1. bookVolume 31 (2021): Edition 4 (December 2021)
    Advanced Machine Learning Techniques in Data Analysis (special section, pp. 549-611), Maciej Kusy, Rafał Scherer, and Adam Krzyżak (Eds.)
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A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

Publié en ligne: 30 Dec 2021
Volume & Edition: Volume 31 (2021) - Edition 4 (December 2021)<br/>Advanced Machine Learning Techniques in Data Analysis (special section, pp. 549-611), Maciej Kusy, Rafał Scherer, and Adam Krzyżak (Eds.)
Pages: 549 - 561
Reçu: 01 Jan 2021
Accepté: 05 Sep 2021
Détails du magazine
License
Format
Magazine
eISSN
2083-8492
Première parution
05 Apr 2007
Périodicité
4 fois par an
Langues
Anglais
Abstract

Parkinson’s disease (PD) is the second most common neurological disorder in the world. Nowadays, it is estimated that it affects from 2% to 3% of the global population over 65 years old. In clinical environments, a spiral drawing task is performed to help to obtain the disease’s diagnosis. The spiral trajectory differs between people with PD and healthy ones. This paper aims to analyze differences between handmade drawings of PD patients and healthy subjects by applying the SqueezeNet convolutional neural network (CNN) model as a feature extractor, and a support vector machine (SVM) as a classifier. The dataset used for training and testing consists of 514 handwritten draws of Archimedes’ spiral images derived from heterogeneous sources (digital and paper-based), from which 296 correspond to PD patients and 218 to healthy subjects. To extract features using the proposed CNN, a model is trained and 20% of its data is used for testing. Feature extraction results in 512 features, which are used for SVM training and testing, while the performance is compared with that of other machine learning classifiers such as a Gaussian naive Bayes (GNB) classifier (82.61%) and a random forest (RF) (87.38%). The proposed method displays an accuracy of 91.26%, which represents an improvement when compared to pure CNN-based models such as SqueezeNet (85.29%), VGG11 (87.25%), and ResNet (89.22%).

Keywords

Al-Yousef, N., Al, R., Al, R., Al-Abdullatif, R., Al-Mutairi, F. and Bchir, O. (2020). Parkinson’s disease diagnosis using spiral test on digital tablets, International Journal of Advanced Computer Science and Applications 11(5): 461–470.10.14569/IJACSA.2020.0110560 Search in Google Scholar

Ali, L., Zhu, C., Golilarz, N.A., Javeed, A., Zhou, M. and Liu, Y. (2019). Reliable Parkinson’s disease detection by analyzing handwritten drawings: Construction of an unbiased cascaded learning system based on feature selection and adaptive boosting model, IEEE Access 7: 116480–116489.10.1109/ACCESS.2019.2932037 Search in Google Scholar

Almeida, J.S., Filho, P.P.R., Carneiro, T., Wei, W., Damaševičius, R., Maskeliūnas, R. and de Albuquerque, V.H.C. (2019). Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques, Pattern Recognition Letters 125: 55–62.10.1016/j.patrec.2019.04.005 Search in Google Scholar

Awatramani, V. and Gupta, D. (2020). Parkinson’s disease detection through visual deep learning, in D. Gupta et al. (Eds), Advances in Intelligent Systems and Computing, Springer, Singapore, pp. 963–972. Search in Google Scholar

Bernardo, L.S., Quezada, A., Munoz, R., Maia, F.M., Pereira, C.R., Wu, W. and de Albuquerque, V.H.C. (2019). Handwritten pattern recognition for early Parkinson’s disease diagnosis, Pattern Recognition Letters 125: 78–84.10.1016/j.patrec.2019.04.003 Search in Google Scholar

Chakraborty, S., Aich, S., Jong-Seong-Sim, Han, E., Park, J. and Kim, H.-C. (2020). Parkinson’s disease detection from spiral and wave drawings using convolutional neural networks: A multistage classifier approach, IEEE 22nd International Conference on Advanced Communication Technology (ICACT), Phoenix Park, South Korea, pp. 298–303. Search in Google Scholar

Chen, J.H. and Asch, S.M. (2017). Machine learning and prediction in medicine—Beyond the peak of inflated expectations, New England Journal of Medicine 376(26): 2507–2509.10.1056/NEJMp1702071595382528657867 Search in Google Scholar

Chen, J., Liao, M., Wang, G. and Chen, C. (2020). An intelligent multimodal framework for identifying children with autism spectrum disorder, International Journal of Applied Mathematics and Computer Science 30(3): 435–448, DOI: 10.34768/amcs-2020-0032. Search in Google Scholar

Chen-Plotkin, A.S. (2017). Blood transcriptomics for Parkinson disease?, Nature Reviews Neurology 14(1): 5–6.10.1038/nrneurol.2017.166583112329192261 Search in Google Scholar

Cristianini, N. and Ricci, E. (2008). Support vector machines, in M.Y. Kao et al. (Ed.), Encyclopedia of Algorithms, Springer, Boston, pp. 928–932.10.1007/978-0-387-30162-4_415 Search in Google Scholar

Crowley, E., Nolan, Y. and Sullivan, A. (2019). Exercise as a therapeutic intervention for motor and non-motor symptoms in Parkinson’s disease: Evidence from rodent models, Progress in Neurobiology 172: 2–22.10.1016/j.pneurobio.2018.11.00330481560 Search in Google Scholar

Damaševičius, R. (2010). Optimization of SVM parameters for recognition of regulatory DNA sequences, TOP 18(2): 339–353.10.1007/s11750-010-0152-x Search in Google Scholar

de Ipina, K.L., Solé-Casals, J., Faúndez-Zanuy, M., Calvo, P., Sesa, E., Roure, J., de Lizarduy, U.M., Beitia, B., Fernández, E., Iradi, J., Garcia-Melero, J. and Bergareche, A. (2018). Automatic analysis of Archimedes’ spiral for characterization of genetic essential tremor based on Shannon’s entropy and fractal dimension, Entropy 20(7): 531.10.3390/e20070531751305533265620 Search in Google Scholar

Espay, A.J., Bonato, P., Nahab, F.B., Maetzler, W., Dean, J.M., Klucken, J., Eskofier, B.M., Merola, A., Horak, F., Lang, A.E., Reilmann, R., Giuffrida, J., Nieuwboer, A., Horne, M., Little, M.A., Litvan, I., Simuni, T., Dorsey, E.R., Burack, M.A., Kubota, K., Kamondi, A., Godinho, C., Daneault, J.-F., Mitsi, G., Krinke, L., Hausdorff, J.M., Bloem, B.R. and Papapetropoulos, S. (2016). Technology in Parkinson’s disease: Challenges and opportunities, Movement Disorders 31(9): 1272–1282.10.1002/mds.26642501459427125836 Search in Google Scholar

Gallicchio, C., Micheli, A. and Pedrelli, L. (2018). Deep echo state networks for diagnosis of Parkinson’s disease, 26th European Symposium on Artificial Neural Networks, ESANN 2018, Bruges, Belgium, pp. 397–402. Search in Google Scholar

Garre-Olmo, J., Faúndez-Zanuy, M., de Ipiña, K.L., Calvó-Perxas, L. and Turró-Garriga, O. (2017). Kinematic and pressure features of handwriting and drawing: Preliminary results between patients with mild cognitive impairment, Alzheimer disease and healthy controls, Current Alzheimer Research 14(9): 960–968.10.2174/1567205014666170309120708573551828290244 Search in Google Scholar

Gelb, D.J., Oliver, E. and Gilman, S. (1999). Diagnostic criteria for Parkinson disease, Archives of Neurology 56(1): 33–39.10.1001/archneur.56.1.339923759 Search in Google Scholar

Gil-Martín, M., Montero, J.M. and San-Segundo, R. (2019). Parkinson’s disease detection from drawing movements using convolutional neural networks, Electronics 8(8): 1–10, Article 907.10.3390/electronics8080907 Search in Google Scholar

Guan, H., Zhang, Y., Cheng, H.-D. and Tang, X. (2020). Bounded-abstaining classification for breast tumors in imbalanced ultrasound images, International Journal of Applied Mathematics and Computer Science 30(2): 325–336, DOI: 10.34768/amcs-2020-0025. Search in Google Scholar

Gupta, D., Julka, A., Jain, S., Aggarwal, T., Khanna, A., Arunkumar, N. and de Albuquerque, V.H.C. (2019). Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease, Cognitive Systems Research 52: 36–48.10.1016/j.cogsys.2018.06.006 Search in Google Scholar

Gupta, D., Sundaram, S., Khanna, A., Hassanien, A.E. and de Albuquerque, V. H.C. (2018). Improved diagnosis of Parkinson’s disease using optimized crow search algorithm, Computers & Electrical Engineering 68: 412–424.10.1016/j.compeleceng.2018.04.014 Search in Google Scholar

Haubenberger, D., Kalowitz, D., Nahab, F.B., Toro, C., Ippolito, D., Luckenbaugh, D.A., Wittevrongel, L. and Hallett, M. (2011). Validation of digital spiral analysis as outcome parameter for clinical trials in essential tremor, Movement Disorders 26(11): 2073–2080.10.1002/mds.23808411768121714004 Search in Google Scholar

Hess, C.W., Hsu, A.W., Yu, Q., Ortega, R. and Pullman, S.L. (2014). Increased variability in spiral drawing in patients with functional (psychogenic) tremor, Human Movement Science 38: 15–22.10.1016/j.humov.2014.08.00725240176 Search in Google Scholar

Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J. and Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50× fewer parameters and <0.5 mb model size, arXiv 1602.07360. Search in Google Scholar

Impedovo, D. and Pirlo, G. (2019). Dynamic handwriting analysis for the assessment of neurodegenerative diseases: A pattern recognition perspective, IEEE Reviews in Biomedical Engineering 12: 209–220.10.1109/RBME.2018.284067929993722 Search in Google Scholar

Impedovo, D., Pirlo, G. and Vessio, G. (2018). Dynamic handwriting analysis for supporting earlier Parkinson’s disease diagnosis, Information 9(10): 1–11, Article: 247.10.3390/info9100247 Search in Google Scholar

Isenkul, M., Sakar, B. and O. Kursun, O. (2014). Improved spiral test using digitized graphics tablet for monitoring Parkinson’s disease, 2nd International Conference on e-Health and Telemedicine (ICEHTM-2014), Istanbul, Turkey, pp. 171–175. Search in Google Scholar

Islam, M.M., Tasnim, N. and Baek, J.-H. (2020). Human gender classification using transfer learning via Pareto frontier CNN networks, Inventions 5(2): 16.10.3390/inventions5020016 Search in Google Scholar

Jin, W., Dong, S., Dong, C. and Ye, X. (2021). Hybrid ensemble model for differential diagnosis between Covid-19 and common viral pneumonia by chest x-ray radiograph, Computers in Biology and Medicine 131, Article 104252.10.1016/j.compbiomed.2021.104252796681933610001 Search in Google Scholar

Kalliola, J., Kapočiūtė-Dzikienė, J. and Damaševičius, R. (2021). Neural network hyperparameter optimization for prediction of real estate prices in Helsinki, PeerJ Computer Science 7: e444.10.7717/peerj-cs.444806423433977129 Search in Google Scholar

Khatamino, P., Canturk, I. and Ozyilmaz, L. (2018). A deep learning-CNN based system for medical diagnosis: An application on Parkinson’s disease handwriting drawings, 6th International IEEE Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey, pp. 1–6. Search in Google Scholar

Khoshdeli, M., Cong, R. and Parvin, B. (2017). Detection of nuclei in H&E stained sections using convolutional neural networks, IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, USA, pp. 105–108. Search in Google Scholar

Kowal, M., Skobel, M., Gramacki, A. and Korbicz, J. (2021). Breast cancer nuclei segmentation and classification based on a deep learning approach, International Journal of Applied Mathematics and Computer Science 31(1): 85–106, DOI: 10.34768/amcs-2021-0007. Search in Google Scholar

Kriti, Virmani, J. and Agarwal, R. (2020). Deep feature extraction and classification of breast ultrasound images, Multimedia Tools and Applications 79(37–38): 27257–27292.10.1007/s11042-020-09337-z Search in Google Scholar

Lauraitis, A., Maskeliunas, R., Damasevicius, R., Polap, D. and Wozniak, M. (2019). A smartphone application for automated decision support in cognitive task based evaluation of central nervous system motor disorders, IEEE Journal of Biomedical and Health Informatics 23(5): 1865–1876.10.1109/JBHI.2019.289172930629520 Search in Google Scholar

Lauraitis, A., Maskeliūnas, R., Damaševičius, R. and Krilavičius, T. (2020). A mobile application for smart computer-aided self-administered testing of cognition, speech, and motor impairment, Sensors 20(11), Article 3236.10.3390/s20113236730906132517223 Search in Google Scholar

Lin, P.-C., Chen, K.-H., Yang, B.-S. and Chen, Y.-J. (2018). A digital assessment system for evaluating kinetic tremor in essential tremor and Parkinson’s disease, BMC Neurology 18(1): 25.10.1186/s12883-018-1027-2584529629523097 Search in Google Scholar

Luciano, M.S., Wang, C., Ortega, R.A., Yu, Q., Boschung, S., Soto-Valencia, J., Bressman, S.B., Lipton, R.B., Pullman, S. and Saunders-Pullman, R. (2016). Digitized spiral drawing: A possible biomarker for early Parkinson’s disease, PLOS ONE 11(10): e0162799.10.1371/journal.pone.0162799506137227732597 Search in Google Scholar

Moetesum, M., Siddiqi, I., Vincent, N. and Cloppet, F. (2019). Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson’s disease, Pattern Recognition Letters 121: 19–27.10.1016/j.patrec.2018.04.008 Search in Google Scholar

Moshkova, A., Samorodov, A., Ivanova, E. and Fedotova, E. (2020). High accuracy discrimination of Parkinson’s disease from healthy controls by hand movements analysis using LeapMotion sensor and 1D convolutional neural network, Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, Yekaterinburg, Russia, pp. 0062–0065. Search in Google Scholar

Mucha, J., Mekyska, J., Galaz, Z., Faundez-Zanuy, M., de Ipina, K.L., Zvoncak, V., Kiska, T., Smekal, Z., Brabenec, L. and Rektorova, I. (2018). Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting, Applied Sciences 8(12): 2566.10.3390/app8122566 Search in Google Scholar

Nguyen, T., Park, E., Cui, X., Nguyen, V. and Kim, H. (2018). fPADnet: Small and efficient convolutional neural network for presentation attack detection, Sensors 18(8): 2532.10.3390/s18082532611173030072662 Search in Google Scholar

Oertel, W.H. (2017). Recent advances in treating Parkinson’s disease, F1000Research 6: 260.10.12688/f1000research.10100.1535703428357055 Search in Google Scholar

Oh, S.L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M. and Acharya, U.R. (2018). A deep learning approach for Parkinson’s disease diagnosis from EEG signals, Neural Computing and Applications 32(15): 10927–10933.10.1007/s00521-018-3689-5 Search in Google Scholar

Pereira, C.R., Pereira, D.R., da Silva, F.A., Hook, C., Weber, S.A., Pereira, L.A. and Papa, J.P. (2015). A step towards the automated diagnosis of Parkinson’s disease: Analyzing handwriting movements, IEEE 28th International Symposium on Computer-Based Medical Systems, Sao Carlos, Brazil, pp. 171–176. Search in Google Scholar

Pereira, C.R., Pereira, D.R., Rosa, G.H., Albuquerque, V.H., Weber, S.A., Hook, C. and Papa, J.P. (2018). Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson’s disease identification, Artificial Intelligence in Medicine 87: 67–77.10.1016/j.artmed.2018.04.00129673947 Search in Google Scholar

Pereira, C.R., Pereira, D.R., Silva, F.A., Masieiro, J.P., Weber, S.A.T., Hook, C. and Papa, J.P. (2016). A new computer vision-based approach to aid the diagnosis of Parkinson’s disease, Computer Methods and Programs in Biomedicine 136: 79–88.10.1016/j.cmpb.2016.08.00527686705 Search in Google Scholar

Poewe, W., Seppi, K., Tanner, C.M., Halliday, G.M., Brundin, P., Volkmann, J., Schrag, A.-E. and Lang, A.E. (2017). Parkinson disease, Nature Reviews Disease Primers 3(1), Article 17013.10.1038/nrdp.2017.1328332488 Search in Google Scholar

Priya, S.J., Rani, A.J., Subathra, M.S.P., Mohammed, M.A., Damaševičius, R. and Ubendran, N. (2021). Local pattern transformation based feature extraction for recognition of Parkinson’s disease based on gait signals, Diagnostics 11(8), Article 1395.10.3390/diagnostics11081395839151334441329 Search in Google Scholar

Raghavendra, U., Bhat, N.S., Gudigar, A. and Acharya, U.R. (2018). Automated system for the detection of thoracolumbar fractures using a CNN architecture, Future Generation Computer Systems 85: 184–189.10.1016/j.future.2018.03.023 Search in Google Scholar

Rosenblum, S., Samuel, M., Zlotnik, S., Erikh, I. and Schlesinger, I. (2013). Handwriting as an objective tool for Parkinson’s disease diagnosis, Journal of Neurology 260(9): 2357–2361.10.1007/s00415-013-6996-x23771509 Search in Google Scholar

Saunders-Pullman, R., Derby, C., Stanley, K., Floyd, A., Bressman, S., Lipton, R.B., Deligtisch, A., Severt, L., Yu, Q., Kurtis, M. and Pullman, S.L. (2008). Validity of spiral analysis in early Parkinson’s disease, Movement Disorders 23(4): 531–537.10.1002/mds.2187418074362 Search in Google Scholar

Savitt, J.M. (2006). Diagnosis and treatment of Parkinson disease: Molecules to medicine, Journal of Clinical Investigation 116(7): 1744–1754.10.1172/JCI29178148317816823471 Search in Google Scholar

Shaban, M. (2020). Deep convolutional neural network for Parkinson’s disease based handwriting screening, IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, USA, pp. 1–4. Search in Google Scholar

Sivaranjini, S. and Sujatha, C.M. (2019). Deep learning based diagnosis of Parkinson’s disease using convolutional neural network, Multimedia Tools and Applications 79(21–22): 15467–15479.10.1007/s11042-019-7469-8 Search in Google Scholar

Stefano, C.D., Fontanella, F., Impedovo, D., Pirlo, G. and di Freca, A.S. (2019). Handwriting analysis to support neurodegenerative diseases diagnosis: A review, Pattern Recognition Letters 121: 37–45.10.1016/j.patrec.2018.05.013 Search in Google Scholar

Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B. and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Transactions on Medical Imaging 35(5): 1299–1312.10.1109/TMI.2016.253530226978662 Search in Google Scholar

Trist, B.G., Hare, D.J. and Double, K.L. (2019). Oxidative stress in the aging substantia nigra and the etiology of Parkinson’s disease, Aging Cell 18(6), Article e13031.10.1111/acel.13031682616031432604 Search in Google Scholar

Tseng, M.H. and Cermak, S.A. (1993). The influence of ergonomic factors and perceptual-motor abilities on handwriting performance, American Journal of Occupational Therapy 47(10): 919–926.10.5014/ajot.47.10.9198109612 Search in Google Scholar

Tysnes, O.-B. and Storstein, A. (2017). Epidemiology of Parkinson’s disease, Journal of Neural Transmission 124(8): 901–905.10.1007/s00702-017-1686-y28150045 Search in Google Scholar

van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-SNE, Journal of Machine Learning Research 9(86): 2579–2605. Search in Google Scholar

Wang, H., Zheng, B., Yoon, S.W. and Ko, H.S. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis, European Journal of Operational Research 267(2): 687–699.10.1016/j.ejor.2017.12.001 Search in Google Scholar

Wang, M. and Chen, H. (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis, Applied Soft Computing 88: 105946.10.1016/j.asoc.2019.105946 Search in Google Scholar

Zham, P., Arjunan, S.P., Raghav, S. and Kumar, D.K. (2018). Efficacy of guided spiral drawing in the classification of Parkinson’s disease, IEEE Journal of Biomedical and Health Informatics 22(5): 1648–1652.10.1109/JBHI.2017.276200829028217 Search in Google Scholar

Zham, P., Kumar, D., Dabnichki, P., Arjunan, S.P. and Raghav, S. (2017). Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral, Frontiers in Neurology 8, Article 435.10.3389/fneur.2017.00435559274128932206 Search in Google Scholar

Zhang, T., Zhang, Y., Cao, Y., n Li and Hao, L. (2020). Diagnosing Parkinson’s disease with speech signal based on convolutional neural network, International Journal of Computer Applications in Technology 63(4): 348.10.1504/IJCAT.2020.10032598 Search in Google Scholar

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