Parkinson Disease Prediction Using CNN-LSTM Model from Voice Signal
Author:
Funder
NM-ICPS TIH Kolkata
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42979-024-02728-1.pdf
Reference34 articles.
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2. Parra-Gallego LF, Arias-Vergara T, Vasquez-Correa JC, Garcia-Ospina N, Orozco-Arroyave JR, Noth E. Automatic intelligibility assessment of parkinson’s disease with diadochokinetic exercises. Commun Comput Inf Sci. 2018. https://doi.org/10.1007/978-3-030-00353-1_20.
3. N. Hosseini-Kivanani, J.C. Vasquez-Correa, M. Stede, E. Noth (2019) Automated crosslanguage intelligibility analysis of parkinson’s disease patients using speech recognition technologies. Proc 57th Annu Meet AssocComput Linguist Student Res Work. https://doi.org/10.18653/v1/p19-2010.
4. Goyal J, Khandnor P, Aseri TC. A hybrid approach for Parkinson’s disease diagnosis with resonance and time-frequency based features from speech signals. Expert Syst Appl. 2021;182: 115283. https://doi.org/10.1016/j.eswa.2021.115283.
5. Liu Y, Li Y, Tan X, Wang P, Zhang Y. Local discriminant preservation projection embedded ensemble learning based dimensionality reduction of speech data of Parkinson’s disease. Biomed Signal Process Control. 2021;63:102165. https://doi.org/10.1016/j.bspc.2020.102165.
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