Cepstral Coefficient Extraction using the MFCC with the Discrete Wavelet Transform for the Parkinson's Disease Diagnosis

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Belhoussine Drissi Taoufiq, Zayrit Soumaya, Nsiri Benayad, Boualoulou Nouhaila
DOI : 10.14445/22315381/IJETT-V70I7P229

How to Cite?

Belhoussine Drissi Taoufiq, Zayrit Soumaya, Nsiri Benayad, Boualoulou Nouhaila, "Cepstral Coefficient Extraction using the MFCC with the Discrete Wavelet Transform for the Parkinson's Disease Diagnosis" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 283-290, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P229

Abstract
Studies consider Parkinson's disease the second most common neurological syndrome after Alzheimer's. To get an accurate diagnosis, we have recourse to the signal treatment. One of the most used methods in sound recognition and speaker identification is the Mel Frequency Cepstral Coefficients (MFCC). In this technique, the first step is Pre-emphasize the signal, then frame and window it. After that, the Cepstral analysis in which the Fast Fourier Transform (FFT) is applied, followed by the Discrete Cosine Transform (DCT) and the liftering of the Mel Frequency Cepstral Coefficients. This paper proposes to work with the Discrete Wavelet Transform (DWT) instead of the filter bank at the filtering step. Using data containing 17 healthy and 20 patients with Parkinson's, 12 MFCC coefficients will be extracted and classified using the Support Vector Machine (SVM). Thence, a method consisting of inserting DWT into the MFCC bloc will be conducted to compare the result with the previous experience when DWT was outside the MFCC bloc and with the one using only the MFCC without DWT to fetch the accurate process of Parkinson's disease detection.

Keywords
Parkinson's disease, MFCC, Daubechies, Wavelet, Filter bank, SVM.

Reference
[1] James Parkinson, "Anonymous an Essay on the Shaking Palsy," London Medical and Physical Journal, Vol.38, 1817.
[2] H. Karimi-Rouzbahani, M. Daliri, "Diagnosis of Parkinson's Disease in Human Using Voice Signals,” Basic and Clinical Neuroscience, Vol.2, No.3, Pp.12 , 2011.
[3] Y. Campos-Roca, D. Monta, C.J. Perez. "Parkinson's Disease Detection Based on a Heterogeneous Acoustic Database,” Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science (EECSS 2015) Barcelona, Spain, Pp.13-14, 2015.
[4] A. Benba, A. Jilbab, A. Hammouch. "Discriminating Between Patients with Parkinson's and Neurological Diseases Using Cepstral Analysis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol.24, No.10, Pp. 1100–1108, 2016.
[5] E. Benmalek, J. Elmhamdi, A. Jilbab. "Voice Assessments for Detecting Patients with Parkinson's Diseases in Different Stages,” International Journal of Electrical and Computer Engineering , Vol. 8, No. 6, Pp. 4265-4271, 2018. ISSN: 2088-8708. Doi: 10.11591/Ijece.V8i6.Pp4265-4271.
[6] B. Nsiri, S. Zayrit, T. Belhoussine Drissi, A. Ammoumou, "Features Selection By Genetic Algorithm Optimization with K-Nearest Neighbour and Learning Ensemble to Predict Parkinson Disease,” International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 2, Pp. 1982-1989, 2022. DOI: 10.11591/Ijece.V12i2.Pp1982-1989.
[7] S. Savitha, A.N. Upadhya, J.H. Cheeranb. Nirmalc. "Thomson Multitaper MFCC and PLP Voice Features for Early Detection of Parkinson Disease,” Biomedical Signal Processing and Control, Vol.46, Pp.293-30, 2018.
[8] Mrs.Snehal S. Golait, Dr.L.G. Malik, Prof.A.Thomas. "Handwritten Marathi Compound Character Recognition Using Structural and Statistical Features,”Impact Factor Value 3.441 E-ISSN: 2456-3463 International Journal of Innovations in Engineering and Science, Vol. 2, No.12, 2017. Www.Ijies.Net 13.
[9] N. Boualoulou, T. Belhoussine Drissi, B. Nsiri. "an Intelligent Approach Based on the Combination of the Discrete Wavelet Transform, Delta Delta MFCC for Parkinson's Disease Diagnosis,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 4, 2022.
[10] Sonali Lohbare (Pakhmode) and Swati Dixit. "Elimination of Noise From Ambulatory ECG Signal Using DWT,” International Journal of Engineering Trends and Technology, Vol. 70, No. 5, Pp. 266-273, 2022. Crossref, Https://Doi.Org/10.14445/22315381/IJETT-V70I5P229.
[11] Raaed Faleh Hassan Et Al. "ECG Signal De-Noising and Feature Extraction Using Discrete Wavelet Transform,” International Journal of Engineering Trends and Technology (IJETT), Vol.63, No.1, 2018.
[12] B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini-Issac, M. O. Colliot, M. Sarazin, H. Benali. "Support Vector Machine-Based Classification of Alzheimer's Disease From Whole-Brain Anatomical MRI,” Neuroradiology, Vol.51, No.2, Pp .73–83, 2009.
[13] L. Jin, Y. Feng, J. He, S. Zhou, Q. Zeng & Y. Wu. "A Relieff-SVM-Based Method for Marking Dopamine-Based Disease Characteristics: A Study on SWEDD and Parkinson's Disease,” Behavioural Brain Research, Vol.356, Pp.400-407, 2009.
[14] A. Benba, A. Jilbab, A. Hammouch, S. Sandabad. "Voiceprints Analysis Using MFCC and SVM for Detecting Patients with Parkinson's Disease,” 1st International Conference on Electrical and Information Technologies ICEIT'2015, Marrakech, Morocco, Pp. 300-304, 2015.
[15] A. Benba, A. Jilbab, A. Hammouch, "Voiceprint Analysis Using Perceptual Linear Prediction and Support Vector Machines for Detecting Persons with Parkinson's Disease," the 3rd International Conference on Health Science and Biomedical Systems (HSBS '14), Florence, Italy, Pp.22-24, 2014.
[16] T. Belhoussine, S. Zayrit, B. Nsiri, A. Ammoummou. "Diagnosis of Parkinson's Disease Based on Wavelet Transform and Mel Frequency Cepstral Coefficients,” International Journal of Advanced Computer Science and Applications, Vol.10, No.3, 2019.
[17] Y. Toulni, T. Belhoussine Drissi, B. Nsiri. "Electrocardiogram Signals Classification Using Discrete Wavelet Transform and Support Vector Machine Classifier,” IAES International Journal of Artificial Intelligence (IJ-AI) , Vol.10, No.4, Pp.960-970, 2021.DOI: 10.11591/Ijai.V10.I4.
[18] Z. Soumaya, B. D. Taoufiq, B. Nsiri, and A. Abdelkrim, “ Diagnosis of Parkinson Disease Using the Wavelet Transform and MFCC and SVM Classifier,” IEEE 2019 4th World Conference on Complex Systems (WCCS), Ouarzazate, Moroccopp, Pp.1-6, 2019. Doi:10.1109/Icocs.2019.8930802.
[19] S. Zayrit, T. Belhoussine Drissi, A. Ammoumou and B. Nsiri, "Daubechies Wavelet Cepstral Coefficients for Parkinson's Disease Detection," Complex Systems, Vol.29, No.3, Pp.729–739,2020. Https://Doi.Org/10.25088/Complexsystems.29.3.729.
[20] Z. Soumaya, B. Drissi Taoufiq, N. Benayad, K. Yunus, A. Abdelkrim, “the Detection of Parkinson Disease Using the Genetic Algorithm and SVM Classifier,” Applied Acoustics, Vol.171, Pp.107528, 2021. Doi:10.1016/J.Apacoust.2020.107528.
[21] M. Hoq, M.N. Uddin, S.-B. Park. "Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson's Disease Detection,” Diagnostics (Basel), Vol.11, No.6,Pp.1076, 2021. Https://Doi.Org/10.3390/ Diagnostics11061076.
[22] G. Solana-Lavalle, R. Rosas-Romero. "Analysis of Voice As an Assisting Tool for Detection of Parkinson's Disease and Its Subsequent Clinical Interpretation,” Biomedical Signal Processing and Control, Vol.66, Pp.102415. Doi:10.1016/J.Bspc.2021.102415. 2021.
[23] Singh, Sanjana, and Wenyao Xu. "Robust Detection of Parkinson's Disease Using Harvested Smartphone Voice Data: A Telemedicine Approach,” Telemedicine Journal and E-Health: the Official Journal of the American Telemedicine Association, Vol. 26, No.3, Pp.327-33, 2020. Doi:10.1089/Tmj.2018.0271.
[24] S. Aich, M. Joo, H. C. Kim, J. Park. "Improvisation of Classification Performance Based on Feature Optimization for Differentiation of Parkinson's Disease From Other Neurological Diseases Using Gait Characteristics,” International Journal of Electrical and Computer Engineering (IJECE), Vol. 9, No. 6, Pp. 5176-5184, 2019. DOI: 10.11591/Ijece.V9i6.Pp5176-5184
[25] B.E. Sakar, M.E. Isenkul, C.O. Sakar, A. Sertbas, F. Gurgen, S. Delil, … O. Kursun. "Collection and Analysis of A Parkinson Speech Dataset with Multiple Types of Sound Recordings,” IEEE Journal of Biomedical and Health Informatics, Vol.17, No.4, Pp.828–834,2013.
[26] Anett Antony Et Al. "Speaker Identification Based on Combination of MFCC and UMRT Based Features" Procedia Computer Science, Vol.143, Pp.250–257,2018.
[27] Wojcicki, K.: Writehtk. in: Voicebox Toolbox, 2011.Http://Www.Mathworks.Com/Matlabcentral/Fileexchange/32849-Htk-Mfcc-Matlab/Content/Mfcc/Writehtk.M.