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.	扈楊, 左麗君, 余舒揚, 等. 帕金森病患者伴很可能的快速眼動睡眠行為障礙和相關因素的研究. 中華臨床醫師雜志, 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.	楊改清, 徐志強, 胥麗霞, 等. 帕金森病精神病性障礙患者快速眼動睡眠行為障礙研究. 中華行為醫學與腦科學雜志, 2019, 28(1): 59-63.
4.	郁婷婷, 丁勇民, 黃衛. 快速眼動睡眠期行為障礙與帕金森病相關性的研究進展. 中華老年醫學雜志, 2015, 34(7): 816-818.
5.	胡艷, 王萍. 伴快速眼動睡眠期行為障礙的帕金森病臨床研究進展. 中華神經醫學雜志, 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, 陳彥, 等. 頭皮腦電圖在迷走神經電刺激治療難治性癲癇研究中的應用. 生物醫學工程學雜志, 2020, 37(4): 699-707.
13.	李昕, 安占周, 李秋月, 等. 加權多重多尺度熵及其在孤獨癥兒童腦電信號分析中的應用. 生物醫學工程學雜志, 2019, 36(1): 33-39,49.
14.	付榮榮, 田永勝, 鮑甜恬. 基于稀疏共空間模式和Fisher判別的單次運動想象腦電信號識別方法. 生物醫學工程學雜志, 2019, 36(6): 911-915,923.
15.	胡盼, 張磊, 周蚌艷, 等. 基于獨立分量分析的在線腦-機接口系統. 生物醫學工程學雜志, 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.	楊婧, 耿辰, 王海林, 等. 基于 DenseNet 的低分辨 CT 影像肺腺癌組織學亞型分類. 浙江大學學報(工學版), 2019, 53(6): 1164-1170.
26.	楊熠, 錢旭升, 周志勇, 等. 采用影像組學的腎腫瘤組織學亞型分類. 浙江大學學報(工學版), 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, 劉燕, 等. 基于深度神經網絡的個性化睡眠癲癇發作預測. 模式識別與人工智能, 2021, 34(4): 333-342.
40.	劉偉楠, 劉燕, 佟寶同, 等. 基于功率譜的睡眠中癲癇發作預測. 生物醫學工程學雜志, 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. 扈楊, 左麗君, 余舒揚, 等. 帕金森病患者伴很可能的快速眼動睡眠行為障礙和相關因素的研究. 中華臨床醫師雜志, 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. 楊改清, 徐志強, 胥麗霞, 等. 帕金森病精神病性障礙患者快速眼動睡眠行為障礙研究. 中華行為醫學與腦科學雜志, 2019, 28(1): 59-63.
4. 郁婷婷, 丁勇民, 黃衛. 快速眼動睡眠期行為障礙與帕金森病相關性的研究進展. 中華老年醫學雜志, 2015, 34(7): 816-818.
5. 胡艷, 王萍. 伴快速眼動睡眠期行為障礙的帕金森病臨床研究進展. 中華神經醫學雜志, 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, 陳彥, 等. 頭皮腦電圖在迷走神經電刺激治療難治性癲癇研究中的應用. 生物醫學工程學雜志, 2020, 37(4): 699-707.
13. 李昕, 安占周, 李秋月, 等. 加權多重多尺度熵及其在孤獨癥兒童腦電信號分析中的應用. 生物醫學工程學雜志, 2019, 36(1): 33-39,49.
14. 付榮榮, 田永勝, 鮑甜恬. 基于稀疏共空間模式和Fisher判別的單次運動想象腦電信號識別方法. 生物醫學工程學雜志, 2019, 36(6): 911-915,923.
15. 胡盼, 張磊, 周蚌艷, 等. 基于獨立分量分析的在線腦-機接口系統. 生物醫學工程學雜志, 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. 楊婧, 耿辰, 王海林, 等. 基于 DenseNet 的低分辨 CT 影像肺腺癌組織學亞型分類. 浙江大學學報(工學版), 2019, 53(6): 1164-1170.
26. 楊熠, 錢旭升, 周志勇, 等. 采用影像組學的腎腫瘤組織學亞型分類. 浙江大學學報(工學版), 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, 劉燕, 等. 基于深度神經網絡的個性化睡眠癲癇發作預測. 模式識別與人工智能, 2021, 34(4): 333-342.
40. 劉偉楠, 劉燕, 佟寶同, 等. 基于功率譜的睡眠中癲癇發作預測. 生物醫學工程學雜志, 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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