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A new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN

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Abstract

Parkinson's is one of the most rapidly increasing neurological diseases in the world, caused by the deficiency of dopamine-producing cells in the brain. Voice disorders are a significant finding in the early stage of Parkinson's disease (PD). Detection of this finding at an early stage of the disease allows early treatment of the disease. Therefore, in this study, using sound data, a hybrid model for detecting PD has been designed. In the developed method, first of all, the sound data were converted into spectrograms. Then, the feature maps of the obtained spectrogram images were extracted using 3 different CNN architectures. Feature maps with different features obtained by utilizing the accumulation of different architectures were combined. Then, these features were selected using the arithmetic optimization algorithm (AOA), one of the most recent metaheuristic optimization algorithms, and then classified by support vector machine (SVM) and K-nearest neighbors (KNN). One of the important novelties in the study is the reduction of the size of the acquired feature maps with AOA, a new and high-performance metaheuristic approach. The success of the proposed model in diagnosing Parkinson's disease reached up to 98.19%. In addition, feature maps of the sound data in the dataset were acquired by using the MFCC method to compare the performance of the proposed model. Eight different classifiers were used to categorize the acquired feature maps. The highest accuracy value obtained in this method was obtained in the Random Forest classifier with 93.98%.

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Data availability

A publicly available data set was used in the paper.

References

  1. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., Gandomi, A.H.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)

    ADS  MathSciNet  Google Scholar 

  2. Arias-Vergara, T., Vasquez-Correa, J. C., Orozco-Arroyave, J. R., Klumpp, P., & Nöth, E.: Unobtrusive monitoring of speech impairments of Parkinson's disease patients through mobile devices. Paper presented at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2018)

  3. Aszemi, N.M., Dominic, P.: Hyperparameter optimization in convolutional neural network using genetic algorithms. Int. J. Adv. Comput. Sci. Appl. (2019). https://doi.org/10.14569/IJACSA.2019.0100638

    Article  Google Scholar 

  4. Auclair-Ouellet, N., Lieberman, P., Monchi, O.: Contribution of language studies to the understanding of cognitive impairment and its progression over time in Parkinson’s disease. Neurosci. Biobehav. Rev. 80, 657–672 (2017)

    PubMed  Google Scholar 

  5. Bayes, T.: Naive bayes classifier. Article Sources and Contributors, pp. 1–9 (1968)

  6. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2), 281–305 (2012)

    MathSciNet  Google Scholar 

  7. Bochinski, E., Senst, T., Sikora, T.: Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. Paper presented at the 2017 IEEE international conference on image processing (ICIP), (2017)

  8. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Google Scholar 

  9. Caliskan, A., Badem, H., Basturk, A., Yuksel, M.E.: Diagnosis of the parkinson disease by using deep neural network classifier. IU-J. Electr. Electro. Eng. 17(2), 3311–3318 (2017)

    Google Scholar 

  10. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. Paper presented at the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, (2016)

  11. Chiaramonte, R., Bonfiglio, M.: Acoustic analysis of voice in Parkinson’s disease: a systematic review of voice disability and meta-analysis of studies. Rev. Neurol. 70(11), 393–405 (2020)

    CAS  PubMed  Google Scholar 

  12. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Google Scholar 

  13. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Google Scholar 

  14. Dimauro, G.: Italian Parkinson’s Voice and Speech. Austin, TX, US: IEEE Dataport (2019)

  15. Dixit, U., Mishra, A., Shukla, A., Tiwari, R.: Texture classification using convolutional neural network optimized with whale optimization algorithm. SN Appl. Sci. 1, 1–11 (2019)

    Google Scholar 

  16. Dorantes-Méndez, G., Mendez, M.O., Méndez-Magdaleno, L.E., Muñoz-Mata, B.G., Rodríguez-Leyva, I., Mejía-Rodríguez, A.R.: Characterization and classification of Parkinson’s disease patients based on symbolic dynamics analysis of heart rate variability. Biomed. Signal Process. Control 71, 103064 (2022)

    Google Scholar 

  17. Dorsey, E., Sherer, T., Okun, M.S., Bloem, B.R.: The emerging evidence of the Parkinson pandemic. J. Parkinsons Dis. 8(s1), S3–S8 (2018)

    PubMed  PubMed Central  Google Scholar 

  18. Erdogdu Sakar, B., Serbes, G., Sakar, C.O.: Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson’s disease. PLoS ONE 12(8), e0182428 (2017)

    PubMed  PubMed Central  Google Scholar 

  19. Eroglu, Y., Yildirim, M., Cinar, A.: mRMR-based hybrid convolutional neural network model for classification of Alzheimer’s disease on brain magnetic resonance images. Int. J. Imaging Syst. Technol. 32(2), 517–527 (2022)

    Google Scholar 

  20. Eroğlu, O., Yildirim, M.: Automatic detection of eardrum otoendoscopic images in patients with otitis media using hybrid-based deep models. Int. J. Imaging Syst. Technol. 32(3), 717–727 (2022)

    Google Scholar 

  21. He, K., Zhang, X., Ren, S. and Sun, J.: Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition(2016)

  22. Hemmerling, D., Orozco-Arroyave, J. R., Skalski, A., Gajda, J. and Nöth, E.: Automatic Detection of Parkinson's Disease Based on Modulated Vowels. Paper presented at the INTERSPEECH, (2016)

  23. Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K. Q.: Densely connected convolutional networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, (2017)

  24. Huang, L., Ye, X., Yang, M., Pan, L., Hua Zheng, S.: MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson’s disease diagnosis. Comput. Biol. Med. 152, 106308 (2023)

    PubMed  Google Scholar 

  25. Johri, A. and Tripathi, A.: Parkinson disease detection using deep neural networks. Paper presented at the 2019 Twelfth International Conference on Contemporary Computing (IC3), (2019)

  26. Karan, B., Sahu, S.S., Orozco-Arroyave, J.R., Mahto, K.: Hilbert spectrum analysis for automatic detection and evaluation of Parkinson’s speech. Biomed. Signal Process. Control 61, 102050 (2020)

    Google Scholar 

  27. Karan, B., Sahu, S.S., Mahto, K.: Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybern. Biomed. Eng. 40(1), 249–264 (2020)

    Google Scholar 

  28. Karasulu, B.: Sound scene and events detection using deep learning in the scope of cyber security for multimedia systems. Acta Infologica 3(2), 60–82 (2019)

    Google Scholar 

  29. Kiziloluk, S., Sert, E.: COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer. Med. Biol. Eng. Compu. 60(6), 1595–1612 (2022)

    Google Scholar 

  30. Kızıloluk, S., Sert, E.: Hurricane-faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer. Multimed. Tools Appl. 81(26), 37981–37999 (2022)

    Google Scholar 

  31. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25, 84–90 (2012)

    Google Scholar 

  32. Kumar, P., Hati, A.S.: Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM. ISA Trans. 111, 350–359 (2021)

    PubMed  Google Scholar 

  33. Lamba, R., Gulati, T., & Jain, A.: Automated Parkinson’s disease diagnosis system using transfer learning techniques. In Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021 (pp. 183–196), (2022a) Springer

  34. 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. J. Reliab. Intell. Environ. 7, 1–10 (2021)

    Google Scholar 

  35. Lamba, R., Gulati, T., Jain, A.: An intelligent system for Parkinson’s diagnosis using hybrid feature selection approach. Int. J. Softw. Innov. (IJSI) 10(1), 1–13 (2022)

    Google Scholar 

  36. Lamba, R., Gulati, T., Jain, A., Rani, P.: A speech-based hybrid decision support system for early detection of Parkinson’s disease. Arab. J. Sci. Eng. 48(2), 2247–2260 (2023)

    Google Scholar 

  37. Li, H., Pun, C.-M., Xu, F., Pan, L., Zong, R., Gao, H., Lu, H.: A hybrid feature selection algorithm based on a discrete artificial bee colony for Parkinson’s diagnosis. ACM Trans. Internet Technol. 21(3), 1–22 (2021)

    Google Scholar 

  38. Majda-Zdancewicz, E., Potulska-Chromik, A., Jakubowski, J., Nojszewska, M., Kostera-Pruszczyk, A.: Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease. Bull. Polish Acad. Sci. Tech. Sci. (2021). https://doi.org/10.24425/bpasts.2021.137347

    Article  Google Scholar 

  39. Narendra, N., Schuller, B., Alku, P.: The detection of Parkinson’s disease from speech using voice source information. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 1925–1936 (2021)

    Google Scholar 

  40. Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2106–2112 (2010)

    PubMed  Google Scholar 

  41. Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013)

    PubMed  PubMed Central  Google Scholar 

  42. Nguyen, T., Nguyen, G., Nguyen, B.M.: EO-CNN: an enhanced CNN model trained by equilibrium optimization for traffic transportation prediction. Proc. Comput. Sci. 176, 800–809 (2020)

    Google Scholar 

  43. Prasad, B. and Prasanna, S. M.: Speech, audio, image and biomedical signal processing using neural networks (Vol. 83): Springer, (2007)

  44. Pugh, W., Wong, R., Falson, F., Michniak, B., Moss, G.: Discriminant analysis as a tool to identify compounds with potential as transdermal enhancers. J. Pharm. Pharmacol. 57(11), 1389–1396 (2005)

    CAS  PubMed  Google Scholar 

  45. Qasim, H.M., Ata, O., Ansari, M.A., Alomary, M.N., Alghamdi, S., Almehmadi, M.: Hybrid feature selection framework for the Parkinson imbalanced dataset prediction problem. Medicina 57(11), 1217 (2021)

    PubMed  PubMed Central  Google Scholar 

  46. 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. 42(2), 556–574 (2022)

    Google Scholar 

  47. Sadek, R. M., Mohammed, S. A., Abunbehan, A. R. K., Ghattas, A. K. H. A., Badawi, M. R., Mortaja, M. N., Abu-Naser, S. S.: Parkinson's disease prediction using artificial neural network, (2019)

  48. Sakar, B.E., Isenkul, M.E., Sakar, C.O., Sertbas, A., Gurgen, F., Delil, S., Kursun, O.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17(4), 828–834 (2013)

    PubMed  Google Scholar 

  49. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, (2018)

  50. Shahbakhi, M., Far, D.T., Tahami, E.: Speech analysis for diagnosis of Parkinson’s disease using genetic algorithm and support vector machine. J. Biomed. Sci. Eng. (2014). https://doi.org/10.4236/jbise.2014.74019

    Article  Google Scholar 

  51. Smith, K.M., Caplan, D.N.: Communication impairment in Parkinson’s disease: impact of motor and cognitive symptoms on speech and language. Brain Lang. 185, 38–46 (2018)

    PubMed  Google Scholar 

  52. Soon, F.C., Khaw, H.Y., Chuah, J.H., Kanesan, J.: Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition. IET Intel. Transport Syst. 12(8), 939–946 (2018)

    Google Scholar 

  53. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, (2015)

  54. Tan, M. and Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. Paper presented at the International conference on machine learning, (2019)

  55. Tang, J., Yang, B., Adams, M.P., Shenkov, N.N., Klyuzhin, I.S., Fotouhi, S., Sossi, V.: Artificial neural network–based prediction of outcome in Parkinson’s disease patients using DaTscan SPECT imaging features. Mol. Imaging Biol. 21(6), 1165–1173 (2019)

    CAS  PubMed  Google Scholar 

  56. Tsanas, A., Little, M.A., McSharry, P.E., Spielman, J., Ramig, L.O.: Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans. Biomed. Eng. 59(5), 1264–1271 (2012)

    PubMed  Google Scholar 

  57. Ul Haq, A., Li, J., Memon, M.H., Ali, Z., Abbas, S.Z., Nazir, S.: Recognition of the Parkinson’s disease using a hybrid feature selection approach. J. Intell. Fuzzy Syst. 39(1), 1319–1339 (2020)

    Google Scholar 

  58. Vásquez-Correa, J. C., Orozco-Arroyave, J. R. and Nöth, E.: Convolutional Neural Network to Model Articulation Impairments in Patients with Parkinson's Disease. Paper presented at the INTERSPEECH, (2017)

  59. Vásquez-Correa, J. C., Arias-Vergara, T., Orozco-Arroyave, J. R., & Nöth, E.: A Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease. Paper presented at the INTERSPEECH, (2018)

  60. Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A.E., Pandey, H.M.: An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput. Electron. Agric. 175, 105456 (2020)

    Google Scholar 

  61. Wodzinski, M., Skalski, A., Hemmerling, D., Orozco-Arroyave, J. R., and Nöth, E.: Deep learning approach to Parkinson’s disease detection using voice recordings and convolutional neural network dedicated to image classification. Paper presented at the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2019)

  62. Yao, D., Chi, W., Khishe, M.: Parkinson’s disease and cleft lip and palate of pathological speech diagnosis using deep convolutional neural networks evolved by IPWOA. Appl. Acoust. 199, 109003 (2022)

    Google Scholar 

  63. Yildirim, M.: MFCC Yöntemi ve Önerilen Derin Model ile Çevresel Seslerin Otomatik Olarak Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 34(1), 449–457 (2022)

    Google Scholar 

  64. Yousif, N.R., Balaha, H.M., Haikal, A.Y., El-Gendy, E.M.: A generic optimization and learning framework for Parkinson disease via speech and handwritten records. J. Ambient Intell. Human. Comput. 14, 1–21 (2022)

    Google Scholar 

  65. Zhang, J.: Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease. NPJ Parkinson’s Dis. 8(1), 13 (2022)

    MathSciNet  Google Scholar 

  66. Zhang, X., Zhou, X., Lin, M., & Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, (2018)

  67. Zhang, Y., Zhao, P., Li, D., Konstantin, K.: Spatial attention based real-time object detection network for Internet of Things devices. IEEE Access 8, 165863–165871 (2020)

    Google Scholar 

  68. Zhang, T., Zhang, Y., Sun, H., Shan, H.: Parkinson disease detection using energy direction features based on EMD from voice signal. Biocybern. Biomed. Eng. 41(1), 127–141 (2021)

    Google Scholar 

  69. Zhao, A., Li, J.: A significantly enhanced neural network for handwriting assessment in Parkinson’s disease detection. Multimed. Tools Appl. 82, 1–21 (2023)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey

    Muhammed Yildirim, Soner Kiziloluk & Eser Sert

  2. Department of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey

    Serpil Aslan

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  1. Muhammed Yildirim
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Yildirim, M., Kiziloluk, S., Aslan, S. et al. A new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN. SIViP 18, 1227–1240 (2024). https://doi.org/10.1007/s11760-023-02826-2

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