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A hybrid model based on bidirectional long-short term memory and support vector machine for rest tremor classification

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Abstract

Parkinson’s disease is a neurodegenerative disease, where tremor is the main symptom. Deep brain stimulation can help manage a broad range of neurological ailments like Parkinson’s disease. It involves electrical impulses delivered to specific targets in the brain to alter or modulate neural functioning. Our purpose in this study was to adopt deep learning methodologies to classify resting tremors. A novel approach for resting tremor classification in patients with Parkinson’s disease using a hybrid model based on bidirectional long-short term memory and support vector machine was proposed to achieve this purpose. The proposed hybrid model combines the key properties of both classifiers. Specifically, this research exploited the efficiency of the bidirectional long short-term memory layers to identify short-term and long-term dependencies in both forward and backward directions. In addition, the support vector machine was used as a binary classifier to obtain a new effectual classification model inspired by the two formalisms for rest tremor classification. In our experiment, we adopted the 10-fold cross-validation method to ensure the reliability of the experimental results. The performed experiments proved that our proposed approach outperforms the best results achieved by other state-of-the-art methods.

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Authors and Affiliations

  1. National Engineering School of Sfax, University of Sfax, 1173, Sfax, BP, Tunisia

    Jihen Fourati

  2. Research Lab: Technology, Energy, and Innovative Materials Lab, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia

    Jihen Fourati

  3. Faculty of Sciences of Gafsa, University of Gafsa, 2100, Gafsa, BP, Tunisia

    Mohamed Othmani

  4. Department of Applied Natural Sciences, Applied College, Qassim University, Buraydah, Saudi Arabia

    Mohamed Othmani

  5. Faculty of Sciences and Techniques of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia

    Hela Ltifi

  6. Research Groups in Intelligent Machines Lab, 3038, Sfax, BP, Tunisia

    Hela Ltifi

Authors
  1. Jihen Fourati
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  2. Mohamed Othmani
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  3. Hela Ltifi
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Correspondence to Jihen Fourati.

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Fourati, J., Othmani, M. & Ltifi, H. A hybrid model based on bidirectional long-short term memory and support vector machine for rest tremor classification. SIViP 16, 2175–2182 (2022). https://doi.org/10.1007/s11760-022-02180-9

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  • DOI: https://doi.org/10.1007/s11760-022-02180-9

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