A novel Parkinson's disease detection algorithm combined EMD, BFCC, and SVM classifier
1
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco.
2
Computer Science and Systems Laboratory (LIS), Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco
3
Research Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM). Mohammed V University in Rabat, Morocco
Submission date: 2023-06-02
Final revision date: 2023-07-27
Acceptance date: 2023-08-31
Online publication date: 2023-10-09
Publication date: 2023-10-09
Corresponding author
Nouhaila Boualoulou
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco.
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco.
Diagnostyka 2023;24(4):2023403
KEYWORDS
TOPICS
ABSTRACT
Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease.
REFERENCES (24)
1.
de Lau LM, Breteler MM. Epidemiology of Parkinson’s disease. Lancet Neurology 2006; 5(6): 525–35. https://doi.org/10.1016/S1474-....
2.
Drissi TB, Zayrit S, Nsiri B, Ammoummou A. Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients. International Journal of Advanced Computer Science and Applications 2019;10(3). https://dx.doi.org/10.14569/IJ....
3.
Nouhaila B, Taoufiq BD, Benayad N. An intelligent approach based on the combination of the discrete wavelet transform, delta delta MFCC for Parkinson’s Disease diagnosis. International Journal of Advanced Computer Science and Applications 2022;13(4). http://dx.doi.org/10.14569/IJA....
4.
Karan B, Sahu SS, Mahto K. Parkinson disease prediction using intrinsic mode function-based features from speech signal. Biocybernetics and Biomedical Engineering 2020; 40(1): 249–64. https://doi.org/10.1016/j.bbe.....
5.
Karan B, Sahu SS, Orozco-Arroyave JR, Mahto K. Hilbert spectrum analysis for automatic detection and evaluation of Parkinson’s speech. Biomedical Signal Processing and Control 2020; 61: 102050. https://doi.org/10.1016/j.bspc....
6.
Karan B, Sahu SS, Orozco-Arroyave JR, Mahto K. Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson’s disease prediction. Computer Speech & Language 2021;69:101216. https://doi.org/10.1016/j.csl.....
7.
Zhang T, Zhang Y, Sun H, Shan H. Parkinson disease detection using energy direction features based on EMD from voice signal. Biocybernetics and Biomedical Engineering 2021; 41(1): 127–41. https://doi.org/10.1016/j.bbe.....
8.
Zhang T, Lin L, Zhang Y, Niu X. Statistical analysis of local gradient in Mel transform domain for Parkinson’s dysphonia. Journal of Frontiers of Computer Science and Technology. 2022; 16(10): 2345-2356. https://doi.org/10.3778/j.issn....
9.
Soumaya Z, Drissi Taoufiq B, Benayad N, Yunus K, Abdelkrim A. The detection of Parkinson disease using the genetic algorithm and SVM classifier. Applied Acoustics 2021; 171:107528. https://doi.org/10.1016/j.apac....
10.
Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics 2013; 17(4): 828–834. https://doi.org/10.1109/JBHI.2....
11.
Davis SB, Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. in: readings in speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 1990; 28(4): 65–74. https://doi.org/10.1109/TASSP.....
12.
Shrivastava Y, Singh B. A comparative study of EMD and EEMD approaches for identifying chatter frequency in CNC turning. European Journal of Mechanics - A/Solids 2019; 73: 381–393. https://doi.org/10.1016/j.euro....
13.
Mondal A, Banerjee P, Tang H. A novel feature extraction technique for pulmonary sound analysis based on EMD. Computer Methods and Programs in Biomedicine 2018 :159:199–209. https://doi.org/10.1016/j.cmpb....
14.
Mansour RF, Amraoui A El, Nouaouri I, Diaz VG, Gupta D, Kumar S. Artificial intelligence and Internet of Things enabled disease diagnosis model for smart healthcare systems. IEEE Access 2021; 9: 45137–45146. https://doi.org/10.1109/ACCESS....
15.
Soumaya Z, Taoufiq BD, Nsiri B, Abdelkrim A. Diagnosis of Parkinson disease using the wavelet transform and MFCC and SVM classifier. 2019 4th World Conference on Complex Systems (WCCS) 2019; 1–6. https://doi.org/10.1109/ICoCS.....
16.
Benba A, Jilbab A, Hammouch A, Sandabad S. Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson’s disease. 2015 International Conference on Electrical and Information Technologies (ICEIT) 2015; 300–304. https://doi.org/10.1109/EITech....
17.
Fang SH, Tsao Y, Hsiao MJ, Chen JY, Lai YH, Lin FC. Detection of pathological voice using cepstrum vectors: a deep learning approach. Journal of Voice 2019; 33(5): 634–641. https://doi.org/10.1016/j.jvoi....
18.
Solana-Lavalle G, Galán-Hernández JC, Rosas-Romero R. Automatic Parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features. Biocybernetics and Biomedical Engineering 2020; 40(1): 505–516. https://doi.org/10.1016/j.bbe.....
19.
Sakar CO, Serbes G, Gunduz A, Tunc HC, Nizam H, Sakar BE. A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing 2019; 74: 255–263. https://doi.org/10.1016/j.asoc....
20.
Skodda S, Grönheit W, Mancinelli N, Schlegel U. Progression of voice and speech impairment in the course of Parkinson’s Disease: a longitudinal study. Parkinsons Dis. 2013; 2013: 1–8. https://doi.org/10.1155/2013/3....
21.
Roberts A, Post D. Information content and efficiency in the spoken discourse of individuals with Parkinson’s disease. Journal of Speech, Language, and Hearing Research 2018; 61(6): 2259–74. http://dx.doi.org/10.1044/2018....
22.
Mühlhaus J, Frieg H, Bilda K, Ritterfeld U. Game-based speech rehabilitation for people with Parkinson’s Disease. Universal Access in Human-Computer Interaction. Human and Technological Environments 2017 :76–85. https://doi.org/10.1007/978-3-....
23.
Sharma R, Mahadeva Prasanna SR. A better decomposition of speech obtained using modified Empirical Mode Decomposition. Digital Signal Processing 2016;58:26–39. https://doi.org/10.1016/j.dsp.....
24.
Hayakawa S, Itakura F. Text-dependent speaker recognition using the information in the higher frequency band. Proceedings of ICASSP ’94 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE 1994; p. I/137-I/140. https://doi.org/10.1109/ICASSP....
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