıkarıldı. Daha sonra AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost ve Bagging topluluk öğrenme algoritmalarının performansları karşılaştırıldı. Deneyler sonucunda, Logit Boost algoritması en yüksek performansa sahipti. Algoritma, %93,04 doğruluk, %93,09 f1-skor, %92,75 duyarlılık, %93,43 kesinlik ve %93,33 özgüllük ile umut verici bir performans elde etti.

References

  • 1. Agarwal, S. and Chowdary, C. R. (2020) A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection. Expert Systems with Applications, 146, 113160. doi: 10.1016/j.eswa.2019.113160
  • 2. Akcan, F. and Sertbaş, A. (2021) Topluluk öğrenmesi yöntemleri ile göğüs kanseri teşhisi. Electronic Turkish Studies, 16(2).512-527. doi: 10.7827/TurkishStudies
  • 3. Amezquita-Sanchez, J. P., Mammone, N., Morabito, F. C., Marino, S., and Adeli, H. (2019) A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. Journal of Neuroscience Methods, 322, 88-95. doi: 10.1016/j.jneumeth.2019.04.013
  • 4. Amini, M., Pedram, M. M., Moradi, A., and Ouchani, M. (2021) Diagnosis of Alzheimer’s disease by time-dependent power spectrum descriptors and convolutional neural network using EEG signal. Computational and Mathematical Methods in Medicine, 2021, 1-17. doi: 10.1155/2021/5511922
  • 5. Aslan, Z. (2022) EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(2), 213-220. doi: 10.24012/dumf.1092569
  • 6. Bairagi, V. (2018) EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet-based features. International Journal of Information Technology, 10(3), 403-412. doi: 10.1007/s41870-018-0165-5
  • 7. Balan, P. S. and Sunny, L. E. (2018) Survey on feature extraction techniques in image processing. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 6, 217-222. doi: 10.22214/ijraset.2018.3035
  • 8. Blennow, K. (2010) PL. 02.01 CSF biomarkers in Alzheimer's disease–use in clinical diagnosis and to monitor treatment effects. European Neuropsychopharmacology, (20), S159. doi: 10.1016/S0924-977X(10)70115-2
  • 9. Breiman, L. (1996). Bagging predictors. Machine Learning, 24:2, 123-140
  • 10. Cai, Q. and Jeong, Y. Y. (2020) Mitophagy in Alzheimer’s disease and other age-related neurodegenerative diseases. Cells, 9(1), 1-28. doi: 10.3390/cells9010150
  • 11. Pineda, A. M., Ramos, F. M., Betting, L. E., Campanharo, A. S. (2020). Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. Plos One, 15(6), e0231169. Data from: https://osf.io/s74qf/. doi: 10.1371/journal.pone.0231169
  • 12. Candy, J. V. (2019) Multitaper spectral estimation: An alternative to the welch periodogram approach (No. LLNL-TR-788954). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).
  • 13. Dong, X., Yu, Z., Cao, W., Shi, Y., and Ma, Q. (2020) A survey on ensemble learning. Frontiers of Computer Science, 14(2), 241-258. doi: 10.1007/s11704-019-8208-z
  • 14. Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A.,Bramanti, P., and De Cola, M. C. (2018) Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Medical Informatics and Decision Making, 18(1), 1-10. doi: 10.1186/s12911-018-0613-y
  • 15. Freund, Y., and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. doi: 10.1006/jcss.1997.1504
  • 16. Guo, J., Wang, Z., Liu, R., Huang, Y., Zhang, N., and Zhang, R. (2020) Memantine, donepezil, or combination therapy—What is the best therapy for Alzheimer’s disease? A network meta‐analysis. Brain and Behavior, 10(11), e01831, 1-13. doi: 10.1002/brb3.1831
  • 17. Güneç, K., Kasım, Ö., Tosun, M., and Büyükköroğlu, E. (2021) Estimation of pain threshold from EEG signals of subjects in physical therapy using long-short-term memory deep learning model. Uludağ University Journal of the Faculty of Engineering, 26(2), 447- 460. doi: 10.17482/uumfd.883100
  • 18. Klepl, D., He, F., Wu, M., De Marco, M., Blackburn, D. J., and Sarrigiannis, P. G. (2021) Characterising Alzheimer’s disease with EEG-based energy landscape analysis. IEEE Journal of Biomedical and Health Informatics, 26(3), 992-1000. doi: 10.1109/JBHI.2021.3105397
  • 19. Kulkarni, N. (2018) Use of complexity-based features in diagnosis of mild Alzheimer disease using EEG signals. International Journal of Information Technology, 10(1), 59-64. doi: 10.1007/s41870-017-0057-0
  • 20. Matloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., Abbas, S., and Soomro, T. R. (2021) Software defect prediction using ensemble learning: A systematic literature review. IEEE Access. 9, 98754-98771. doi: 10.1109/ACCESS.2021.3095559
  • 21. Özmen, N. G., Durmuş, E., and Sadreddini, Z. (2017) Müzik sınıflandırması beyin bilgisayar arayüzü uygulamaları için bir alternatif olabilir mi?. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 22(2), 11-22. doi: 10.17482/uumfd.335419
  • 22. Pineda, A. M., Ramos, F. M., Betting, L. E., and Campanharo, A. S. (2020) Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. Plos One, 15(6), e0231169. doi: 10.1371/journal.pone.0231169
  • 23. Rincy, T. N. and Gupta, R. (2020, February) Ensemble learning techniques and its efficiency in machine learning: A survey. In 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-6). IEEE. doi: 10.1109/IDEA49133.2020.9170675
  • 24. Rodrigues, P. M., Bispo, B. C., Garrett, C., Alves, D., Teixeira, J. P., and Freitas, D. (2021) Lacsogram: A new EEG tool to diagnose Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics, 25(9), 3384-3395. doi: 10.1109/JBHI.2021.3069789
  • 25. Ruiz-Gómez, S. J., Gómez, C., Poza, J., Gutiérrez-Tobal, G. C., Tola-Arribas, M. A., Cano, M., and Hornero, R. (2018) Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy, 20(1), 35. doi: doi.org/10.3390/e20010035
  • 26. Smailovic, U. and Jelic, V. (2019) Neurophysiological markers of Alzheimer’s disease: quantitative EEG approach. Neurology and Therapy, 8(2), 37-55. doi: 10.1007/s40120-019- 00169-0
  • 27. Thomson, D. J. and Vernon, F. L. (1998, November). Signal extraction via multitaper spectra of nonstationary data. In Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No. 98CH36284) (Vol. 1, pp. 271-275). IEEE. doi: 10.1109/ACSSC.1998.750869
  • 28. Tosun, M. (2021). Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Physical and Engineering Sciences in Medicine, 44(3), 693-702. doi: 10.1007/s13246-021-01018-x
  • 29. Upadhya, S. S., Cheeran, A. N., and Nirmal, J. H. (2018) Thomson Multitaper MFCC and PLP voice features for early detection of Parkinson disease. Biomedical Signal Processing and Control, 46, 293-301. doi: 10.1016/j.bspc.2018.07.019
  • 30. World Health Organization. (2021). Dementia. Access address: https://who.int/newsroom/ fact-sheets/detail/dementia. (Accessed in 20.05.2022).
  • 31. Yıldırım, P., Birant, K. U., Radevski, V., Kut, A., and Birant, D. (2018, May) Comparative analysis of ensemble learning methods for signal classification. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. doi: 10.1109/SIU.2018.8404601
  • 32. Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., and Saeed, J. (2020) A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction Journal of Applied Science and Technology Trends, 1(2), 56-70. doi: 10.38094/jastt1224
There are 32 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Hanife Göker 0000-0003-0396-7885

Publication Date April 30, 2023
Submission Date July 8, 2022
Acceptance Date February 28, 2023
Published in Issue Year 2023 Volume: 28 Issue: 1

Cite

APA Göker, H. (2023). DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 28(1), 141-152. https://doi.org/10.17482/uumfd.1142345
AMA Göker H. DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS. UUJFE. April 2023;28(1):141-152. doi:10.17482/uumfd.1142345
Chicago Göker, Hanife. “DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28, no. 1 (April 2023): 141-52. https://doi.org/10.17482/uumfd.1142345.
EndNote Göker H (April 1, 2023) DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28 1 141–152.
IEEE H. Göker, “DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS”, UUJFE, vol. 28, no. 1, pp. 141–152, 2023, doi: 10.17482/uumfd.1142345.
ISNAD Göker, Hanife. “DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28/1 (April 2023), 141-152. https://doi.org/10.17482/uumfd.1142345.
JAMA Göker H. DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS. UUJFE. 2023;28:141–152.
MLA Göker, Hanife. “DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 28, no. 1, 2023, pp. 141-52, doi:10.17482/uumfd.1142345.
Vancouver Göker H. DETECTION OF ALZHEIMER’S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS. UUJFE. 2023;28(1):141-52.

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