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

Article  Google Scholar 

Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D. (2016). The mPower study, Parkinson Disease mobile data collected using ResearchKit. Scientific Data, 3(1), https://doi.org/10.1038/sdata.2016.11

Carrón, J., Campos-Roca, Y., Madruga, M., & Pérez, C. J. (2021). A mobile-assisted voice condition analysis system for Parkinson’s Disease: Assessment of usability conditions. Biomedical Engineering Online, 20(1), https://doi.org/10.1186/s12938-021-00951-y

Deng, K., Li, Y., Zhang, H., Wang, J., Albin, R. L., & Guan, Y. (2022). Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson’s Disease. Communications Biology, 5(1), https://doi.org/10.1038/s42003-022-03002-x

Dinesh, A., & He, J. (2017). Using machine learning to diagnose Parkinson’s disease from voice recordings. 2017 IEEE MIT Undergraduate Research Technology Conference (URTC).

Hireš, M., Gazda, M., Drotár, P., Pah, N. D., Motin, M. A., & Kumar, D. K. (2022). Convolutional neural network ensemble for Parkinson’s Disease detection from voice recordings. Computers in Biology and Medicine, 141(105021), 105021. https://doi.org/10.1016/j.compbiomed.2021.105021

Article  Google Scholar 

Karabayir, I., Goldman, S. M., Pappu, S., & Akbilgic, O. (2020). Gradient boosting for Parkinson’s Disease diagnosis from voice recordings. BMC Medical Informatics and Decision Making, 20(1), https://doi.org/10.1186/s12911-020-01250-7

Little, M. A., McSharry, P. E., Roberts, S. J., Costello, D. A. E., & Moroz, I. M. (2007). Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomedical Engineering Online, 6(1), 23. https://doi.org/10.1186/1475-925x-6-23

Article  Google Scholar 

Orozco-Arroyave, J. R., Arias-Londoño, J. D., Vargas-Bonilla, J. F., González-Rátiva, M. C., & Nöth, E. (2014). New Spanish speech corpus database for the analysis of people suffering from Parkinson ‘s disease. In Proceedings of the ninth international conference on language resources and evaluation (LREC’14) (pp. 342–347). ELRA.

Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5(1), https://doi.org/10.1186/s13643-016-0384-4

Pützer, M., & Barry, W. J. (2007). Saarbruecken voice database [Data set]. https://www.stimmdatenbank.coli.uni-saarland.de/

Sujatha, J., & Rajagopalan, S. P. (2017). Performance evaluation of machine learning algorithms in the classification of Parkinson disease using voice attributes. International Journal of Applied Engineering Research, 12(21), 10669–10675.

Google Scholar 

Valstar, M., Schuller, B., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., & Pantic, M. (2013). AVEC 2013: The continuous audio/visual emotion and depression recognition challenge. In Proceedings of the 3rd ACM international workshop on audio/visual emotion challenge.

Venegas, D. A. R. (2018). Dataset of vowels [Data set]. https://www.kaggle.com/datasets/darubiano57/dataset-of-vowels

Wroge, T. J., Ozkanca, Y., Demiroglu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018). Parkinson’s disease diagnosis using machine learning and voice. In 2018 IEEE signal processing in medicine and biology symposium (SPMB).