An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics
DOI:
https://doi.org/10.4108/eetpht.10.5424Keywords:
Voice Features, Deep Neural Network, LSTM, Parkinson's Diseases, Machine Learning MLAbstract
INTRODUCTION: A neurological condition known as Parkinson's disease (PD); it affected millions of individuals worldwide. An early diagnosis can help enhance the quality of life for those who are affected with this disease. This paper presents a novel Deep neural network model based on Long Short-Term Memory (LSTM) design for the identification of PD using voice features.
OBJECTIVES: This research work aims to Identify the presence of PD using voice features of individuals. To achieve this, a Deep neural Network with LSTM is to be designed. Objective of the work is to analyse the voice data and implement the model with good accuracy.
METHODS: The proposed model is a Deep Neural Network with LSTM.
RESULTS: The proposed method uses the features gleaned from voice signals for training phase of LSTM model which achieved an accuracy of 89.23%, precision value as 0.898, F1-score of 0.965, and recall value as 0.931and is observed as best when compared to existing models.
CONCLUSION: Deep Neural Networks are more powerful than ANNs ahd when associated with LSTM , the model outperformed the job of identifying PD using voice data.
Downloads
References
Laureano Moro-Velazquez, Jorge A. Gomez-Garcia, Julian D. Arias-Londoño, Najim Dehak, Juan I. Godino-Llorente, “Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects,” 2021, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2021.102418 DOI: https://doi.org/10.1016/j.bspc.2021.102418
Amir Hossein Poorjam, Mathew Shaji Kavalekalam, Liming Shi, Yordan P. Raykov, Jesper Rindom Jensen, Max A. Little, Mads Græsbøll Christensen, “Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection,” 2019, arxiv, https://doi.org/10.48550/arXiv.1905.11785
Amrit Romana, John Bandon, Matthew Perez, Stephanie Gutierrez, Richard Richter, Angela Roberts, Emily Mower Provost, “Automatically Detecting Errors and Disfluencies in Read Speech to Predict Cognitive Impairment in People with Parkinson’s Disease,” 2021, ISCA, https://doi.org/10.21437/Interspeech.2021-1694 DOI: https://doi.org/10.21437/Interspeech.2021-1694
O. Karaman, H. Çakın, A. Alhudhaif, K. Polat, “Robust automated Parkinson disease detection based on voice signals with transfer learning,” Expert Syst. Appl. 178 (2021), 115013, https://doi.org/10.1016/j.eswa.2021.115013 DOI: https://doi.org/10.1016/j.eswa.2021.115013
Pei-Fang Guo, Prabir Bhattacharya and Nawwaf Kharma “Advances in Detecting Parkinson’s Disease” 2010, Springer.
Ali H. Al-Fatlawi, Mohammed H. Jabardi, Sai Ho Ling, “Efficient Diagnosis System for Parkinson's Disease Using Deep Belief Network” 2016, Elsevier https://doi.org/10.1109/CEC.2016.7743941 DOI: https://doi.org/10.1109/CEC.2016.7743941
Mehrbakhsh Nilashi, Othman Ibrahim, Hossein Ahmadi, Leila Shahmoradi, Mohammadreza Farahmand “A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques” 2017, Elsevier. DOI: https://doi.org/10.1016/j.bbe.2017.09.002
Salim Lahmiri, Debra Ann Dawson, Amir Shmuel “Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures” 2017, Springer. DOI: https://doi.org/10.1007/s13534-017-0051-2
Postuma, R. & Montplaisir, J. Predicting Parkinson’s disease-why, when, and how? Parkinsonism & related disorders 15, S105–S109 (2009). DOI: https://doi.org/10.1016/S1353-8020(09)70793-X
Ishihara, L., and Brayne, C., A systematic review of depression and mental illness preceding Parkinson’s disease. Acta Neurol. Scand. 113(4)211–220, 2006. doi:10.1111/j.1600-0404.2006.00579.x. DOI: https://doi.org/10.1111/j.1600-0404.2006.00579.x
B. E. Sakar, M. E. Isenkul, C. O. Sakar, A. Sertbas, F.Gurgen, S. Delil, H. Apaydin, O. Kursun, "Collection and Analysis of a Parkinson Speech Dataset with Multiple Types of Sound Recordings", IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 4, pp. 828-834, July 2013.https://doi.org/10.1109/JBHI.2013.2245674 DOI: https://doi.org/10.1109/JBHI.2013.2245674
Benba A., Jilbab A. Hammouch A., “Hybridization of best acoustic cues for detecting persons with Parkinson's disease,” 2014 Second World Conference on Complex Systems (WCCS), Agadir, 2014, pp. 622-625. DOI: https://doi.org/10.1109/ICoCS.2014.7060885
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Anila M, G Kiran Kumar, D Malathi Rani, M V V Prasad Kantipudi, D Jayaram
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
