Voice-Based Detection of Parkinson’s Disease Using Empirical Mode Decomposition, IMFCC, MFCC, and Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48573-2_21
Reference12 articles.
1. Karan, B., Sekhar Sahu, S. : An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal. Biocybern. Biomed. Eng. 41(2), 717–732 (2021). https://doi.org/10.1016/j.bbe.2021.04.014
2. Orozco-Arroyave, J.R., Hönig, F., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Nöth, E.: Spectral and cepstral analyses for Parkinson’s disease detection in Spanish vowels and words. Expert. Syst. 32(6), 688–697 (2015). https://doi.org/10.1111/exsy.12106
3. Khan, T., Westin, J., Dougherty, M.: Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson’s disease. Biocybern. Biomed. Eng. 34(1), 25–34 (2014). https://doi.org/10.1016/j.bbe.2013.06.001
4. Er, M.B., Isik, E., Isik, I.: Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with variational mode decomposition. Biomed. Signal Process. Control 70, 103006 (2021). https://doi.org/10.1016/j.bspc.2021.103006
5. Sakar, C.O., et al.: A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput. 74, 255–263 (2019). https://doi.org/10.1016/j.asoc.2018.10.022
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