Parkinson’s Disease Identification from Speech Signals Using Machine Learning Models
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8479-4_15
Reference17 articles.
1. Tsanas A, Little MA, McSharry PE, Ramig LO (2010) Accurate telemonitoring of parkinsons disease progression by noninvasive speech tests. IEEE Trans Biomed Eng 57(4):884–893. https://doi.org/10.1109/TBME.2009.2036000
2. Quan C, Ren K, Luo Z, Chen Z, Ling Y (2022) End-to-end deep learning approach for Parkinson’s disease detection from speech signals. Biocybern Biomed Eng 42(2):556–574. https://doi.org/10.1016/j.bbe.2022.04.002
3. Jyotiyana M, Kesswani N, Kumar M (2022) A deep learning approach for classification and diagnosis of Parkinson’s disease. Soft Comput 26(18):9155–9165. https://doi.org/10.1007/s00500-022-07275-6
4. Roobini MS, Reddy YRK, Royal USG, Singh AK, Babu K (2022) Parkinson’s disease detection using machine learning. In: 2022 International conference on communication, computing and internet of things, IC3IoT 2022—Proceedings. IEEE. https://doi.org/10.1109/IC3IOT53935.2022.9768002
5. Grover S, Bhartia S, Akshama, Yadav A, Seeja KR (2018) Predicting severity of Parkinson’s disease using deep learning. Procedia Comput Sci 1788–1794. https://doi.org/10.1016/j.procs.2018.05.154
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