Abstract
Early detection of degenerative diseases is crucial in medical science. Disease like Parkinson’s Disease is a neurological disorder affecting the brain control over the limbs and in turn affecting motor skills. Parkinson patients suffer from tremors and low dopamine levels resulting in anxiety and depression. Thus, an early diagnosis for such diseases become vital. But in many of the cases, by the time a clear diagnosis can be made, the disease has been progressed significantly. The most common and early symptom among all is the loss of motor control. Thus, the progression in loss of motor control can be easily detected using a computer aided system for quicker diagnosis. The proposed CNN model with an accuracy 84 for cross fold of 40% for the kinematics detection of a person with Parkinson in early stage using images and signals collected by a smart pen and tablet.
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References
Diaz, M., Ferrer, M.A., Impedovo, D., Pirlo, G., Vessio, G.: Dynamically enhanced static handwriting representation for Parkinson’s disease detection. Pattern Recogn. Lett. 128, 204–210 (2019)
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Data augmentation using synthetic data for time series classification with deep residual networks. arXiv preprint arXiv:1808.02455 (2018)
Gallicchio, C., Micheli, A.: Deep tree echo state networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Huzaifah, M.: Comparison of time-frequency representations for environmental sound classification using convolutional neural networks. arXiv preprint arXiv:1706.07156 (2017)
Júnior, E.P., et al.: Intelligent sensory pen for aiding in the diagnosis of Parkinson’s disease from dynamic handwriting analysis. Sensors 20(20), 5840 (2020)
Khan, N.A., Jafri, M.N., Qazi, S.A.: Improved resolution short time Fourier transform. In: 2011 7th International Conference on Emerging Technologies, pp. 1–3. IEEE (2011)
Khatamino, P., Cantürk, İ., Özyılmaz, L.: A deep learning-CNN based system for medical diagnosis: an application on Parkinson’s disease handwriting drawings. In: 2018 6th International Conference on Control Engineering & Information Technology (CEIT), pp. 1–6. IEEE (2018)
Moetesum, M., Siddiqi, I., Vincent, N., Cloppet, F.: Assessing visual attributes of handwriting for prediction of neurological disorders—a case study on Parkinson’s disease. Pattern Recogn. Lett. 121, 19–27 (2019)
Mormont, R., Geurts, P., Marée, R.: Comparison of deep transfer learning strategies for digital pathology. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2262–2271 (2018)
Nilashi, M., Ibrahim, O., Ahani, A.: Accuracy improvement for predicting Parkinson’s disease progression. Sci. Rep. 6(1), 1–18 (2016)
Nisar, S., Khan, O.U., Tariq, M.: An efficient adaptive window size selection method for improving spectrogram visualization. Comput. Intell. Neurosci. 2016 (2016)
Pereira, C.R., et al.: Handwritten dynamics assessment through convolutional neural networks: an application to Parkinson’s disease identification. Artif. Intell. Med. 87, 67–77 (2018)
Pereira, C.R., Weber, S.A., Hook, C., Rosa, G.H., Papa, J.P.: Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 340–346. IEEE (2016)
Rizzo, G., Copetti, M., Arcuti, S., Martino, D., Fontana, A., Logroscino, G.: Accuracy of clinical diagnosis of Parkinson disease: a systematic review and metanalysis. Neurology 86(6), 566–576 (2016)
Sharma, R., Gupta, A.K., et al.: Voice analysis for telediagnosis of Parkinson disease using artificial neural networks and support vector machines. Int. J. Intell. Syst. Appl. 7(6), 41 (2015)
Taleb, C., Likforman-Sulem, L., Mokbel, C., Khachab, M.: Detection of Parkinson’s disease from handwriting using deep learning: a comparative study. Evol. Intell. 1–12 (2020)
Tysnes, O.B., Storstein, A.: Epidemiology of Parkinson’s disease. J. Neural Transm. 124(8), 901–905 (2017)
Um, T.T., et al.: Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 216–220 (2017)
Vatsaraj, I., Nagare, G.: Early detection of Parkinson’s disease using contrast enhancement techniques and CNN. Int. J. Eng. Res. Technol. 10(5) (2021)
Wang, F., Zhong, S., Peng, J., Jiang, J., Liu, Y.: Data augmentation for EEGased emotion recognition with deep convolutional neural networks. In: Schoeffmann, K., Chalidabhongse, T.H., Ngo, C.W., Aramvith, S., O’Connor, N.E., Ho, Y.-S. (eds.) MMM 2018. LNCS, vol. 10705, pp. 82–93. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73600-6_8
Wen, Q., et al.: Time series data augmentation for deep learning: a survey. arXiv preprint arXiv:2002.12478 (2020)
Zham, P., et al.: A kinematic study of progressive micrographia in Parkinson’s disease. Front. Neurol. 10, 403 (2019)
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Savalia, J., Desai, S., Geddam, R., Shah, P., Chhikaniwala, H. (2022). Early-Stage Detection Model Using Deep Learning Algorithms for Parkinson’s Disease Based on Handwriting Patterns. In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1759. Springer, Cham. https://doi.org/10.1007/978-3-031-23092-9_26
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