Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-05741-0.pdf
Reference102 articles.
1. Mirarchi D, Vizza P, Tradigo G et al (2017) Signal analysis for voice evaluation in Parkinson’s disease. In: 2017 IEEE International conference on healthcare informatics (ICHI). IEEE, pp 530–535
2. Vollstedt EJ, Kasten M, Klein C et al (2019) Using global team science to identify genetic Parkinson’s disease worldwide. Ann Neurol 86(2):153
3. Tsanas A, Little MA, Mcsharry PE et al (2012) Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans Biomed Eng 59(5):1264–1271
4. Gümüşçü A, Karadağ K, Tenekecı ME et al (2017) Genetic algorithm based feature selection on diagnosis of Parkinson disease via vocal analysis. In: 2017 25th Signal processing and communications applications conference (SIU). IEEE, pp 1–4
5. Emrani S, McGuirk A, Xiao W (2017) Prognosis and diagnosis of Parkinson's disease using multi-task learning. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1457–1466
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