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Parkinson Disease Prediction Using CNN-LSTM Model from Voice Signal

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Abstract

Parkinson disease (PD) is a neurodegenerative disease cause by the lack of dopamine hormone secretion. Humans get affected in motor and non-motor activities with Parkinsonism. Motor dysfunction affects speech production. Speech is produced by the muscles of the larynx, trachea, epiglottis, vocal fold, vocal tract, tongue, pallet, and cartilage. It has been studied that PD can be identified by looking at changes in speech signals over time. The deep learning based features have been used for effective characterization of speech signal. In this paper, a hybrid CNN-LSTM classifier is proposed to efficiently detect the PD. To assess the performance of the proposed approach, 22 healthy and 28 Parkinson patients who speak Italian are used. An average accuracy of 97% is achieved with the proposed method. The results suggest that the proposed approach is appropriate for automatic identification of PD in practical scenarios.

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Data availability

Italian Database is open access database and avilable on https://ieee-dataport.org/openaccess/italian-parkinsons-voice-and-speech.

References

  1. Poewe W, Seppi K, Tanner CM, Halliday GM, Brundin P, Volkmann J, Schrag AE, Lang AE. Parkinson disease. Nat Rev Dis Prim. 2017. https://doi.org/10.1038/nrdp.2017.13.

    Article  Google Scholar 

  2. Parra-Gallego LF, Arias-Vergara T, Vasquez-Correa JC, Garcia-Ospina N, Orozco-Arroyave JR, Noth E. Automatic intelligibility assessment of parkinson’s disease with diadochokinetic exercises. Commun Comput Inf Sci. 2018. https://doi.org/10.1007/978-3-030-00353-1_20.

    Article  Google Scholar 

  3. N. Hosseini-Kivanani, J.C. Vasquez-Correa, M. Stede, E. Noth (2019) Automated crosslanguage intelligibility analysis of parkinson’s disease patients using speech recognition technologies. Proc 57th Annu Meet AssocComput Linguist Student Res Work. https://doi.org/10.18653/v1/p19-2010.

  4. Goyal J, Khandnor P, Aseri TC. A hybrid approach for Parkinson’s disease diagnosis with resonance and time-frequency based features from speech signals. Expert Syst Appl. 2021;182: 115283. https://doi.org/10.1016/j.eswa.2021.115283.

    Article  Google Scholar 

  5. 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. 2021;63:102165. https://doi.org/10.1016/j.bspc.2020.102165.

    Article  Google Scholar 

  6. ulHaq A, Li JP, Agbley BLY, Mawuli CB, Ali Z, Nazir S, Din SU. A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data. Expert Syst Appl. 2022;208:118045.

    Article  Google Scholar 

  7. Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Heal Inform. 2013;17(4):828–34. https://doi.org/10.1109/JBHI.2013.2245674.

    Article  Google Scholar 

  8. Rios-Urrego CD, Vasquez-Correa JC, Vargas-Bonilla JF, Noth E, Lopera F, Orozco-Arroyave JR. Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features. Comput Methods Programs Biomed. 2019;173:43–52. https://doi.org/10.1016/j.cmpb.2019.03.005.

    Article  Google Scholar 

  9. Trinh NH, O’Brien D. Pathological speech classification using a convolutional neural network, Proc. Irel: IMVIP; 2019.

    Google Scholar 

  10. Gunduz H. Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access. 2019;7:115540–51. https://doi.org/10.1109/ACCESS.2019.2936564.

    Article  Google Scholar 

  11. Chen C, Hua Z, Zhang R, Liu G, Wen W. Automated arrhythmia classification based on a combination network of CNN and LSTM. Biomed Signal Process Control. 2020;57: 101819. https://doi.org/10.1016/j.bspc.2019.101819.

    Article  Google Scholar 

  12. Little M, McSharry P, Hunter E, Spielman J, Ramig L. Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. Nat Preced. 2008. https://doi.org/10.1038/npre.2008.2298.1.

    Article  Google Scholar 

  13. Bhattacharya I, Bhatia MPS. SVM classification to distinguish Parkinson disease patients. In: Proceedings of the 1st amrita ACM-W celebration on women in computing in India. 2010. p. 1–6.

    Article  Google Scholar 

  14. Sakar CO, Serbes G, Gunduz A, Tunc HC, Nizam H, Sakar BE, Tutuncu M, Aydin T, Isenkul ME, Apaydin H. A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl Soft Comput. 2019;74:255–63. https://doi.org/10.1016/j.asoc.2018.10.022.

    Article  Google Scholar 

  15. Parisi L, RaviChandran N, Manaog ML. Feature-driven machine learning to improve early diagnosis of Parkinson’s disease, Expert Syst. Appl. 2018;110:182–90. https://doi.org/10.1016/j.eswa.2018.06.003.

    Article  Google Scholar 

  16. Ali L, Zhu C, Zhou M, Liu Y. Early diagnosis of Parkinson’s disease from multiple voice recordings by simultaneous sample and feature selection, Expert Syst. Appl. 2019;137:22–8. https://doi.org/10.1016/j.eswa.2019.06.052.

    Article  Google Scholar 

  17. Chen L, Wang C, Chen J, Xiang Z, Hu X. Voice disorder identification by using hilbert-huang transform (HHT) and K nearest neighbor (KNN). J Voice. 2020. https://doi.org/10.1016/j.jvoice.2020.03.009.

    Article  Google Scholar 

  18. Sivaranjini S, Sujatha CM. Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed Tools Appl. 2020;79(21–22):15467–79. https://doi.org/10.1007/s11042-019-7469-8.

    Article  Google Scholar 

  19. Karan B, Sahu SS, Orozco-Arroyave JR, Mahto K. Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson’s disease prediction. Comput Speech Lang. 2021;69: 101216.

    Article  Google Scholar 

  20. Cernak M, Orozco-Arroyave JR, Rudzicz F, Christensen H, Vásquez-Correa JC, Nöth E. Characterization of voice quality of Parkinson’s disease using differential phonological posterior features. Comput Speech Lang. 2017;46:196–208.

    Article  Google Scholar 

  21. Vásquez-Correa JC, Rios-Urrego CD, Rueda A, Orozco-Arroyave JR, Krishnan S, Nöth E. Articulation and empirical mode decomposition features in diadochokinetic exercises for the speech assessment of Parkinson’s disease patients. In: Progress in pattern recognition, image analysis, computer vision, and applications: 24th iberoamerican congress. Springer; 2019. p. 688–96.

    Chapter  Google Scholar 

  22. Karan B, Sahu SS, Mahto K. Parkinson disease prediction using intrinsic mode function-based features from speech signal. Biocybern Biomed Eng. 2020;40(1):249–64.

    Article  Google Scholar 

  23. Wilkinson N, Niesler T, Hybrid JA. CNN-BiLSTM voice activity detector. In: IEEE international conference on acoustics speech and signal processing. IEEE; 2021. p. 6803–7.

    Google Scholar 

  24. Er MB, Isik E, Isik I. Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with variational mode decomposition. Biomed Signal Process Control. 2021;70: 103006.

    Article  Google Scholar 

  25. Lilhore UK, Dalal S, Faujdar N, Margala M, Chakrabarti P, Chakrabarti T, Simaiya S, Kumar P, Thangaraju P, Velmurugan H. Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease. Sci Rep. 2023;13(1):14605.

    Article  Google Scholar 

  26. Quan C, Ren K, Luo Z, Chen Z, Ling Y. End-to-end deep learning approach for Parkinson’s disease detection from speech signals. Biocybern Biomed Eng. 2022;42(2):556–74.

    Article  Google Scholar 

  27. Xu M, Yoon S, Fuentes A, Park DS. A comprehensive survey of image augmentation techniques for deep learning. Pattern Recogn. 2023;137:109347.

    Article  Google Scholar 

  28. Maskeliūnas R. A hybrid U-lossian deep learning network for screening and evaluating Parkinson’s disease. Appl Sci. 2022;12(22):11601.

    Article  Google Scholar 

  29. Sharma P, Nayak DR, Balabantaray BK, Tanveer M, Nayak R. A survey on cancer detection via convolutional neural networks: current challenges and future directions. Neural Netw. 2023.

  30. Lipton, Z.C., Kale, D.C., Elkan, C. and Wetzel, R., (2015). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677.

  31. Dimauro, G.; Girardi, F. (2019), Italian Parkinson’s voice and speech. Available online: https://doi.org/10.21227/AW6B-TG17. Accessed 1 Oct 2023

  32. Zahid L, Maqsood M, Durrani MY, Bakhtyar M, Baber J, Jamal H, Mehmood I, Song O-Y. A spectrogram-based deep feature assisted computer-aided diagnostic system for Parkinson’s disease. IEEE Access. 2020;8:35482–95. https://doi.org/10.1109/ACCESS.2020.2974008.

    Article  Google Scholar 

  33. Arias-Vergara T, Vasquez-Correa JC, Orozco-Arroyave JR, Klumpp P, Noth E. Unobtrusive monitoring of speech impairments of Parkinson’S disease patients through mobile devices. IEEE Int Conf Acoust Speech Signal Process. 2018. https://doi.org/10.1109/ICASSP.2018.8462332.

    Article  Google Scholar 

  34. Pandey PVK, Sahu SS. Speech signal analysis using hybrid feature extraction technique for parkinson’s disease prediction. In: International conference on data science and applications. Singapore: Springer Nature Singapore; 2023. p. 427-435.

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Acknowledgements

The study was completed at the Signal Processing Lab of the Electronics and Communication Engineering Department at Birla Institute of Technology in Mesra, Ranchi, India, and was partially supported by the NM-ICPS TIH Kolkata Grant # ISI/TIH/2022/48.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, Mesra, India

    Pandit Vivek Kumar Pandey & Sitanshu Sekhar Sahu

  2. Department of Electrical and Electronics Engineering, Stellenbosch University, Stellenbosch, South Africa

    Biswajit Karan

  3. Department of Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, Mesra, India

    Sudhanshu Kumar Mishra

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  1. Pandit Vivek Kumar Pandey
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Correspondence to Pandit Vivek Kumar Pandey.

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This article is part of the topical collection “Emerging Applications of Data Science for Real-World Problems” Guest edited by Satyasai Jagannath Nanda, Rajendra Prasad Yadav and Mukesh Saraswat.

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Pandey, P.V.K., Sahu, S.S., Karan, B. et al. Parkinson Disease Prediction Using CNN-LSTM Model from Voice Signal. SN COMPUT. SCI. 5, 381 (2024). https://doi.org/10.1007/s42979-024-02728-1

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