The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer’s disease_Journal of Biomedical Engineering

Authors：

LIU Yi ^1,2 , LI Zhenyang ^1,2 , WEI Zhiwei ^1,2 , XU Yonghong ^1,2 , XIE Ping ^1,2,3 , WANG Yulin ⁴ , LIU Qinshuang ⁴ ,  LI Xin ^1,2

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China;
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P. R. China;
3. Institute of Health and Wellness Industry Technology, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China;
4. Qinhuangdao First Hospital, Qinhuangdao, Hebei 066004, P. R. China;

Corresponding author：

Keywords：

Alzheimer’s disease; Electroencephalogram diagnosis; Neural network; Deep learning

DOI：

10.7507/1001-5515.202201001

Video：

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The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.

Citation： LIU Yi, LI Zhenyang, WEI Zhiwei, XU Yonghong, XIE Ping, WANG Yulin, LIU Qinshuang, LI Xin. The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer’s disease. Journal of Biomedical Engineering, 2022, 39(6): 1233-1239, 1246. doi: 10.7507/1001-5515.202201001 Copy

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2.	2020 Alzheimer’s disease facts and figures. Alzheimers Dement, 2020. DOI: 10.1002/alz.12068.
3.	Bliss A V, Lynch C, Dabas L, et al. From plan to impact III: Maintaining dementia as a priority in unprecedented times. London: Alzheimer’s Disease International, 2020.
4.	Cahill S. WHO's global action plan on the public health response to dementia: some challenges and opportunities. Aging Mental Health, 2020, 24(2): 197-199.
5.	王英全, 梁景宏, 贾瑞霞, 等. 2020-2050年中国阿尔茨海默病患病情况预测研究. 阿尔茨海默病及相关病杂志, 2019, 2(1): 289-298.
6.	Patterson C. World Alzheimer Report 2018 - The state of the art of dementia research: new frontiers. London: Alzheimer’s Disease International (ADI), 2018.
7.	中华医学会神经病学分会痴呆与认知障碍学组. 阿尔茨海默病源性轻度认知障碍诊疗中国专家共识2021. 中华神经科杂志, 2022, 55(5): 421-440.
8.	Prem K A, Singh N, Nair D, et al. Neuronal PET tracers for Alzheimer's disease. Biochem Biophys Res Commun, 2022, 587: 58-62.
9.	Zhang Y, Teng Q, Liu Y, et al. Diagnosis of Alzheimer's disease based on regional attention with sMRI gray matter slices. J Neurosci Methods, 2022, 365: 109376.
10.	Ni Y C, Tseng F P, Pai M C, et al. The feasibility of differentiating lewy body dementia and Alzheimer's disease by deep learning using ECD SPECT images. Diagnostics (Basel), 2021, 11(11): 2091.
11.	Wang P, Wang Z, Wang J, et al. Altered homotopic functional connectivity within white matter in the early stages of Alzheimer's disease. Front Neurosci, 2021, 15: 697493.
12.	Delaby C, Teunissen C E, Blennow K, et al. Clinical reporting following the quantification of cerebrospinal fluid biomarkers in Alzheimer's disease: an international overview. Alzheimers Dement, 2022, 18(10): 1868-1879.
13.	Liu N, Zhang T, Ma L, et al. Vitamin D receptor gene polymorphisms and risk of Alzheimer disease and mild cognitive impairment: a systematic review and meta-analysis. Adv Nutr, 2021, 12(6): 2255-2264.
14.	Morabito F C, Ieracitano C, Mammone N. An explainable artificial intelligence approach to study MCI to AD conversion via HD-EEG processing. Clin EEG Neurosci, 2021: 15500594211063662.
15.	Soriano-Segura P, Ianez E, Ortiz M, et al. Detection of the intention of direction changes during gait through EEG signals. Int J Neural Syst, 2021, 31(11): 2150015.
16.	Li S, Duan J, Sun Y, et al. Exploring fatigue effects on performance variation of intensive brain-computer interface practice. Front Neurosci, 2021, 15: 773790.
17.	Gaubert S, Raimondo F, Houot M, et al. EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease. Brain, 2019, 142(7): 2096-2112.
18.	Zhang H, Geng X, Wang Y, et al. The significance of EEG alpha oscillation spectral power and beta oscillation phase synchronization for diagnosing probable Alzheimer disease. Front Aging Neurosci, 2021, 13: 631587.
19.	Abazid M, Houmani N, Boudy J, et al. A comparative study of functional connectivity measures for brain network analysis in the context of AD detection with EEG. Entropy (Basel), 2021, 23(11): 1553.
20.	Leijnen S, Veen F. The Neural Network Zoo. Proceedings, 2020, 47(1): 9.
21.	Bevilacqua V, Salatino A A, Di L C, et al. Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers// 2015 International Joint Conference on Neural Networks (IJCNN). Killarney, IEEE, 2015: 1-5.
22.	Ieracitano C, Mammone N, Hussain A, et al. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw, 2020, 123: 176-190.
23.	Torrents-Barrena J, Lazar P, Jayapathy R, et al. Complex wavelet algorithm for computer-aided diagnosis of Alzheimer's disease. Electron Lett, 2015, 51: 1566-1568.
24.	Triggiani A I, Bevilacqua V, Brunetti A, et al. Classification of healthy subjects and Alzheimer's disease patients with dementia from cortical sources of resting state EEG rhythms: a study using artificial neural networks. Front Neurosci, 2017, 10: 604.
25.	Ruiz-Gomez S J, Gomez C, Poza J, et al. Automated multiclass classification of spontaneous EEG activity in Alzheimer's disease and mild cognitive impairment. Entropy (Basel), 2018, 20(1): 35.
26.	Leracitano C, Mammone N, Bramanti A, et al. A time-frequency based machine learning system for brain states classification via EEG signal processing// 2019 International Joint Conference on Neural Networks (IJCNN). Budapest: IEEE, 2019: 1-8.
27.	Santos Toural J E, Montoya Pedrón A, Marañón E J. A wavelet entropy based methodology for classification among healthy, mild cognitive impairment and Alzheimer's disease people// Nyström I, Hernández Heredia Y, Milián Núñez V. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Cham: Springer, 2019: 589-598.
28.	Amezquita-Sanchez J P, Mammone N, Morabito F C, et al. A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals. J Neurosci Methods, 2019, 322: 88-95.
29.	Santos Toural J E, Montoya Pedrón A, Marañón R E J. Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power. Pattern Anal Applic, 2021, 24: 413–422.
30.	Goli P, Rad E M, Ghandehari K, et al. Early assessment of mild Alzheimer's disease using Elman neural network, LDA and SVM methods. Mach Learn Res, 2017, 2(4): 148-151.
31.	Alessandrini M, Biagetti G, Crippa P, et al. EEG-based Alzheimer's disease recognition using robust-PCA and LSTM recurrent neural network. Sensors (Basel), 2022, 22(10): 3696.
32.	Gkenios G, Latsiou K, Diamantaras K, et al. Diagnosis of Alzheimer's disease and mild cognitive impairment using EEG and recurrent neural networks. Annu Int Conf IEEE Eng Med Biol Soc, 2022, 2022: 3179-3182.
33.	Alvi A M, Siuly S, Wang H. A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans Emerg Top Comput Intell, 2022, 2022: 1-14.
34.	Alvi A M, Siuly S, Wang H, et al. A deep learning based framework for diagnosis of mild cognitive impairment. Knowl-Based Syst, 2022, 248: 108815.
35.	Geng D, Wang C, Fu Z, et al. Sleep EEG-based approach to detect mild cognitive impairment. Front Aging Neurosci, 2022, 14: 865558.
36.	Ieracitano C, Mammone N, Bramanti A, et al. A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing, 2019, 323: 96-107.
37.	Ieracitano C, Mammone N, Hussain A, et al. A convolutional neural network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia// 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020: 1-8.
38.	You Z, Zeng R H, Lan X Y, et al. Alzheimer's disease classification with a cascade neural network. Front Public Health, 2020, 8: 584387.
39.	Wen D, Zhou Y H, Li P, et al. Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network. J Neural Eng, 2020, 17(3): 036005.
40.	Wen D, Li P, Zhou Y H, et al. Feature classification method of resting-state EEG signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on multi-view convolutional neural network. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(8): 1702-1709.
41.	Wen D, Li P, Li X L, et al. The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method. Neural Netw, 2020, 124: 373-382.
42.	Chriskos P, Frantzidis C A, Papanastasiou E, et al. Applications of convolutional neural networks in neurodegeneration and physiological aging. Int J Psychophysiol, 2021, 159: 1-10.
43.	Huggins C J, Escudero J, Parra M A, et al. Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing. J Neural Eng, 2021, 18(4). DOI: 10.1088/1741-2552/ac05d8.
44.	Li X, Liu Y, Kang J N, et al. Identifying amnestic mild cognitive impairment with convolutional neural network adapted to the spectral entropy heat map of the EEG. Front Hum Neurosci, 2022, 16: 924222.
45.	Wen D, Cheng Z H, Li J J, et al. Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network. J Neurosci Methods, 2021, 363: 109353.
46.	Zhao Y, He L. Deep learning in the EEG diagnosis of Alzheimer's disease// Jawahar C, Shan S. 12th Asian Conference on Computer Vision (ACCV). Cham: Springer, 2015, 9008: 340-353.
47.	Jiang J, Yan Z, Shen T, et al. Use of deep belief network model to discriminate mild cognitive impairment and normal controls based on EEG, eye movement signals and neuropsychological tests. J Med Imag Health In, 2019, 9: 1978-1985.
48.	Bi X, Wang H B. Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning. Neural Netw, 2019, 114: 119-135.
49.	Cicalese P A, Li R, Ahmadi M B, et al. An EEG-fNIRS hybridization technique in the four-class classification of Alzheimer's disease. J Neurosci Methods, 2020, 336: 108618.
50.	Zhang J, Gao Y, He X, et al. Identifying parkinson's disease with mild cognitive impairment by using combined MR imaging and electroencephalogram. Eur Radiol, 2021, 31(10): 7386-7394.
51.	Jia Z, Ji J, Zhou X, et al. Hybrid spiking neural network for sleep electroencephalogram signals. Sci China Inf Sci, 2022, 65: 140403.

1. World Health Organization. Dementia (2021-7-7) [2022-11-15]. https: //www.who.int/news-room/fact-sheets/detail/dementia.
2. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement, 2020. DOI: 10.1002/alz.12068.
3. Bliss A V, Lynch C, Dabas L, et al. From plan to impact III: Maintaining dementia as a priority in unprecedented times. London: Alzheimer’s Disease International, 2020.
4. Cahill S. WHO's global action plan on the public health response to dementia: some challenges and opportunities. Aging Mental Health, 2020, 24(2): 197-199.
5. 王英全, 梁景宏, 贾瑞霞, 等. 2020-2050年中国阿尔茨海默病患病情况预测研究. 阿尔茨海默病及相关病杂志, 2019, 2(1): 289-298.
6. Patterson C. World Alzheimer Report 2018 - The state of the art of dementia research: new frontiers. London: Alzheimer’s Disease International (ADI), 2018.
7. 中华医学会神经病学分会痴呆与认知障碍学组. 阿尔茨海默病源性轻度认知障碍诊疗中国专家共识2021. 中华神经科杂志, 2022, 55(5): 421-440.
8. Prem K A, Singh N, Nair D, et al. Neuronal PET tracers for Alzheimer's disease. Biochem Biophys Res Commun, 2022, 587: 58-62.
9. Zhang Y, Teng Q, Liu Y, et al. Diagnosis of Alzheimer's disease based on regional attention with sMRI gray matter slices. J Neurosci Methods, 2022, 365: 109376.
10. Ni Y C, Tseng F P, Pai M C, et al. The feasibility of differentiating lewy body dementia and Alzheimer's disease by deep learning using ECD SPECT images. Diagnostics (Basel), 2021, 11(11): 2091.
11. Wang P, Wang Z, Wang J, et al. Altered homotopic functional connectivity within white matter in the early stages of Alzheimer's disease. Front Neurosci, 2021, 15: 697493.
12. Delaby C, Teunissen C E, Blennow K, et al. Clinical reporting following the quantification of cerebrospinal fluid biomarkers in Alzheimer's disease: an international overview. Alzheimers Dement, 2022, 18(10): 1868-1879.
13. Liu N, Zhang T, Ma L, et al. Vitamin D receptor gene polymorphisms and risk of Alzheimer disease and mild cognitive impairment: a systematic review and meta-analysis. Adv Nutr, 2021, 12(6): 2255-2264.
14. Morabito F C, Ieracitano C, Mammone N. An explainable artificial intelligence approach to study MCI to AD conversion via HD-EEG processing. Clin EEG Neurosci, 2021: 15500594211063662.
15. Soriano-Segura P, Ianez E, Ortiz M, et al. Detection of the intention of direction changes during gait through EEG signals. Int J Neural Syst, 2021, 31(11): 2150015.
16. Li S, Duan J, Sun Y, et al. Exploring fatigue effects on performance variation of intensive brain-computer interface practice. Front Neurosci, 2021, 15: 773790.
17. Gaubert S, Raimondo F, Houot M, et al. EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease. Brain, 2019, 142(7): 2096-2112.
18. Zhang H, Geng X, Wang Y, et al. The significance of EEG alpha oscillation spectral power and beta oscillation phase synchronization for diagnosing probable Alzheimer disease. Front Aging Neurosci, 2021, 13: 631587.
19. Abazid M, Houmani N, Boudy J, et al. A comparative study of functional connectivity measures for brain network analysis in the context of AD detection with EEG. Entropy (Basel), 2021, 23(11): 1553.
20. Leijnen S, Veen F. The Neural Network Zoo. Proceedings, 2020, 47(1): 9.
21. Bevilacqua V, Salatino A A, Di L C, et al. Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers// 2015 International Joint Conference on Neural Networks (IJCNN). Killarney, IEEE, 2015: 1-5.
22. Ieracitano C, Mammone N, Hussain A, et al. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw, 2020, 123: 176-190.
23. Torrents-Barrena J, Lazar P, Jayapathy R, et al. Complex wavelet algorithm for computer-aided diagnosis of Alzheimer's disease. Electron Lett, 2015, 51: 1566-1568.
24. Triggiani A I, Bevilacqua V, Brunetti A, et al. Classification of healthy subjects and Alzheimer's disease patients with dementia from cortical sources of resting state EEG rhythms: a study using artificial neural networks. Front Neurosci, 2017, 10: 604.
25. Ruiz-Gomez S J, Gomez C, Poza J, et al. Automated multiclass classification of spontaneous EEG activity in Alzheimer's disease and mild cognitive impairment. Entropy (Basel), 2018, 20(1): 35.
26. Leracitano C, Mammone N, Bramanti A, et al. A time-frequency based machine learning system for brain states classification via EEG signal processing// 2019 International Joint Conference on Neural Networks (IJCNN). Budapest: IEEE, 2019: 1-8.
27. Santos Toural J E, Montoya Pedrón A, Marañón E J. A wavelet entropy based methodology for classification among healthy, mild cognitive impairment and Alzheimer's disease people// Nyström I, Hernández Heredia Y, Milián Núñez V. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Cham: Springer, 2019: 589-598.
28. Amezquita-Sanchez J P, Mammone N, Morabito F C, et al. A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals. J Neurosci Methods, 2019, 322: 88-95.
29. Santos Toural J E, Montoya Pedrón A, Marañón R E J. Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power. Pattern Anal Applic, 2021, 24: 413–422.
30. Goli P, Rad E M, Ghandehari K, et al. Early assessment of mild Alzheimer's disease using Elman neural network, LDA and SVM methods. Mach Learn Res, 2017, 2(4): 148-151.
31. Alessandrini M, Biagetti G, Crippa P, et al. EEG-based Alzheimer's disease recognition using robust-PCA and LSTM recurrent neural network. Sensors (Basel), 2022, 22(10): 3696.
32. Gkenios G, Latsiou K, Diamantaras K, et al. Diagnosis of Alzheimer's disease and mild cognitive impairment using EEG and recurrent neural networks. Annu Int Conf IEEE Eng Med Biol Soc, 2022, 2022: 3179-3182.
33. Alvi A M, Siuly S, Wang H. A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans Emerg Top Comput Intell, 2022, 2022: 1-14.
34. Alvi A M, Siuly S, Wang H, et al. A deep learning based framework for diagnosis of mild cognitive impairment. Knowl-Based Syst, 2022, 248: 108815.
35. Geng D, Wang C, Fu Z, et al. Sleep EEG-based approach to detect mild cognitive impairment. Front Aging Neurosci, 2022, 14: 865558.
36. Ieracitano C, Mammone N, Bramanti A, et al. A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing, 2019, 323: 96-107.
37. Ieracitano C, Mammone N, Hussain A, et al. A convolutional neural network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia// 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020: 1-8.
38. You Z, Zeng R H, Lan X Y, et al. Alzheimer's disease classification with a cascade neural network. Front Public Health, 2020, 8: 584387.
39. Wen D, Zhou Y H, Li P, et al. Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network. J Neural Eng, 2020, 17(3): 036005.
40. Wen D, Li P, Zhou Y H, et al. Feature classification method of resting-state EEG signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on multi-view convolutional neural network. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(8): 1702-1709.
41. Wen D, Li P, Li X L, et al. The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method. Neural Netw, 2020, 124: 373-382.
42. Chriskos P, Frantzidis C A, Papanastasiou E, et al. Applications of convolutional neural networks in neurodegeneration and physiological aging. Int J Psychophysiol, 2021, 159: 1-10.
43. Huggins C J, Escudero J, Parra M A, et al. Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing. J Neural Eng, 2021, 18(4). DOI: 10.1088/1741-2552/ac05d8.
44. Li X, Liu Y, Kang J N, et al. Identifying amnestic mild cognitive impairment with convolutional neural network adapted to the spectral entropy heat map of the EEG. Front Hum Neurosci, 2022, 16: 924222.
45. Wen D, Cheng Z H, Li J J, et al. Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network. J Neurosci Methods, 2021, 363: 109353.
46. Zhao Y, He L. Deep learning in the EEG diagnosis of Alzheimer's disease// Jawahar C, Shan S. 12th Asian Conference on Computer Vision (ACCV). Cham: Springer, 2015, 9008: 340-353.
47. Jiang J, Yan Z, Shen T, et al. Use of deep belief network model to discriminate mild cognitive impairment and normal controls based on EEG, eye movement signals and neuropsychological tests. J Med Imag Health In, 2019, 9: 1978-1985.
48. Bi X, Wang H B. Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning. Neural Netw, 2019, 114: 119-135.
49. Cicalese P A, Li R, Ahmadi M B, et al. An EEG-fNIRS hybridization technique in the four-class classification of Alzheimer's disease. J Neurosci Methods, 2020, 336: 108618.
50. Zhang J, Gao Y, He X, et al. Identifying parkinson's disease with mild cognitive impairment by using combined MR imaging and electroencephalogram. Eur Radiol, 2021, 31(10): 7386-7394.
51. Jia Z, Ji J, Zhou X, et al. Hybrid spiking neural network for sleep electroencephalogram signals. Sci China Inf Sci, 2022, 65: 140403.

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