Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model_Journal of Biomedical Engineering

Authors：

 LI Xin ^1,2 , LI Zhenyang ^1,2 , LIU Yi ^1,2 , SU Rui ^1,2 , XU Yonghong ^1,2 , JING Jun ^1,2 ,  YIN Liyong ³

1. School of Electrical 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. Qinhuangdao First Hospital, Qinhuangdao, Hebei 066004, P. R. China;

Corresponding author：

LI Xin, Email: yddylixin@ysu.edu.cn; YIN Liyong, Email: yinliyong@163.com

Keywords：

Mild cognitive impairment; Bidirectional long short-term memory network; Bayesian optimization; Multi-feature fusion

DOI：

10.7507/1001-5515.202205005

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

Citation： LI Xin, LI Zhenyang, LIU Yi, SU Rui, XU Yonghong, JING Jun, YIN Liyong. Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model. Journal of Biomedical Engineering, 2023, 40(3): 450-457. doi: 10.7507/1001-5515.202205005 Copy

1.	Zhao L. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement, 2020, 16(3): 391-460.
2.	Jessen F, Amariglio R E, Buckley R F, et al. The characterisation of subjective cognitive decline. Lancet Neurol, 2020, 19(3): 271-278.
3.	Ismail Z, Smith E E, Geda Y, et al. Neuropsychiatric symptoms as early manifestations of emergent dementia: Provisional diagnostic criteria for mild behavioral impairment. Alzheimers Dement, 2016, 12(2): 195-202.
4.	Atri A. The Alzheimer’s disease clinical spectrum: diagnosis and management. Med Clin North Am, 2019, 103(2): 263-293.
5.	Creese B, Brooker H, Ismail Z, et al. Mild behavioral impairment as a marker of cognitive decline in cognitively normal older adults. Am J Geriat Psychiatry, 2019, 27(8): 823-834.
6.	李恒智, 文冬, 魏振豪, 等. 轻度认知障碍患者EEG动力学特征提取与分类方法研究进展. 中国生物医学工程学报, 2019, 38(3): 348-354.
7.	林伟铭, 袁江南, 冯陈伟, 等. 基于极限学习机的阿尔兹海默病辅助诊断. 中国生物医学工程学报, 2020, 39(3): 288-294.
8.	Tanveer M, Richhariya B, Khan R U, et al. Machine learning techniques for the diagnosis of Alzheimer’s disease: a review. ACM Trans Multimed Comput Commun Appl, 2020, 16(1): 35.
9.	Zhang Yuanpeng, Wang Shuihua, Xia Kaijian, et al. Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion. Inf Fusion, 2021, 66: 170-183.
10.	Yin J, Cao J, Siuly S, et al. An integrated MCI detection framework based on spectral-temporal analysis. Int J Auto Comput, 2019, 16(6): 786-799.
11.	Siuly S, Alçin Ö F, Kabir E, et al. A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(9): 1966-1976.
12.	Liu M, Zhang J, Adeli E, et al. Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng, 2018, 66(5): 1195-1206.
13.	Zhou T, Thung K H, Zhu X, et al. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Hum Brain Mapp, 2019, 40(3): 1001-1016.
14.	Islam J, Zhang Y. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inf, 2018, 5(2): 1-14.
15.	Jo T, Nho K, Saykin A J. Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci, 2019, 11: 220.
16.	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.
17.	曾安, 贾龙飞, 潘丹. 基于卷积神经网络和集成学习的阿尔茨海默症早期诊断. 生物医学工程学杂志, 2019, 36(5): 711-719.
18.	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
19.	Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput, 2019, 31(7): 1235-1270.
20.	Pan Q, Wang S, Zhang J. Prediction of Alzheimer’s disease based on bidirectional LSTM. J Phys Conf Ser, 2019, 1187(5): 052030.
21.	Michielli N, Acharya U R, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med, 2019, 106: 71-81.
22.	Tsiouris K M, Pezoulas V C, Zervakis M, et al. A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med, 2018, 99: 24-37.
23.	Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM networks// Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005. Montreal: IEEE, 2005, 4: 2047-2052.
24.	Liu G, Guo J. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 2019, 337(APR.14): 325-338.
25.	Yan W, Zhang H, Sui J, et al. Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis. Med Image Comput Comput Assist Interv, 2018, 11072: 249-257.
26.	Feng C, Elazab A, Yang P, et al. Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 2019, 7: 63605-63618.
27.	Simons S, Espino P, Abásolo D. Fuzzy entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: is the method superior to sample entropy? Entropy, 2018, 20(1): 21.
28.	Gaurav G, Anand R S, Kumar V. EEG based cognitive task classification using multifractal detrended fluctuation analysis. Cogn Neurodyn, 2021, 15(6): 999-1013.
29.	李昕, 孙小棋, 齐晓英, 等. 面向心理压力评估的脑电信号多重分形去趋势波动分析方法研究. 生物医学工程学杂志, 2017, 34(2): 180-187.
30.	Lee S H, Chan C S, Remagnino P. Multi-organ plant classification based on convolutional and recurrent neural networks. IEEE Trans Image Process, 2018, 27(99): 4287-4301.

1. Zhao L. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement, 2020, 16(3): 391-460.
2. Jessen F, Amariglio R E, Buckley R F, et al. The characterisation of subjective cognitive decline. Lancet Neurol, 2020, 19(3): 271-278.
3. Ismail Z, Smith E E, Geda Y, et al. Neuropsychiatric symptoms as early manifestations of emergent dementia: Provisional diagnostic criteria for mild behavioral impairment. Alzheimers Dement, 2016, 12(2): 195-202.
4. Atri A. The Alzheimer’s disease clinical spectrum: diagnosis and management. Med Clin North Am, 2019, 103(2): 263-293.
5. Creese B, Brooker H, Ismail Z, et al. Mild behavioral impairment as a marker of cognitive decline in cognitively normal older adults. Am J Geriat Psychiatry, 2019, 27(8): 823-834.
6. 李恒智, 文冬, 魏振豪, 等. 轻度认知障碍患者EEG动力学特征提取与分类方法研究进展. 中国生物医学工程学报, 2019, 38(3): 348-354.
7. 林伟铭, 袁江南, 冯陈伟, 等. 基于极限学习机的阿尔兹海默病辅助诊断. 中国生物医学工程学报, 2020, 39(3): 288-294.
8. Tanveer M, Richhariya B, Khan R U, et al. Machine learning techniques for the diagnosis of Alzheimer’s disease: a review. ACM Trans Multimed Comput Commun Appl, 2020, 16(1): 35.
9. Zhang Yuanpeng, Wang Shuihua, Xia Kaijian, et al. Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion. Inf Fusion, 2021, 66: 170-183.
10. Yin J, Cao J, Siuly S, et al. An integrated MCI detection framework based on spectral-temporal analysis. Int J Auto Comput, 2019, 16(6): 786-799.
11. Siuly S, Alçin Ö F, Kabir E, et al. A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(9): 1966-1976.
12. Liu M, Zhang J, Adeli E, et al. Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng, 2018, 66(5): 1195-1206.
13. Zhou T, Thung K H, Zhu X, et al. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Hum Brain Mapp, 2019, 40(3): 1001-1016.
14. Islam J, Zhang Y. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inf, 2018, 5(2): 1-14.
15. Jo T, Nho K, Saykin A J. Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci, 2019, 11: 220.
16. 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.
17. 曾安, 贾龙飞, 潘丹. 基于卷积神经网络和集成学习的阿尔茨海默症早期诊断. 生物医学工程学杂志, 2019, 36(5): 711-719.
18. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
19. Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput, 2019, 31(7): 1235-1270.
20. Pan Q, Wang S, Zhang J. Prediction of Alzheimer’s disease based on bidirectional LSTM. J Phys Conf Ser, 2019, 1187(5): 052030.
21. Michielli N, Acharya U R, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med, 2019, 106: 71-81.
22. Tsiouris K M, Pezoulas V C, Zervakis M, et al. A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med, 2018, 99: 24-37.
23. Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM networks// Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005. Montreal: IEEE, 2005, 4: 2047-2052.
24. Liu G, Guo J. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 2019, 337(APR.14): 325-338.
25. Yan W, Zhang H, Sui J, et al. Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis. Med Image Comput Comput Assist Interv, 2018, 11072: 249-257.
26. Feng C, Elazab A, Yang P, et al. Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 2019, 7: 63605-63618.
27. Simons S, Espino P, Abásolo D. Fuzzy entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: is the method superior to sample entropy? Entropy, 2018, 20(1): 21.
28. Gaurav G, Anand R S, Kumar V. EEG based cognitive task classification using multifractal detrended fluctuation analysis. Cogn Neurodyn, 2021, 15(6): 999-1013.
29. 李昕, 孙小棋, 齐晓英, 等. 面向心理压力评估的脑电信号多重分形去趋势波动分析方法研究. 生物医学工程学杂志, 2017, 34(2): 180-187.
30. Lee S H, Chan C S, Remagnino P. Multi-organ plant classification based on convolutional and recurrent neural networks. IEEE Trans Image Process, 2018, 27(99): 4287-4301.

Previous Article
A method of mental disorder recognition based on visibility graph
Next Article
Automatic sleep staging model based on single channel electroencephalogram signal

Journal of Biomedical Engineering

Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content