Parkinson disease prediction using intrinsic mode function based features from speech signal

• Autorzy: Karan Biswajit , Sahu Sitanshu Sekhar , Mahto Kartik

Identyfikatory

DOI

10.1016/j.bbe.2019.05.005

• Języki publikacji: EN

Abstrakty

EN

Parkinson's disease (PD) is a progressive neurological disorder prevalent in old age. Past studies have shown that speech can be used as an early marker for identification of PD. It affects a number of speech components such as phonation, speech intensity, articulation, and respiration, which alters the speech intelligibility. Speech feature extraction and classification always have been challenging tasks due to the existence of non-stationary and discontinuity in the speech signal. In this study, empirical mode decomposition (EMD) based features are demonstrated to capture the speech characteristics. A new feature, intrinsic mode function cepstral coefficient (IMFCC) is proposed to efficiently represent the characteristics of Parkinson speech. The performances of proposed features are assessed with two different datasets: dataset-1 and dataset-2 each having 20 normal and 25 Parkinson affected peoples. From the results, it is demonstrated that the proposed intrinsic mode function cepstral coefficient feature provides superior classification accuracy in both data-sets. There is a significant increase of 10–20% in accuracy compared to the standard acoustic and Mel-frequency cepstral coefficient (MFCC) features.

Słowa kluczowe

EN

Parkinson disease; empirical mode decomposition; intrinsic mode function; IMFCC; MFCC; support vector machine

PL

choroba Parkinsona; empiryczna dekompozycja sygnału; funkcja trybu wewnętrznego; IMFCC; MFCC; maszyna wektorów wsparcia

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 249--264
• Opis fizyczny: Bibliogr. 49 poz., rys., tab., wykr.

Twórcy

autor

Karan Biswajit

• Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India

autor

Sahu Sitanshu Sekhar

• Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India

autor

Mahto Kartik

• Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Bibliografia

• [1] Dashtipour K, Tafreshi A, Lee J, Crawley B. Speech disorders in Parkinson's disease: pathophysiology, medical management and surgical approaches. Neurodegener Dis Manag 2018;337–48. http://dx.doi.org/10.2217/nmt-2018-0021.
• [2] Roberts A, Post D. Information content and efficiency in the spoken discourse of individuals with Parkinson's disease. J Speech Lang Hear Res 2018;61(9 (Sep 19)):2259–60. http://dx.doi.org/10.1044/2018_JSLHR-L-17-0338.
• [3] Mühlhaus J, Frieg H, Bilda K, Ritterfeld U. Game-based speech rehabilitation for people with Parkinson's disease. International Conference on Universal Access in Human– Computer Interaction. Cham: Springer; 2017 Jul 9. p. 76–85.
• [4] Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans Biomed Eng 2009;56 (4):015–1022. http://dx.doi.org/10.1109/TBME.2008.2005954.
• [5] Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO. Novel speech signal processing algorithms for high accuracy classification of Parkinsons disease. IEEE Trans Biomed Eng 2012;59:1264–70. http://dx.doi.org/10.1109/TBME.2012.2183367.
• [6] Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Heal Inform 2013;17:828–34. http://dx.doi.org/10.1109/JBHI.2013.2245674.
• [7] Pérez CJ, Campos-Roca Y, Naranjo L, Martín J. Diagnosis and tracking of Parkinson's disease by using automatically extracted acoustic features. J Alzheimers Dis Parkinsonism 2016;6:260. http://dx.doi.org/10.4172/2161-0460.1000260.
• [8] Shahbakhi M, Taheri Far D, Tahami E. Speech analysis for diagnosis of Parkinson's disease using genetic algorithm and support vector machine. J Biomed Sci Eng 2014;147–56. http://dx.doi.org/10.4236/jbise.2014.7401.
• [9] Gupta D, Sundaram S, Khanna A, Hassanien AE, De Albuquerque VHC. Improved diagnosis of Parkinson's disease using optimized crow search algorithm. Comput Electr Eng 2018. http://dx.doi.org/10.1016/j.compeleceng.2018.04.014.
• [10] Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, Arunkumar N, et al. Optimized cuttlefish algorithm for diagnosis of Parkinson's disease. Cogn Syst Res 2018. http://dx.doi.org/10.1016/j.cogsys.2018.06.006.
• [11] Belalcazar-Bolaños EA, Orozco-Arroyave JR, Arias-Londono JD, Vargas-Bonilla JF, Noth E. Automatic detection of Parkinson's disease using noise measures of speech. Symp Signals Images Artif Vis – 2013 STSIVA 2013. http://dx.doi.org/10.1109/STSIVA.2013.6644928.
• [12] Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M. A hybrid intelligent system for the prediction of Parkinson's disease progression using machine learning techniques. Biocybern Biomed Eng 2018;38:1–15. http://dx.doi.org/10.1016/j.bbe.2017.09.002.
• [13] Arias-Londoño JD, Godino-Llorente JI, Sáenz-Lechón N, Osma-Ruiz V, Castellanos-Domínguez G. Automatic detection of pathological voices using complexity measures, noise parameters, and mel-cepstral coefficients. IEEE Trans Biomed Eng 2011;58(2):370–9. http://dx.doi.org/10.1109/TBME.2010.2089052.
• [14] Vaiciukynas E, Verikas A, Gelzinis A, Bacauskiene M. Detecting Parkinson's disease from sustained phonation and speech signals. PloS One 2017. http://dx.doi.org/10.1371/journal.pone.0185613.
• [15] Rusz J, Cmejla R, Ruzickova H, Ruzicka E. Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson's 2011. http://dx.doi.org/10.1121/1.3514381.
• [16] Bocklet T, Nöth E, Stemmer G, Ruzickova H, Rusz J. Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis. 2011 IEEE Workshop on Automatic Speech Recognition & Understanding. IEEE; 2011. p. 478–83. doi:0.1109/ASRU.2011.6163978.
• [17] Novotný M, Rusz J, C?mejla R, Růžicka E. Automatic evaluation of articulatory disorders in Parkinson's disease. IEEE/ACM Trans Audio Speech Lang Process 2014. http://dx.doi.org/10.1109/TASLP.2014.2329734.
• [18] Benba A, Jilbab A, Hammouch A. Voice analysis for detecting patients with Parkinson's disease using the hybridization of the best acoustic features. Int J Electr Eng Inform 2016. http://dx.doi.org/10.15676/ijeei.8.1.20168.
• [19] Hlavnicka J, C?mejla R, Tykalová T, Šonka K, Růžicka E, Rusz J. Automated analysis of connected speech reveals early biomarkers of Parkinson's disease in patients with rapid eye movement sleep behaviour disorder. Sci Rep 2017;7:12. http://dx.doi.org/10.1038/s41598-2004017-00047. Neurology.
• [20] Brückl M, Ghio A, Viallet F. Measurement of tremor in the voices of speakers with Parkinson's disease. Procedia Comp Sci 2018;128:47–54. http://dx.doi.org/10.1016/j.procs.2018.03.007.
• [21] Orozco-Arroyave JR, Hönig F, Arias-Londoño JD, Vargas- Bonilla JF, Nöth E. Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words. Expert Syst 2015;32:688–97. http://dx.doi.org/10.1111/exsy.12106.
• [22] Orozco-Arroyave JR, Hönig F, Arias-Londoño JD, Vargas- Bonilla JF, Daqrouq K, Skodda S, et al. Automatic detection of Parkinson's disease in running speech spoken in three different languages. J Acoust Soc Am 2016 Jan;139(1):481– 500. http://dx.doi.org/10.1121/1.4939739.
• [23] Khan T, Westin J, Dougherty M. Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson's disease. Biocybern Biomed Eng 2014;34:25–34. http://dx.doi.org/10.1016/j.bbe.2013.06.001.
• [24] Afonso LC, Rosa GH, Pereira CR, Weber SA, Hook C, Albuquerque VH, et al. A recurrence plot-based approach for Parkinson's disease identification. Future Gener Comput Syst 2019;282–92. http://dx.doi.org/10.1016/j.future.2018.11.054.
• [25] Gómez-Vilda P, Mekyska J, Ferrández JM, Palacios-Alonso D, Gómez-Rodellar A, Rodellar-Biarge V, et al. Parkinson disease detection from speech articulation neuromechanics. Front Neuroinform 2017 Aug 25;11:56. http://dx.doi.org/10.3389/fninf.2017.00056.
• [26] Karan B, Mahto K, Sahu SS. Detection of Parkinson disease using variational mode decomposition of speech signal. 2018 International Conference on Communication and Signal Processing (ICCSP). IEEE; 2018. http://dx.doi.org/10.1109/ICCSP.2018.8524445.
• [27] Arias-Vergara T, Vásquez-Correa JC, Orozco-Arroyave JR, Nöth E. Speaker models for monitoring Parkinson's disease progression considering different communication channels and acoustic conditions. Speech Commun 2018 Jul 1;101:11–25. http://dx.doi.org/10.1016/j.specom.2018.05.007.
• [28] Moro-Velázquez L, Gómez-García JA, Godino-Llorente JI, Villalba J, Orozco-Arroyave JR, Dehak N. Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease. Appl Soft Comput 2018 Jan 1;62:649–66. doi:16/j.asoc.2017.11.001.
• [29] Torres ME, Schlotthauer G, Rufiner HL, Jackson-Menaldi MC. Empirical mode decomposition. Spectral properties in normal and pathological voices. 4th European Conference of the International Federation for Medical and Biological Engineering. 2009. pp. 252–5.
• [30] Pereira CR, Pereira DR, Rosa GH, Albuquerque VH, Weber SA, Hook C, et al. Handwritten dynamics assessment through convolutional neural networks: an application to Parkinson's disease identification. Artif Intell Med 2018 May 1;87:67–77. http://dx.doi.org/10.1016/j.artmed.2018.04.001.
• [31] Pereira CR, Pereira DR, Weber SA, Hook C, de Albuquerque VH, Papa JP. A survey on computer-assisted Parkinson's disease diagnosis. Artif Intell Med 2018 Sep 7. http://dx.doi.org/10.1016/j.artmed.2018.08.007.
• [32] Orozco-Arroyave JR, Arias-Londoño JD, Vargas-Bonilla JF, González-Rátiva MC, Nöth E. New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease. Lr 2014. Proc Ninth Int Conf Lang Resour Eval 2014;342–7.
• [33] Lartillot O, Toiviainen P. A Matlab toolbox for musical feature extraction from audio. International conference on digital audio effects; 2007.
• [34] Murty KS, Yegnanarayana B. Combining evidence from residual phase and MFCC features for speaker recognition. IEEE Signal Process Lett 2006;13(1):52–5. http://dx.doi.org/10.1109/LSP.2005.860538.
• [35] Dhanalakshmi P, Palanivel S, Ramalingam V. Classification of audio signals using AANN and GMM. Appl Soft Comput J 2011;11:716–23. http://dx.doi.org/10.1016/j.asoc.2009.12.033.
• [36] Cosi P. Evidence against frame-based analysis techniques. 1998 Proceedings of NATO Advance Institute on Computational Hearing; 2009. p. 163–8. doi:10.1.1.39.4812.
• [37] Jung C, Han KJ, Seo H, Narayanan SS, Kang H. A variable frame length and rate algorithm based on the spectral kurtosis measure for speaker verification. Perform Eval 2010;2754–7.
• [38] Deshpande MS, Holambe RS. Speaker identification based on robust AM-FM features. 2009 2nd Int Conf Emerg Trends Eng Technol ICETET. 2009. pp. 880–4. http://dx.doi.org/10.1109/ICETET.2009.209.
• [39] Huang NE, Shen Z, Long SR, Wu M-LC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A 1998;454:903–95.
• [40] Sharma R, Prasanna SRM, Bhukya RK, Kumar Das R. Analysis of the intrinsic mode functions for speaker information. Speech Commun 2017;91:1–16. http://dx.doi.org/10.1016/j.specom.2017.04.006.
• [41] Sharma R, Prasanna SRM, Rufiner HL, Schlotthauer G. Detection of the glottal closure instants using empirical mode decomposition. Circuits Syst Signal Process 2018;37:3412–20. http://dx.doi.org/10.1007/s00034-017-0713-4.
• [42] Flandrin P, Rilling G, Goncalves P. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 2004;11(2):112–4. http://dx.doi.org/10.1109/LSP.2003.628216.
• [43] Schlotthauer G, Torres ME, Rufiner HL. Voice fundamental frequency extraction algorithm based on ensemble empirical mode decomposition and entropies. World Congress on Medical Physics and Biomedical Engineering, September 7–12, 2009, Munich, Germany; 2009. pp. 984–7. http://dx.doi.org/10.1007/978-3-642-2-2_2620388.
• [44] Giannakopoulos T, Pikrakis A. Introduction to audio analysis: a MATLAB approach; 2014. http://dx.doi.org/10.1016/C2012-0-4-70352.
• [45] Bayestehtashk A, Asgari M, Shafran I, McNames J. Fully automated assessment of the severity of Parkinson's disease from speech. Comput Speech Lang 2015;29:172–85. http://dx.doi.org/10.1080/01924036.1990.9289688.
• [46] Parveen P, Thuraisingham B. Face recognition using multiple classifiers. Proc – Int Conf Tools with Artif Intell ICTAI 2006;179–86. http://dx.doi.org/10.1109/ICTAI.200659.
• [47] Cortes C, Vapnik V. Cortes_Vapnik95Pdf, vol. 297. 1995;p. 273–97. http://dx.doi.org/10.1023/A:1102262741141.
• [48] Ai OC, Hariharan M, Yaacob S, Chee LS. Classification of speech dysfluencies with MFCC and LPCC features. Expert Syst Appl 2012 Feb 1;39(2):2157–60. http://dx.doi.org/10.1016/j.eswa.2011.07.065.
• [49] Rusz J, Novotný M, Hlavnicka J, Tykalová T, Růžicka E. High-accuracy voice-based classification between patients with Parkinson's disease and other neurological diseases may be an easy task with inappropriate experimental design. IEEE Trans Neural Syst Rehabil Eng 2017 Aug;25(8):1319–20. http://dx.doi.org/10.1109/TNSRE.2016.8852621.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).