Abstract
Parkinson’s disease (PD) is a complex disorder characterized by several motor and non-motor symptoms that worsen over time, and that differ from person to another. In the early stages, when the symptoms are often incomplete, the diagnosis becomes difficult and at times, the subject may remain undiagnosed. This difficulty is a strong motivation for computer-based assessment tools that can aid in the early diagnosing and predicting the progression of PD. Handwriting’s deterioration, vocal and eye movement impairments may be ones of the earliest indicators for the onset of the illness. A language independent model to detect PD at early stages by using multimodal signals has not been enough addressed. Due to the lack of multimodal and multilingual databases, database which includes online handwriting, speech signals, and eye movement’s recordings have been recently collected. After succeeding in building language independent models for PD early diagnosis using pure handwriting or speech, we propose in this work language independent models based on bimodal analyses (handwriting and speech), where both SVM and deep learning models are studied. Our experiments show that classification accuracy up to 100% can be obtained by our SVM model through handwriting/speech bimodal analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Drotar, P., Mekyska, J., Rektorova, I., Masarova, L., Smekal, Z., Faundez-Zanuy, M.: Decision support framework for Parkinsons disease based on novel handwriting markers. IEEE Trans. Neural Syst. Rehabil. Eng. 1 (2015). https://doi.org/10.1109/tnsre.2014.2359997
Weiner, W.J., Shulman, L.M., Lang, A.E.: Parkinsons Disease: A Complete Guide for Patients and Families. Johns Hopkins University Press, Baltimore (2013)
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)
Impedovo, D., Pirlo, G., Vessio, G.: Dynamic handwriting analysis for supporting earlier Parkinson’s disease diagnosis. Information 9, 247 (2018)
Taleb, C., Likforman-Sulem, L., Khachab, M., Mokbel, C: Feature selection for an improved Parkinson’s disease identification based on handwriting. In: 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), Nancy, France (2017)
Taleb, C., Likforman-Sulem, L., Mokbel, C., Khachab, M.: Detection of Parkinson’s disease from handwriting using deep learning: a comparative study. Evol. Intell. (2020). https://doi.org/10.1007/s12065-020-00470-0
Orozco-Arroyave, J.R., et al.: Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases. IEEE J. Biomed. Health Inform. 19, 1820–1828 (2015)
Duffy, J.: Motor speech disorders: clues to neurologic diagnosis. In: Parkinson’s Disease and Movement Disorders: Diagnosis and Treatment Guidelines for the Practicing Physician, pp. 35–53 (2000). https://doi.org/10.1016/j.wocn.2017.01.009
Pinto, S., Chan, A., Guimarães, I., Rothe-Neves, R., Sadat, J.: A cross-linguistic perspective to the study of dysarthria in Parkinson’s disease. J. Phon. 64, 156–167 (2017)
Shalash, A.S., et al.: Non-motor symptoms as predictors of quality of life in Egyptian patients with Parkinson’s disease: a cross-sectional study using a culturally adapted 39-item Parkinson’s disease questionnaire. Front. Neurol. 9, 357 (2018). https://doi.org/10.3389/fneur.2018.00357
Khan, T.: Running-speech MFCC are better markers of Parkinsonian speech deficits than vowel phonation and diadochokinetic (2014). http://www.diva-portal.org/smash/record.jsf?pid=diva2:705196. Accessed 10 May 2021
Mazuel, L., et al.: Proton MR spectroscopy for diagnosis and evaluation of treatment efficacy in Parkinson disease. Radiology 278, 505–513 (2016)
Dumas, B., Lalanne, D., Oviatt, S.: Multimodal interfaces: a survey of principles, models and frameworks. In: Lalanne, D., Kohlas, J. (eds.) Human Machine Interaction. LNCS, vol. 5440, pp. 3–26. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00437-7_1
Rusz, J., et al.: Imprecise vowel articulation as a potential early marker of Parkinson’s disease: effect of speaking task. J. Acoust. Soc. Am. 134, 2171–2181 (2013). https://doi.org/10.1121/1.4816541
Dai, W., Dai, C., Qu, S., Li, J., Das, S.: Very deep convolutional neural networks for raw waveforms. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA (2017)
Lonce, W.: Audio spectrogram representations for processing with convolutional neural networks. In: Proceedings of the 1st International Workshop on Deep Learning for Music, Anchorage, AK, USA (2017)
Pompili, A., et al.: Automatic detection of Parkinson’s disease: an experimental analysis of common speech production tasks used for diagnosis. In: Ekštein, K., Matoušek, V. (eds.) TSD 2017. LNCS (LNAI), vol. 10415, pp. 411–419. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64206-2_46
Taleb, C.: Parkinson’s disease detection by multimodal analysis combining handwriting and speech signals (Unpublished doctoral dissertation). Telecom Paris, France (2020)
Jeancolas, L., et al.: Automatic detection of early stages of Parkinsons disease through acoustic voice analysis with mel-frequency cepstral coefficients. In: 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (2017). https://doi.org/10.1109/atsip.2017.8075567
Sharma, R.K., Gupta, A.K.: Voice analysis for telediagnosis of Parkinson disease using artificial neural networks and support vector machines. Int. J. Intell. Syst. Appl. 7, 41–47 (2015)
Little, M., Mcsharry, P., Hunter, E., Spielman, J., Ramig, L.: Suitability of dysphonia measurements for telemonitoring of Parkinsons disease. IEEE Trans. Biomed. Eng. 56, 1015–1022 (2009)
Rosenblum, S., Samuel, M., Zlotnik, I., Schlesinger, I.: Handwriting as an objective tool for Parkinson’s disease diagnosis. J. Neurol. 260, 2357–2361 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Taleb, C., Likforman-Sulem, L., Mokbel, C. (2021). Language-Independent Bimodal System for Early Parkinson’s Disease Detection. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-86334-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86333-3
Online ISBN: 978-3-030-86334-0
eBook Packages: Computer ScienceComputer Science (R0)