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Pose-Based Tremor Classification for Parkinson’s Disease Diagnosis from Video

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13434))

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Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience rather than a definite medical test, and the diagnostic accuracy is only about 73–84% since it is challenged by the subjective opinions or experiences of different medical experts. Therefore, an efficient and interpretable automatic PD diagnosis system is valuable for supporting clinicians with more robust diagnostic decision-making. To this end, we propose to classify Parkinson’s tremor since it is one of the most predominant symptoms of PD with strong generalizability. Different from other computer-aided time and resource-consuming Parkinson’s Tremor (PT) classification systems that rely on wearable sensors, we propose SPAPNet, which only requires consumer-grade non-intrusive video recording of camera-facing human movements as input to provide undiagnosed patients with low-cost PT classification results as a PD warning sign. For the first time, we propose to use a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture to extract relevant PT information and filter the noise efficiently. This design aids in improving both classification performance and system interpretability. Experimental results show that our system outperforms state-of-the-arts by achieving a balanced accuracy of 90.9% and an F1-score of 90.6% in classifying PT with the non-PT class.

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Change history

  • 28 March 2024

    A correction has been published.

References

  1. Alle, S., Priyakumar, U.D.: Linear prediction residual for efficient diagnosis of Parkinson’s disease from gait. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 614–623. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_59

    Chapter  Google Scholar 

  2. Bhat, S., Acharya, U.R., Hagiwara, Y., Dadmehr, N., Adeli, H.: Parkinson’s disease: cause factors, measurable indicators, and early diagnosis. In: Computers in Biology and Medicine, vol. 102, pp. 234–241. (2018)

    Google Scholar 

  3. Beitz, J. M.: Parkinson’s disease: a review. Front. Biosci. (Schol. Ed.). 6, 65–74. (2014)

    Google Scholar 

  4. Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. arXiv e-prints, arXiv:1812.08008 (2018)

  5. Chen, C., Ramanan, D.: 3D human pose estimation = 2D pose estimation + matching. In: the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7035–7043 (2017)

    Google Scholar 

  6. Ci, H., Ma, X., Wang C., Wang, Y.: Locally connected network for monocular 3D human pose estimation. In: IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1429–1442 (2022)

    Google Scholar 

  7. Vásquez-Correa, J.C., Arias-Vergara, T., Orozco-Arroyave, J.R., Eskofier, B., Klucken, J., Nöth, E.: Multimodal assessment of Parkinson’s disease: a deep learning approach. IEEE J. Biomed. Health Inform. 23(4), 1618–1630 (2019)

    Google Scholar 

  8. Li, S., Gao, Z., Lin, H.: LookHOPs: light multi-order convolution and pooling for graph classification. arXiv preprint arXiv:2012.15741 (2020)

  9. Fahn, S.: Description of Parkinson’s disease as a clinical syndrome. Ann. N. Y. Acad. Sci. 991, 1–14 (2003)

    Google Scholar 

  10. Gibb, W.R., Lees, A.J.: The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease. In: J. Neurol. Neurosurg. Psychiatry 51, 745–52 (1988)

    Google Scholar 

  11. Hausdorff J.M.: Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos (Woodbury, N.Y.) 19(2), 026113 (2009)

    Google Scholar 

  12. Hssayeni, M.D., Jimenez-Shahed, J., Burack, M.A., Ghoraani, B.: Wearable sensors for estimation of Parkinsonian tremor severity during free body movements. Sensors (Basel, Switzerland) 19(19), 4215 (2019)

    Google Scholar 

  13. Kipf, N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)

    Google Scholar 

  14. Patel, S., Lorincz, K., Hughes, R., et al.: Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 13(6), 864–873 (2009)

    Google Scholar 

  15. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: CVPR, pp. 2980–2988 (2017)

    Google Scholar 

  16. Lu, M., et al.: Vision-based estimation of MDS-UPDRS gait scores for assessing Parkinson’s disease motor severity. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 637–647. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59716-0_61

    Chapter  Google Scholar 

  17. Lu, M., Zhao, Q., Poston, K., Sullivan, L.,et al.: Quantifying Parkinson’s disease motor severity under uncertainty using MDS-UPDRS videos. Med. Image Anal. 73 (2021)

    Google Scholar 

  18. Luvizon, D.C., Picard, D., Tabia, H.: 2D/3D pose estimation and action recognition using multitask deep learning. In: the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5137–5146 (2018)

    Google Scholar 

  19. Massano, J., Bhatia, K.P.: Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management. Cold Spring Harbor Perspect. Med. 2(6), a008870 (2012)

    Google Scholar 

  20. Mhyre, T.R., Boyd, J.T., Hamill, R.W., Maguire-Zeiss, K.A.: Parkinson’s disease. Subcell. Biochem. 65, 389–455 (2012)

    Article  Google Scholar 

  21. Mostafa, S.A., et al.: Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease, In: Cognitive Systems Research, vol. 54, pp. 90–99 (2019)

    Google Scholar 

  22. Pasquini, J., et al.: Progression of tremor in early stages of Parkinson’s disease: a clinical and neuroimaging study. Brain 141(3), 811–821 (2018)

    Google Scholar 

  23. Pintea, S.L., Zheng, J., Li, X., Bank, P., van Hilten, J.J., van Gemert, J.C.: Hand-tremor frequency estimation in videos. In: ECCV Workshops, vol. 11134, no. 6, pp. 213–228 (2018)

    Google Scholar 

  24. Rizek, P., Kumar, N., Jog, M.S.: An update on the diagnosis and treatment of Parkinson disease. CMAJ: Can. Med. Assoc. J. 188(16), 1157–1165 (2016)

    Google Scholar 

  25. Rizzo, G., Copetti, M., Arcuti, S., Martino, D., Fontana, A., Logroscino, G.: Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 9; 86(6), 566–576 (2016)

    Google Scholar 

  26. Sveinbjornsdottir, S.: The clinical symptoms of Parkinson’s disease. In: J. Neurochem. 139, 318–324 (2016)

    Google Scholar 

  27. Wang, J., Yan, S., Xiong, Y., Lin, D.: Motion guided 3D pose estimation from videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 764–780. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_45

    Chapter  Google Scholar 

  28. Wang, X., Garg, S., Tran, S.N., Bai, Q., Alty, J.: Hand tremor detection in videos with cluttered background using neural network based approaches. Health Inf. Sci. Syst. 9(1), 1–14 (2021). https://doi.org/10.1007/s13755-021-00159-3

    Article  Google Scholar 

  29. Wang, W., Lee, J., Harrou, F., Sun, Y.: Early detection of Parkinson’s disease using deep learning and machine learning. IEEE Access 8, 147635–147646 (2020)

    Google Scholar 

  30. Wirdefeldt, K., Adami, H.O., Cole, P., Trichopoulos, D., Mandel, J.: Epidemiology and etiology of Parkinson’s disease: a review of the evidence. Eur. J. Epidemiol. 26(Suppl 1), S1–58 (2011)

    Google Scholar 

  31. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  32. Zhang, F., et al.: MediaPipe hands: on-device real-time hand tracking. arXiv preprint arXiv:2006.10214 (2020)

  33. Zhang, L., Wang, M., Liu, M., Zhang, D.: A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci. 14 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Durham University, Durham, UK

    Haozheng Zhang, Francis Xiatian Zhang & Hubert P. H. Shum

  2. University of Glasgow, Glasgow, UK

    Edmond S. L. Ho

Authors
  1. Haozheng Zhang
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  2. Edmond S. L. Ho
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  3. Francis Xiatian Zhang
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  4. Hubert P. H. Shum
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Corresponding author

Correspondence to Hubert P. H. Shum .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Zhang, H., Ho, E.S.L., Zhang, F.X., Shum, H.P.H. (2022). Pose-Based Tremor Classification for Parkinson’s Disease Diagnosis from Video. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_47

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  • DOI: https://doi.org/10.1007/978-3-031-16440-8_47

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