A hybrid approach to detecting Parkinson's disease using spectrogram and deep learning CNN-LSTM network
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10772-024-10128-2.pdf
Reference32 articles.
1. Almeida, J. S., Rebouças Filho, P. P., Carneiro, T., Wei, W., Damaševičius, R., Maskeliūnas, R., & de Albuquerque, V. H. C. (2019). Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognition Letters, 125, 55–62. https://doi.org/10.1016/j.patrec.2019.04.005
2. Amato, F., Borzì, L., & Olmo, G. (2021). An algorithm for Parkinson’s disease speech classification based on isolated words analysis. Health Information Science and Systems, 9, 32. https://doi.org/10.1007/s13755-021-00162-8
3. Ayaz, Z., Naz, S., Khan, N. H., Razzak, I., & Imran, M. (2023). Automated methods for diagnosis of Parkinson’s disease and predicting severity level. Neural Computing and Applications, 35, 14499–14534. https://doi.org/10.1007/s00521-021-06626-y
4. Canturk, İ, & Günay, O. (2024). Investigation of scalograms with a deep feature fusion approach for detection of Parkinson’s disease. Cognitive Computation. https://doi.org/10.1007/s12559-024-10254-8
5. Celik, G., & Başaran, E. (2023). Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson’s disease from speech signals. Applied Acoustics, 211, 109476. https://doi.org/10.1016/j.apacoust.2023.109476
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