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A dual-branch model for diagnosis of Parkinson’s disease based on the independent and joint features of the left and right gait

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Abstract

Clinical diagnosis of Parkingson’s disease (PD) requires the physician to assess the patient’s gait and other symptoms. A dual-branch model is proposed in this paper as an objective diagnostic tool to diagnose PD automatically. In this research, the joint features and independent features of left and right gait are fused innovatively. Convolutional neural network (CNN) and long short-term memory network (LSTM) are used to extract the spatial and temporal characteristics of sensors respectively. After the independent features extracted from the branches are collapsed, LSTM is used to incorporate the joint features between the left and right gait. Compared with other methods, the proposed model can learn the correlation between the two feet and extract higher discriminative features to effectively improve the accuracy of Parkinson detection. The model shows the state-of-the-art performance for the public dataset, with the accuracy, sensitivity, and specificity being 99.22%, 100%, and 98.04%, respectively. A simple, fast, and objective method proposed in this paper was believed to improve diagnostic performance.

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Funding

This work was supported by the Project of Supporting Plan of Tianjin (China) (16-YFZCSY00850).

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Authors and Affiliations

  1. School of Microelectronics, Tianjin University, Tianjin International Joint Research Center for Internet of Things, Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin, 300072, China

    Xu Liu, Wang Li, Zheng Liu, Feixiang Du & Qiang Zou

Authors
  1. Xu Liu
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  2. Wang Li
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  3. Zheng Liu
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  4. Feixiang Du
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  5. Qiang Zou
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Contributions

Xu Liu: Formal Analysis, Methodology, Data curation, Writing- Original draft. Wang Li: Logical combing, Theoretical analysis. Zheng Liu: Resources. Feixiang Du: Software. Qiang Zou: Conceptualization, Supervision, Writing- Reviewing & Editing.

Corresponding author

Correspondence to Qiang Zou.

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Conflict of interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.

Availability of data and material

The datasets used during the current study are available in the PhysioNet.

Code availability

According to the introduction of the paper, the model can be easily reproduced through TensorFlow library. If necessary, please contact the corresponding author to explain the purpose . If the reason is right, we will reply to the download link of code.

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Liu, X., Li, W., Liu, Z. et al. A dual-branch model for diagnosis of Parkinson’s disease based on the independent and joint features of the left and right gait. Appl Intell 51, 7221–7232 (2021). https://doi.org/10.1007/s10489-020-02182-5

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  • DOI: https://doi.org/10.1007/s10489-020-02182-5

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