Abstract
Parkinson’s disease is a neurological illness that affects individuals at the later stage of life. Most patients complain of voice or speech abnormalities during the nascent stage of this disease, and it is difficult to recognize these abnormalities. This creates a need for a speech signal-based Parkinson's detection system to aid clinicians in the diagnosis process. A hybrid Parkinson's disease detection system has been proposed in this research work. Two speech datasets have been used in the design of this system: The first is an Italian Parkinson's Voice & Speech dataset, and the other is Mobile Device Voice Recordings at King's College London dataset. Seventeen acoustic features have been generated from the voice samples available in the datasets using Parselmouth library. In addition, based on the significance of features, the eight most significant features have been used in the design of the model. These features have been selected using genetic algorithm method. Four classifiers, k-nearest neighbors, XGBoost, random forest, and logistic regression, have been used during classification stage. The accuracy, sensitivity, f-measure, specificity, and precision parameters have been used for the analysis of the designed system. The combination of a genetic algorithm-based feature selection approach and logistic regression classifier has given 100% accuracy on Italian Parkinson's Voice & Speech dataset. The same feature extraction and classifier combination on the Mobile Device Voice Recordings at King's College London dataset have attained an accuracy level of 90%. Results have shown that the proposed system has outperformed the system found in the literature.
Similar content being viewed by others
Availability of data and material
Not Applicable.
Code availability
Not Applicable.
References
Kotsavasiloglou, C.; Kostikis, N.; Hristu-Varsakelis, D.; Arnaoutoglou, M.: Machine learning-based classification of simple drawing movements in Parkinson’s disease. Biomed. Signal Process. Control 31, 174–180 (2017). https://doi.org/10.1016/j.bspc.2016.08.003
“Parkinson’s disease”, World Health Organization. https://www.who.int/news-room/fact-sheets/detail/parkinson-disease, Accessed 19 June 2022
Lamba, R.; Gulati, T.; Jain, A.: Comparative analysis of parkinson’s disease diagnosis system: a review. Adv. in Math.: Sci. J. 9(6), 3401–3408 (2020). https://doi.org/10.37418/amsj.9.6.20
Ascherio, A.; Schwarzschild, M.A.: The epidemiology of Parkinson’s disease: risk factors and prevention. The Lancet Neurol. 15(12), 1257–1272v (2016). https://doi.org/10.1016/S1474-4422(16)30230-7
Bhat, S.; Acharya, U.R.; Hagiwara, Y.; Dadmehr, N.; Adeli, H.: Parkinson’s disease: Cause factors, measurable indicators, and early diagnosis. Comput. Biol. Med. 102, 234–241 (2018). https://doi.org/10.1016/j.compbiomed.2018.09.008
Ma, A.; Lau, K.K.; Thyagarajan, D.: Voice changes in Parkinson’s disease: What are they telling us? J. Clin. Neurosci. 72, 1–7 (2020). https://doi.org/10.1016/j.jocn.2019.12.029
Lamba, R.; Gulati, T.; Al-Dhlan, K.A.; Jain, A.: A systematic approach to diagnose Parkinson’s disease through kinematic features extracted from handwritten drawings. Journal of Reliable Intelligent Environments 7(3), 253–262 (2021). https://doi.org/10.1007/s40860-021-00130-9
Gupta, R.; Khari, M.; Gupta, D.; Crespo, R.G.: Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Inf. Sci. 530, 201–218 (2020). https://doi.org/10.1016/j.ins.2020.01.031
Afzal, H.R.; Luo, S.; Afzal, M.K.; Chaudhary, G.; Khari, M.; Kumar, S.A.: 3D face reconstruction from single 2D image using distinctive features. IEEE Access. 8, 180681–218068 (2020). https://doi.org/10.1109/ACCESS.2020.3028106
Raj, R.; Rajiv, P.; Kumar, P.; Khari, M.; Verdú, E.; Crespo, R.G.; Manogaran, G.: Feature based video stabilization based on boosted HAAR Cascade and representative point matching algorithm. Image Vis. Comput. 101, 103957 (2020). https://doi.org/10.1016/j.imavis.2020.103957
Gupta, R.; Khari, M.; Gupta, V.; Verdú, E.; Wu, X. Herrera-Viedma, E. and González-Crespo, R.: (2020)Fast single image haze removal method for inhomogeneous environment using variable scattering coefficient.. http://www.techscience.com/CMES/v123n3/39310
Lamba, R.; Gulati, T.; Alharbi, H.F.; Jain, A.: A hybrid system for Parkinson’s disease diagnosis using machine learning techniques. Inter. J. Speech Technol. 25(3), 583–593 (2021). https://doi.org/10.1007/s10772-021-09837-9
Khoury, N.; Attal, F.; Amirat, Y.; Oukhellou, L.; Mohammed, S.: Data-driven based approach to aid Parkinson’s disease diagnosis. Sensors. 19(2), 242 (2019). https://doi.org/10.3390/s19020242
Oh, S.L.; Hagiwara, Y.; Raghavendra, U.; Yuvaraj, R.; Arunkumar, N., Murugappan, M. and Acharya, U.R.: A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Computing and Applications. 1-7 (2020). https://doi.org/10.1007/s00521-018-3689-5
Loconsole, C.; Cascarano, G.D.; Brunetti, A.; Trotta, G.F.; Losavio, G.; Bevilacqua, V.; Di Sciascio, E.: A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis. Pattern Recogn. Lett. 121, 28–36 (2019). https://doi.org/10.1016/j.patrec.2018.04.006
Ertuğrul, Ö.F.; Kaya, Y.; Tekin, R.; Almalı, M.N.: Detection of Parkinson’s disease by shifted one-dimensional local binary patterns from gait. Expert Syst. Appl. 56, 156–163 (2016). https://doi.org/10.1016/j.eswa.2016.03.018
Sivaranjini, S.; Sujatha, C.M.: Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimedia Tools Appl. 79(21–22), 15467–15479 (2020). https://doi.org/10.1007/s11042-019-7469-8
Goyal, J.; Khandnor, P.; Aseri, T.C.: A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson’s Disease. Inter. J. Data Sci. Anal. 11(1), 69–83 (2020). https://doi.org/10.1007/s41060-020-00234-0
Vásquez-Correa, J.C.; Arias-Vergara, T.; Orozco-Arroyave, J.R.; Vargas-Bonilla, J.F.; Arias-Londoño, J.D. and Nöth, E.: (2015) Automatic detection of Parkinson's disease from continuous speech recorded in non-controlled noise conditions. In Sixteenth Annual Conference of the International Speech Communication Association.. https://doi.org/10.21437/Interspeech.2015-36
Appakaya, S.B., Sankar, R. and Sheybani, E.: (2021) Novel Unsupervised Feature Extraction Protocol using Autoencoders for Connected Speech: Application in Parkinson's Disease Classification. In 2021 Wireless Telecommunications Symposium (WTS), 1–5. https://doi.org/10.1109/WTS51064.2021.9433683
Karan, B.; Sahu, S.S.; Mahto, K.: Parkinson disease prediction using intrinsic mode function-based features from speech signal. Biocybernetics and Biomedical Engineering. 40(1), 249–264 (2020). https://doi.org/10.1016/j.bbe.2019.05.005
Quan, C.; Ren, K.; Luo, Z.: A Deep Learning-Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech. IEEE Access. 9, 10239–10252 (2021). https://doi.org/10.1109/ACCESS.2021.3051432
Perez, C.; Campos-Roca, Y.; Naranjo, L.; Martín, J.: Diagnosis and tracking of Parkinson’s disease by using automatically extracted acoustic features. J Alzheimers Dis Parkinsonism. 6(260), 161–0460 (2016). https://doi.org/10.4172/2161-0460.1000260
Rahman, A.; Rizvi, S.S.; Khan, A.; Afzaal Abbasi, A.; Khan, S.U.; Chung, T.S.: Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier. Mob. Inf. Syst. (2021). https://doi.org/10.1155/2021/8822069
Solana-Lavalle, G.; Rosas-Romero, R.: Analysis of voice as an assisting tool for detection of Parkinson’s disease and its subsequent clinical interpretation. Biomed. Signal Process. Control 66, 102415 (2021). https://doi.org/10.1016/j.bspc.2021.102415
Liu, Y.; Li, Y.; Tan, X.; Wang, P.; Zhang, Y.: Local discriminant preservation projection embedded ensemble learning based dimensionality reduction of speech data of Parkinson’s disease. Biomed. Signal Process. Control 63, 102165 (2021). https://doi.org/10.1016/j.bspc.2020.102165
Benba, A., Jilbab, A. and Hammouch, A.: (2016) Voice analysis for detecting patients with Parkinson's disease using the hybridization of the best acoustic features. International Journal on Electrical Engineering and Informatics. https://doi.org/10.15676/ijeei.2016.8.1.8
Zhang, L.; Qu, Y.; Jin, B.; Jing, L.; Gao, Z.; Liang, Z.: An intelligent mobile-enabled system for diagnosing Parkinson disease: Development and validation of a speech impairment detection system. JMIR Med. Inform. 8(9), e18689 (2020). https://doi.org/10.2196/18689
ER, M.B., Esme, I.S.I.K. and Ibrahim, I.S.I.K. (2021) Parkinson's Detection Based On Combined CNN And LSTM Using Enhanced Speech Signals With Variational Mode Decomposition. https://doi.org/10.21203/rs.3.rs-305818/v1
Mohammadi, A.G.; Mehralian, P.; Naseri, A.; Sajedi, H.: Parkinson’s disease diagnosis: The effect of autoencoders on extracting features from vocal characteristics. Array. 11, 100079 (2021). https://doi.org/10.1016/j.array.2021.100079
Tuncer, T.; Dogan, S.: A novel octopus based Parkinson’s disease and gender recognition method using vowels. Appl. Acoust. 155, 75–83 (2019). https://doi.org/10.1016/j.apacoust.2019.05.019
Yaman, O.; Ertam, F.; Tuncer, T.: Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features. Med. Hypotheses 135, 109483 (2020). https://doi.org/10.1016/j.mehy.2019.109483
Tuncer, T.; Dogan, S.; Acharya, U.R.: Automated detection of Parkinson’s disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybernetics Biomed. Eng. 40(1), 211–220 (2020). https://doi.org/10.1016/j.bbe.2019.05.006
Goyal, J.; Khandnor, P.; Aseri, T.C.: A Hybrid Approach for Parkinson’s Disease diagnosis with Resonance and Time-Frequency based features from Speech Signals. Expert Syst. Appl. 182, 115283 (2021). https://doi.org/10.1016/j.eswa.2021.115283
Loh, H.W.; Ooi, C.P.; Palmer, E.; Barua, P.D.; Dogan, S.; Tuncer, T.; Baygin, M.; Acharya, U.R.: GaborPDNet: Gabor transformation and deep neural network for Parkinson’s disease detection using EEG signals. Electronics 10(14), 1740 (2021). https://doi.org/10.3390/electronics10141740
Giovanni Dimauro, Francesco Girardi.: (2019) Italian Parkinson's Voice and Speech.. https://doi.org/10.21227/aw6b-tg17
“Italian Parkinson’s Voice and Speech Dataset”, available online: https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speech. Accessed 05 October 2021.
Jaeger, D.T.H. and Stadtschnitzer, M.: Mobile Device Voice Recordings at King’s College London (MDVR-KCL) from both early and advanced Parkinson’s disease patients and healthy controls (2020). https://doi.org/10.5281/zenodo.2867216
“Mobile Device Voice Recordings at King's College London”, available online: https://zenodo.org/record/2867216#.YtY1SHZBzIU. Accessed 10 November 2021.
Teixeira, J.P.; Oliveira, C.; Lopes, C.: Vocal acoustic analysis–jitter, shimmer and hnr parameters. Procedia Technol. 9, 1112–1122 (2013). https://doi.org/10.1016/j.protcy.2013.12.124
Rani, P.; Kumar, R.; Jain, A.; Chawla, S.K.: A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination. International Journal of Information System Modeling and Design (IJISMD). 12(2), 17–38 (2021). https://doi.org/10.4018/IJISMD.2021040102
Harold Robinson, Y.; Vimal, S.; Khari, M.; Hernández, F.C.L.; Crespo, R.G.: Tree-based convolutional neural networks for object classification in segmented satellite images. The Inter. J. High Performance Comput. Appl. (2020). https://doi.org/10.1177/1094342020945026
Rani, P.; Kumar, R.; Sid, N.M.O.; Ahmed, A.J.: A decision support system for heart disease prediction based upon machine learning. J. Reliable Intelligent Environ. 7(3), 263–275 (2021). https://doi.org/10.1007/s40860-021-00133-6
Tama, B.A.; Im, S.; Lee, S.: Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble. Biomed. Res. Int. (2020). https://doi.org/10.1155/2020/9816142
Rani, P.; Kumar, R.; Jain, A.: HIOC: a hybrid imputation method to predict missing values in medical datasets”. International Journal of Intelligent Computing and Cybernetics. (2021). https://doi.org/10.1108/IJICC-03-2021-0042
Lamba, R.; Gulati, T.; Jain, A.: A Hybrid Feature Selection Approach for Parkinson’s Detection Based on Mutual Information Gain and Recursive Feature Elimination. Arabian J. Sci. Eng. 47(8), 10263–10276 (2022). https://doi.org/10.1007/s13369-021-06544-0
Rani, P.; Kumar, R.; Jain, A.: Multistage Model for Accurate Prediction of Missing Values Using Imputation Methods in Heart Disease Dataset. In: Raj, J.S.; Iliyasu, A.M.; Bestak, R.; Baig, Z.A. (Eds.) Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2020, pp. 637–653. Springer Singapore, Singapore (2021). https://doi.org/10.1007/978-981-15-9651-3_53
Khari, M.; Garg, A.K.; Crespo, R.G.; Verdú, E.: Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks. Int. J. Interact. Multim. Artif. Intell. 5(7), 22–27 (2019). https://doi.org/10.9781/ijimai.2019.09.002
Lamba, R.; Gulati, T.; Jain, A.: Automated Parkinson’s Disease Diagnosis System Using Transfer Learning Techniques. In: Marriwala, N.; Tripathi, C.C.; Jain, S.; Mathapathi, S. (Eds.) Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021, pp. 183–196. Springer Singapore, Singapore (2022). https://doi.org/10.1007/978-981-16-8774-7_16
Pillai, M.S.; Chaudhary, G.; Khari, M.; Crespo, R.G.: Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning. Soft. Comput. 25(18), 1929–11940 (2021). https://doi.org/10.1007/s00500-021-05576-w
Funding
Not Applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lamba, R., Gulati, T., Jain, A. et al. A Speech-Based Hybrid Decision Support System for Early Detection of Parkinson's Disease. Arab J Sci Eng 48, 2247–2260 (2023). https://doi.org/10.1007/s13369-022-07249-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-022-07249-8