Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals

• Autorzy: Khare Smith K. , Bajaj Varun , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2021.04.008

• Języki publikacji: EN

Abstrakty

EN

Deep brain simulations play an important role to study physiological and neuronal behavior during Parkinson’s disease (PD). Electroencephalogram (EEG) signals may faithfully represent the changes that occur during PD in the brain. But manual analysis of EEG signals is tedious, and time consuming as these signals are complex, non-linear, and non-stationary nature. Therefore EEG signals are required to decompose into multiple subbands (SBs) to get detailed and representative information from it. Experimental selection of basis function for the decomposition may cause system degradation due to information loss and an increased number of misclassification. To address this, an automated tunable Q wavelet transform (A-TQWT) is proposed for automatic decomposition. A-TQWT extracts representative SBs for analysis and provides better reconstruction for the synthesis of EEG signals by automatically selecting the tuning parameters. Five features are extracted from the SBs and classified different machine learning techniques. EEG dataset of 16 healthy controls (HC) and 15 PD (ON and OFF medication) subjects obtained from ”openneuro” is used to develop the automated model. We have aimed to develop an automated model that effectively classify HC subjects from PD patients with and without medication. The proposed method yielded an accuracy of 96.13% and 97.65% while the area under the curve of 97% and 98.56% for the classification of HC vs PD OFF medication and HC vs PD ON medication using least square support vector machine, respectively.

Słowa kluczowe

EN

electroencephalography; EEG; Parkinson's disease; automated tunable Q wavelet transform

PL

elektroencefalografia; EEG; choroba Parkinsona; transformata falkowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 679--689
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Khare Smith K.

• Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, India

autor

Bajaj Varun

• Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005, India

autor

Acharya U. Rajendra

• School of Engineering, Division of ECE, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan

Bibliografia

• [1] Parkinson’s foundation, https://www.parkinson.org/ understanding-parkinsons (Accessed: 20 September 2020).
• [2] Bhat S, Acharya UR, Hagiwara Y, Dadmehr N, Adeli H. Parkinson’s disease: Cause factors, measurable indicators, and early diagnosis. Comput Biol Med 2018;102:234–41. https://doi.org/10.1016/j.compbiomed.2018.09.008.
• [3] Bonner N, Bozzi S, Morgan L, Mason B, Peterschmitt M, Fischer TZ, Arbuckle R, Reaney M. Patients’ experiences of parkinson’s disease: a qualitative study in glucocerebrosidase and idiopathic parkinson’s disease. J Patient-Reported Outcomes 2020;4(65):1–14.
• [4] Hamberg K, Hariz G-M. The decision-making process leading to deep brain stimulation in men and women with parkinson’s disease - an interview study. BMC Neurol 2014;14:89. https://doi.org/10.1186/1471-2377- 14-89.
• [5] Spadoto AA, Guido RC, Carnevali FL, Pagnin AF, Falca˜o AX, Papa JP. Improving parkinson’s disease identification through evolutionary-based feature selection. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. p. 7857–60.
• [6] Luukka P. Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst Appl 2011;38(4):4600–7. https://doi.org/10.1016/j.eswa.2010.09.133.
• [7] Stoessl AJ, Neuroimaging in parkinson’s disease: from pathology to diagnosis, Parkinsonism & Related Disorders 18 (2012) S55–S59, proceedings of WFN XIX World Congress on Parkinson’s Disease and Related Disorders. doi: 10.1016/ S1353-8020(11)70019-0.
• [8] George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in parkinson’s disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. NeuroImage: Clinical 2013;3:261–70. https://doi.org/10.1016/j.nicl.2013.07.013.
• [9] Haapaniemi T, Pursiainen V, Korpelainen J, Huikuri H, Sotaniemi K, Myllyl V. Ambulatory ECG and analysis of heart rate variability in parkinson’s disease. J Neurology, Neurosurgery, Psychiatry 2001;70(3):305–10. https://doi.org/ 10.1136/jnnp.70.3.305.
• [10] Flament D, Vaillancourt D, Kempf T, Shannon K, Corcos D. EMG remains fractionated in parkinson’s disease, despite practice-related improvements in performance. Clin Neurophysiol 2003;114(12):2385–96. https://doi.org/10.1016/ S1388-2457(03)00254-2.
• [11] Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. Elevated synchrony in parkinson disease detected with electroencephalography. Ann Neurol 2015;78(5):742–50. https://doi.org/10.1002/ana.24507.
• [12] Taran S, Bajaj V, Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform, Neural Computing and Applications 31 (11 2019). doi:10.1007/s00521- 018-3531-0.
• [13] Al Ghayab HR, Li Y, Siuly S, Abdulla P Wen. Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction. In: Siuly S, Huang Z, Aickelin U, Zhou R, Wang H, Zhang Y, Klimenko S, editors. Health Information Science. Cham: Springer International Publishing; 2017. p. 45–55.
• [14] Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 2016;137:247–59. https://doi.org/10.1016/j.cmpb.2016.09.008. URL: http://www.sciencedirect.com/science/article/pii/ S0169260716304370.
• [15] Hassan AR, Bhuiyan MIH. A decision support system for automatic sleep staging from EEG signals using tunable Qfactor wavelet transform and spectral features. J Neurosci Methods 2016;271:107–18. https://doi.org/10.1016/j. jneumeth.2016.07.012. URL: http:// www.sciencedirect.com/science/article/pii/ S0165027016301650.
• [16] Al Ghayab HR, Li Y, Siuly S, Abdulla S. A feature extraction technique based on tunable q-factor wavelet transform for brain signal classification. J Neurosci Methods 2019;312:43–52. https://doi.org/10.1016/j. jneumeth.2018.11.014. http:// www.sciencedirect.com/science/article/pii/ S0165027018303820.
• [17] Bhattacharyya A, Pachori R, Upadhyay A, Acharya U. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl Sci 2017;7(4):385. https://doi.org/10.3390/app7040385.
• [18] Pezard L, Jech R, Rka E. Investigation of non-linear properties of multichannel EEG in the early stages of parkinson’s disease. Clin Neurophysiol 2001;112(1):38–45. https://doi.org/ 10.1016/S1388-2457(00)00512-5.
• [19] Betrouni N, Delval A, Chaton L, Defebvre L, Duits A, Moonen A, Leentjens AF, Dujardin K. Electroencephalography-based machine learning for cognitive profiling in parkinson’s disease: Preliminary results. Mov Disord 2019;34(2):210–7. https://doi.org/10.1002/mds.27528.
• [20] Jeong D-H, Kim Y-D, Song I-U, Chung, Y-A, Jeong J, Wavelet energy and wavelet coherence as EEG biomarkers for the diagnosis of parkinson’s disease-related dementia and alzheimer’s disease, Entropy 18 (1) (2016). doi:10.3390/ e18010008.
• [21] Liu G, Zhang Y, Hu Z, Du X, Wu W, Xu C, Wang X, Li S, Complexity analysis of electroencephalogram dynamics in patients with parkinson’s disease, Parkinson’s Disease 2017 (03 2017). doi:10.1155/2017/8701061.
• [22] Rizvi SQA, Wang XG. Xing, Early detection of parkinson disease using wavelet transform along with fourier transform. In: Wang G, El Saddik A, Lai X, Martinez Perez G, Choo K-KR, editors. Smart City and Informatization. Springer Singapore, Singapore; 2019. p. 323–33.
• [23] Soumaya Z, Taoufiq BD, Nsiri B, Abdelkrim A, Diagnosis of parkinson disease using the wavelet transform and mfcc and svm classifier, in: 2019 4th World Conference on Complex Systems (WCCS), 2019, pp. 1–6. doi:10.1109/ IcoCS.2019.8930802.
• [24] Drissi TB, Zayrit S, Nsiri B, Ammoummou A. Diagnosis of parkinson’s disease based on wavelet transform and mel frequency cepstral coefficients. Int J Adv Computer Sci Appl 2019;10(3). https://doi.org/10.14569/IJACSA.2019.0100315.
• [25] Oh SL, Hagiwara Y, Rajamanickam RU, Arunkumar Y, Acharya UR. A deep learning approach for parkinson’s disease diagnosis from EEG signals. Neural Comput Appl 2020;32:10927–33. https://doi.org/10.1007/s00521-018-3689-5.
• [26] Xu S, Wang Z, Sun J, Zhang Z, Wu Z, Yang T, Xue G, Cheng C. Using a deep recurrent neural network with eeg signal to detect parkinson’s disease. Ann Transl Med 2020;8(14):1–9.
• [27] Anjum MF, Dasgupta S, Mudumbai R, Singh A, Cavanagh JF, Narayanan NS. Linear predictive coding distinguishes spectral EEG features of parkinson’s disease. Parkinsonism Related Disorders 2020;79:79–85. https://doi.org/10.1016/ j.parkreldis.2020.08.001.
• [28] Murugappan M, Alshuaib W, Bourisly AK, Khare SK, Sruthi S, Bajaj V. Tunable Q wavelet transform based emotion classification in parkinson’s disease using electroencephalography. PLOS ONE 2020;15(11):1–17. https:// doi.org/10.1371/journal.pone.0242014.
• [29] Tuncer T, Dogan S, Acharya UR. Automated detection of parkinson’s disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybernetics Biomed Eng 2020;40(1):211–20. https://doi.org/ 10.1016/j.bbe.2019.05.006. URL: http:// www.sciencedirect.com/science/article/pii/ S0208521619300853.
• [30] Qi Wei O, Basah HMS, Lee H, Vijean V, Empirical wavelet transform based features for classification of parkinson’s disease severity, Journal of Medical Systems 42 (12 2017). doi:10.1007/s10916-017-0877-2.
• [31] Vanneste S, Song J-J, Ridder D, Thalamocortical dysrhythmia detected by machine learning, Nature Communications 9 (03 2018). doi:10.1038/s41467-018-02820-0.
• [32] Chaturvedi M, Hatz F, Gschwandtner U, Bogaarts JG, Meyer A, Fuhr P, Roth V. Quantitative EEG (QEEG) measures differentiate parkinson’s disease (PD) patients from healthy controls (HC). Front Aging Neurosci 2017;9:3. https://doi.org/ 10.3389/fnagi.2017.00003. URL: https://www.frontiersin.org/ article/10.3389/fnagi.2017.00003.
• [33] Ruffini G, Iban˜ ez D, Castellano M, Dubreuil-Vall L, SoriaFrisch A, Postuma R, Gagnon J-F, Montplaisir J. Deep learning with EEG spectrograms in rapid eye movement behavior disorder. Front Neurol 2019;10:806. https://doi.org/10.3389/ fneur.2019.00806. URL: https://www.frontiersin.org/article/ 10.3389/fneur.2019.00806.
• [34] Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E. Brain functional connectivity patterns for emotional state classification in parkinson’s disease patients without dementia. Behav Brain Res 2016;298:248–60. https:// doi.org/10.1016/j.bbr.2015.10.036. URL: https:// www.sciencedirect.com/science/article/pii/ S0166432815302503.
• [35] Khare SK, Bajaj V. Constrained based tunable Q wavelet transform for efficient decomposition of EEG signals. Appl Acoust 2020;163 . https://doi.org/10.1016/j. Apacoust.2020.107234 107234.
• [36] Jackson N, Cole SR, Voytek B, Swann NC, Characteristics of waveform shape in parkinson’s disease detected with scalp electroencephalography, eNeuro 6 (3) (2019). doi:10.1523/ ENEURO.0151-19.2019.
• [37] Hoehn M, Yahr M. Parkinsonism: Onset, progression, and mortality. Neurology 2011;77(9). https://doi.org/10.1212/01. Wnl.0000405146.06300.91. 874–874.
• [38] Selesnick IW. Wavelet transform with tunable Q-factor. IEEE Trans Signal Processing 2011;59(8):3560–75.
• [39] Venkata Rao R. Jaya A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 2016;7:19–34. https://doi.org/10.5267/j.ijiec.2015.8.004.
• [40] la Torre FC-D, González-Trejo JI, Real-Ramı´rez CA, HoyosReyes LF. Fractal dimension algorithms and their application to time series associated with natural phenomena. J Phys: Conf Ser 2013;475 . https://doi.org/10.1088/1742-6596/475/1/ 012002 012002.
• [41] Binkhonain M, Zhao L. A review of machine learning algorithms for identification and classification of nonfunctional requirements. Expert Systems Appl: X 2019;1 . https://doi.org/10.1016/j.eswax.2019.100001. URL: https:// www.sciencedirect.com/science/article/pii/ S2590188519300010 100001.
• [42] Suykens J, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett 1999;9(3):293–300. https://doi.org/10.1023/A:1018628609742.
• [43] Li Z, Li C, Selection of kernel function for least squares support vector machines in downburst wind speed forecasting. In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Vol. 02, 2018, pp. 337–341. doi:10.1109/ISCID.2018.10178.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).