Intelligence-aided diagnosis of Parkinson’s disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network_Journal of Biomedical Engineering

Authors：

ZHONG Weifeng ^1,6 , LI Zhi ^1,2 , LIU Yan ² , CHENG Chenchen ^2,3 , WANG Yue ⁴ , ZHANG Li ⁵ , XU Shulan ⁵ , JIANG Xu ⁵ , ZHU Jun ⁵ ,  DAI Yakang ²

1. School of Automation, Harbin University of Science and Technology, Harbin 150080, P.R.China;
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P.R.China;
3. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, P.R.China;
4. Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan 250102, P.R.China;
5. Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, P.R.China;
6. Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin 150080, P.R.China;

Corresponding author：

DAI Yakang, Email: daiyk@sibet.ac.cn

Keywords：

few-channel scalp electroencephalogram; time-frequency deep network; Parkinson’s disease; rapid eye movement sleep behavior disorder; intelligence-aided diagnosis

DOI：

10.7507/1001-5515.202009067

Video：

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Aiming at the limitations of clinical diagnosis of Parkinson’s disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor’s detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.

Citation： ZHONG Weifeng, LI Zhi, LIU Yan, CHENG Chenchen, WANG Yue, ZHANG Li, XU Shulan, JIANG Xu, ZHU Jun, DAI Yakang. Intelligence-aided diagnosis of Parkinson’s disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network. Journal of Biomedical Engineering, 2021, 38(6): 1043-1053. doi: 10.7507/1001-5515.202009067 Copy

1.	扈杨, 左丽君, 余舒扬, 等. 帕金森病患者伴很可能的快速眼动睡眠行为障碍和相关因素的研究. 中华临床医师杂志, 2013, 7(12): 5216-5222.
2.	Kim Y, Kim Y E, Park E O, et al. REM sleep behavior disorder portends poor prognosis in Parkinson’s disease: A systematic review. J Clin Neurosci, 2018, 47: 6-13.
3.	杨改清, 徐志强, 胥丽霞, 等. 帕金森病精神病性障碍患者快速眼动睡眠行为障碍研究. 中华行为医学与脑科学杂志, 2019, 28(1): 59-63.
4.	郁婷婷, 丁勇民, 黄卫. 快速眼动睡眠期行为障碍与帕金森病相关性的研究进展. 中华老年医学杂志, 2015, 34(7): 816-818.
5.	胡艳, 王萍. 伴快速眼动睡眠期行为障碍的帕金森病临床研究进展. 中华神经医学杂志, 2017, 16(3): 313-316.
6.	Vendette M, Gagnon J F, Decary A, et al. REM sleep behavior disorder predicts cognitive impairment in Parkinson disease without dementia. Neurology, 2007, 69(19): 1843-1849.
7.	Figorilli M, Marques A R, Meloni M, et al. Diagnosing REM sleep behavior disorder in Parkinson’s disease without a gold standard: a latent-class model study. Sleep, 2020, 43(7): 1-8.
8.	Sateia M J. International classification of sleep disorders-third edition: highlights and modifications. Chest, 2014, 146(5): 1387-1394.
9.	Louis E K S, Boeve B F. REM sleep behavior disorder: Diagnosis, clinical implications, and future directions. Mayo Clin Proc, 2017, 92(11): 1723-1736.
10.	Bolitho S J, Naismith S L, Terpening Z, et al. Investigating rapid eye movement sleep without atonia in Parkinson's disease using the rapid eye movement sleep behavior disorder screening questionnaire. Mov Disord, 2014, 29(6): 736-742.
11.	Chen X, Xu X, Liu A, et al. The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Trans Instrum Meas, 2018, 67(2): 359-370.
12.	覃小雅, 袁媛, 陈彦, 等. 头皮脑电图在迷走神经电刺激治疗难治性癫痫研究中的应用. 生物医学工程学杂志, 2020, 37(4): 699-707.
13.	李昕, 安占周, 李秋月, 等. 加权多重多尺度熵及其在孤独症儿童脑电信号分析中的应用. 生物医学工程学杂志, 2019, 36(1): 33-39,49.
14.	付荣荣, 田永胜, 鲍甜恬. 基于稀疏共空间模式和Fisher判别的单次运动想象脑电信号识别方法. 生物医学工程学杂志, 2019, 36(6): 911-915,923.
15.	胡盼, 张磊, 周蚌艳, 等. 基于独立分量分析的在线脑-机接口系统. 生物医学工程学杂志, 2017, 34(1): 106-114.
16.	Koch H, Christensen J A, Frandsen R, et al. Classification of IRBD and Parkinson’s patients using a general data-driven sleep staging model built on EEG// 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE, 2013: 4275-4278.
17.	Ferini-Strambi L, Fasiello E, Sforza M, et al. Neuropsychological, electrophysiological, and neuroimaging biomarkers for REM behavior disorder. Expert Rev Neurother, 2019, 19(11): 1069-1087.
18.	Bisgaard S, Duun-Christensen B, Kempfner L, et al. EEG recordings as a source for the detection of IRBD// 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE, 2015: 606-609.
19.	Iasemidis L D, Shiau D S, Chaovalitwongse W, et al. Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng, 2003, 50(5): 616-627.
20.	Greenspan H, van Ginneken B, Summers R M. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging, 2016, 35(5): 1153-1159.
21.	Wei L, Lin Y, Wang J, et al. Time-frequency convolutional neural network for automatic sleep stage classification based on single-channel EEG// 2017 29th International Conference on Tools with Artificial Intelligence (ICTAI). Boston: IEEE, 2017: 88-95.
22.	Jadhav P, Rajguru G, Datta D, et al. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybern Biomed Eng, 2020, 40(1): 494-504.
23.	Fraiwan L, Lweesy K, Khasawneh N, et al. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Meth Prog Biomed, 2012, 108(1): 10-19.
24.	Waibel A, Hanazawa T, Hinton G, et al. Phoneme recognition using time-delay neural networks. IEEE Trans Acoust Speech Signal Process, 1989, 37(3): 393-404.
25.	杨婧, 耿辰, 王海林, 等. 基于 DenseNet 的低分辨 CT 影像肺腺癌组织学亚型分类. 浙江大学学报(工学版), 2019, 53(6): 1164-1170.
26.	杨熠, 钱旭升, 周志勇, 等. 采用影像组学的肾肿瘤组织学亚型分类. 浙江大学学报(工学版), 2019, 53(12): 2381-2388.
27.	Truong N D, Nguyen A D, Kuhlmann L, et al. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw, 2018, 105: 104-111.
28.	Usman S M, Khalid S, Aslam M H. Epileptic seizures prediction using deep learning techniques. IEEE Access, 2020, 8: 39998-40007.
29.	Budak U, Bajaj V, Akbulut Y, et al. An effective hybrid model for EEG-based drowsiness detection. IEEE Sens J, 2019, 19(17): 7624-7631.
30.	Yuan L, Cao J. Patients’ EEG data analysis via spectrogram image with a convolution neural network// 2017 International Conference on Intelligent Decision Technologies. Sorrento: Springer, 2017: 13-21.
31.	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
32.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proc Int Conf Learn Representat, 2015: 1-14.
33.	Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift// Proceedings of ICML 2015. France: IMLS, 2015: 448-456.
34.	Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 2014, 15(1): 1929-1958.
35.	Krogh A, Hertz J A. A simple weight decay can improve generalization// J. Moody J, Hanson S, Lippmann R P. Advances in Neural Information Processing Systems 4 (NIPS 1991). UK: MIT, 1992: 950-957.
36.	Chen T, Guestrin C. XGBoost: A scalable tree boosting system// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2016: 785-794.
37.	Ruffini G, Ibaez D, Castellano M, et al. Deep learning with EEG spectrograms in rapid eye movement behavior disorder. Front Neurol, 2019, 10: 806.
38.	Wang Y, Wang Z W, Yang Y C, et al. Validation of the rapid eye movement sleep behavior disorder screening questionnaire in China. J Clin Neurosci, 2015, 22(9): 1420-1424.
39.	程晨晨, 尤波, 刘燕, 等. 基于深度神经网络的个性化睡眠癫痫发作预测. 模式识别与人工智能, 2021, 34(4): 333-342.
40.	刘伟楠, 刘燕, 佟宝同, 等. 基于功率谱的睡眠中癫痫发作预测. 生物医学工程学杂志, 2018, 35(3): 329-336.
41.	Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Int J Comput Vision, 2020, 128(2): 336-359.
42.	Li Y, Yang H, Li J, et al. EEG-based intention recognition with deep recurrent-convolution neural network: performance and channel selection by Grad-CAM. Neurocomputing, 2020, 415: 225-233.
43.	Cooray N, Andreotti F, Lo C, et al. Detection of REM sleep behaviour disorder by automated polysomnography analysis. Clin Neurophysiol, 2019, 130(4): 505-514.

1. 扈杨, 左丽君, 余舒扬, 等. 帕金森病患者伴很可能的快速眼动睡眠行为障碍和相关因素的研究. 中华临床医师杂志, 2013, 7(12): 5216-5222.
2. Kim Y, Kim Y E, Park E O, et al. REM sleep behavior disorder portends poor prognosis in Parkinson’s disease: A systematic review. J Clin Neurosci, 2018, 47: 6-13.
3. 杨改清, 徐志强, 胥丽霞, 等. 帕金森病精神病性障碍患者快速眼动睡眠行为障碍研究. 中华行为医学与脑科学杂志, 2019, 28(1): 59-63.
4. 郁婷婷, 丁勇民, 黄卫. 快速眼动睡眠期行为障碍与帕金森病相关性的研究进展. 中华老年医学杂志, 2015, 34(7): 816-818.
5. 胡艳, 王萍. 伴快速眼动睡眠期行为障碍的帕金森病临床研究进展. 中华神经医学杂志, 2017, 16(3): 313-316.
6. Vendette M, Gagnon J F, Decary A, et al. REM sleep behavior disorder predicts cognitive impairment in Parkinson disease without dementia. Neurology, 2007, 69(19): 1843-1849.
7. Figorilli M, Marques A R, Meloni M, et al. Diagnosing REM sleep behavior disorder in Parkinson’s disease without a gold standard: a latent-class model study. Sleep, 2020, 43(7): 1-8.
8. Sateia M J. International classification of sleep disorders-third edition: highlights and modifications. Chest, 2014, 146(5): 1387-1394.
9. Louis E K S, Boeve B F. REM sleep behavior disorder: Diagnosis, clinical implications, and future directions. Mayo Clin Proc, 2017, 92(11): 1723-1736.
10. Bolitho S J, Naismith S L, Terpening Z, et al. Investigating rapid eye movement sleep without atonia in Parkinson's disease using the rapid eye movement sleep behavior disorder screening questionnaire. Mov Disord, 2014, 29(6): 736-742.
11. Chen X, Xu X, Liu A, et al. The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Trans Instrum Meas, 2018, 67(2): 359-370.
12. 覃小雅, 袁媛, 陈彦, 等. 头皮脑电图在迷走神经电刺激治疗难治性癫痫研究中的应用. 生物医学工程学杂志, 2020, 37(4): 699-707.
13. 李昕, 安占周, 李秋月, 等. 加权多重多尺度熵及其在孤独症儿童脑电信号分析中的应用. 生物医学工程学杂志, 2019, 36(1): 33-39,49.
14. 付荣荣, 田永胜, 鲍甜恬. 基于稀疏共空间模式和Fisher判别的单次运动想象脑电信号识别方法. 生物医学工程学杂志, 2019, 36(6): 911-915,923.
15. 胡盼, 张磊, 周蚌艳, 等. 基于独立分量分析的在线脑-机接口系统. 生物医学工程学杂志, 2017, 34(1): 106-114.
16. Koch H, Christensen J A, Frandsen R, et al. Classification of IRBD and Parkinson’s patients using a general data-driven sleep staging model built on EEG// 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE, 2013: 4275-4278.
17. Ferini-Strambi L, Fasiello E, Sforza M, et al. Neuropsychological, electrophysiological, and neuroimaging biomarkers for REM behavior disorder. Expert Rev Neurother, 2019, 19(11): 1069-1087.
18. Bisgaard S, Duun-Christensen B, Kempfner L, et al. EEG recordings as a source for the detection of IRBD// 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE, 2015: 606-609.
19. Iasemidis L D, Shiau D S, Chaovalitwongse W, et al. Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng, 2003, 50(5): 616-627.
20. Greenspan H, van Ginneken B, Summers R M. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging, 2016, 35(5): 1153-1159.
21. Wei L, Lin Y, Wang J, et al. Time-frequency convolutional neural network for automatic sleep stage classification based on single-channel EEG// 2017 29th International Conference on Tools with Artificial Intelligence (ICTAI). Boston: IEEE, 2017: 88-95.
22. Jadhav P, Rajguru G, Datta D, et al. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybern Biomed Eng, 2020, 40(1): 494-504.
23. Fraiwan L, Lweesy K, Khasawneh N, et al. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Meth Prog Biomed, 2012, 108(1): 10-19.
24. Waibel A, Hanazawa T, Hinton G, et al. Phoneme recognition using time-delay neural networks. IEEE Trans Acoust Speech Signal Process, 1989, 37(3): 393-404.
25. 杨婧, 耿辰, 王海林, 等. 基于 DenseNet 的低分辨 CT 影像肺腺癌组织学亚型分类. 浙江大学学报(工学版), 2019, 53(6): 1164-1170.
26. 杨熠, 钱旭升, 周志勇, 等. 采用影像组学的肾肿瘤组织学亚型分类. 浙江大学学报(工学版), 2019, 53(12): 2381-2388.
27. Truong N D, Nguyen A D, Kuhlmann L, et al. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw, 2018, 105: 104-111.
28. Usman S M, Khalid S, Aslam M H. Epileptic seizures prediction using deep learning techniques. IEEE Access, 2020, 8: 39998-40007.
29. Budak U, Bajaj V, Akbulut Y, et al. An effective hybrid model for EEG-based drowsiness detection. IEEE Sens J, 2019, 19(17): 7624-7631.
30. Yuan L, Cao J. Patients’ EEG data analysis via spectrogram image with a convolution neural network// 2017 International Conference on Intelligent Decision Technologies. Sorrento: Springer, 2017: 13-21.
31. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
32. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proc Int Conf Learn Representat, 2015: 1-14.
33. Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift// Proceedings of ICML 2015. France: IMLS, 2015: 448-456.
34. Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 2014, 15(1): 1929-1958.
35. Krogh A, Hertz J A. A simple weight decay can improve generalization// J. Moody J, Hanson S, Lippmann R P. Advances in Neural Information Processing Systems 4 (NIPS 1991). UK: MIT, 1992: 950-957.
36. Chen T, Guestrin C. XGBoost: A scalable tree boosting system// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2016: 785-794.
37. Ruffini G, Ibaez D, Castellano M, et al. Deep learning with EEG spectrograms in rapid eye movement behavior disorder. Front Neurol, 2019, 10: 806.
38. Wang Y, Wang Z W, Yang Y C, et al. Validation of the rapid eye movement sleep behavior disorder screening questionnaire in China. J Clin Neurosci, 2015, 22(9): 1420-1424.
39. 程晨晨, 尤波, 刘燕, 等. 基于深度神经网络的个性化睡眠癫痫发作预测. 模式识别与人工智能, 2021, 34(4): 333-342.
40. 刘伟楠, 刘燕, 佟宝同, 等. 基于功率谱的睡眠中癫痫发作预测. 生物医学工程学杂志, 2018, 35(3): 329-336.
41. Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Int J Comput Vision, 2020, 128(2): 336-359.
42. Li Y, Yang H, Li J, et al. EEG-based intention recognition with deep recurrent-convolution neural network: performance and channel selection by Grad-CAM. Neurocomputing, 2020, 415: 225-233.
43. Cooray N, Andreotti F, Lo C, et al. Detection of REM sleep behaviour disorder by automated polysomnography analysis. Clin Neurophysiol, 2019, 130(4): 505-514.

Journal of Biomedical Engineering

Intelligence-aided diagnosis of Parkinson’s disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network

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