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NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern

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Abstract

Neurodegenerative diseases damage neuromuscular tissues and deteriorate motor neurons which affects the motor capacity of the patient. Particularly the walking gait is greatly influenced by the deterioration process. Early detection of anomalous gait patterns caused by neurodegenerative diseases can help the patient to prevent associated risks. Previous studies in this domain relied on either features extracted from gait parameters or the Ground Reaction Force (GRF) signal. In this work, we aim to combine both GRF signals and extracted features to provide a better analysis of walking gait patterns. For this, we designed NDDNet, a novel neural network architecture to process both of these data simultaneously to detect 3 different Neurodegenerative Diseases (NDDs). We have done several experiments on the data collected from 64 participants and got 96.75% accuracy on average in detecting 3 types of NDDs. The proposed method might provide a way to get the most out of the data in hand while working with GRF signals and help diagnose patients with an anomalous gait more effectively.

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Notes

  1. The source code is available at https://github.com/atick-faisal/NDDNet.

References

  1. Brown RC, Lockwood AH, Sonawane BR (2005) Neurodegenerative diseases: an overview of environmental risk factors. Environ Health Perspect 113(9):1250–1256. https://doi.org/10.1289/EHP.7567

    Article  Google Scholar 

  2. Feigin VL, Vos T, Nichols E, Owolabi MO, Carroll WM, Dichgans M, Deuschl G, Parmar P, Brainin M, Murray C (2020) The global burden of neurological disorders: translating evidence into policy. Lancet Neurol 19(3):255–265. https://doi.org/10.1016/S1474-4422(19)30411-9

    Article  Google Scholar 

  3. Hausdorff JM, Peng CK, Ladin Z, Wei JY, Goldberger AL (1995) Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. J Appl Physiol 78(1):349–358. https://doi.org/10.1152/JAPPL.1995.78.1.349

    Article  Google Scholar 

  4. Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL (1998) Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in parkinson’s disease and Huntington’s disease. Mov Disord 13(3):428–437. https://doi.org/10.1002/MDS.870130310

    Article  Google Scholar 

  5. Frechette ML, Meyer BM, Tulipani LJ, Gurchiek RD, McGinnis RS, Sosnoff JJ (2019) Next steps in wearable technology and community ambulation in multiple sclerosis. Curr Neurol Neurosci Rep 19(10):1–10. https://doi.org/10.1007/S11910-019-0997-9

    Article  Google Scholar 

  6. Tortelli R, Rodrigues FB, Wild EJ (2021) The use of wearable/portable digital sensors in Huntington’s disease: a systematic review. Parkinsonism Relat Disord 83:93–104. https://doi.org/10.1016/J.PARKRELDIS.2021.01.006

    Article  Google Scholar 

  7. Kourtis LC, Regele OB, Wright JM, Jones GB (2019) Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. NPJ Digit Med 2(1):1–9. https://doi.org/10.1038/s41746-019-0084-2

    Article  Google Scholar 

  8. Mirelman A, Bonato P, Camicioli R, Ellis TD, Giladi N, Hamilton JL, Hass CJ, Hausdorff JM, Pelosin E, Almeida QJ (2019) Gait impairments in Parkinson’s disease. Lancet Neurol 18(7):697–708. https://doi.org/10.1016/S1474-4422(19)30044-4

    Article  Google Scholar 

  9. Alaskar H, Hussain AJ, Khan W, Tawfik H, Trevorrow P, Liatsis P, Sbaï Z (2020) A data science approach for reliable classification of neuro-degenerative diseases using gait patterns. J Reliab Intell Environ 6(4):233–247. https://doi.org/10.1007/S40860-020-00114-1

    Article  Google Scholar 

  10. The Unified Parkinson’s Disease Rating Scale (UPDRS): Status and recommendations - - 2003 - Movement Disorders - Wiley Online Library. https://movementdisorders.onlinelibrary.wiley.com/doi/full/10.1002/mds.10473?casa_token=4DT-EQYNO-0AAAAA%3ACQDK5-3eUdXAdS_4LMXO9UdPLIA6uomxp_KFeYwpV1PJM56hxR0J318NRodaIJ6I9nDKEuZHNRxwzMKM. Accessed 22 Oct 2022

  11. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt P, Nyenhuis D et al (2008) Movement Disorder Society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23(15):2129–2170. https://doi.org/10.1002/mds.22340

    Article  Google Scholar 

  12. di Biase L et al (2020) Gait analysis in Parkinson’s disease: an overview of the most accurate markers for diagnosis and symptoms monitoring. Sensors (Basel) 20(12):E3529. https://doi.org/10.3390/s20123529

    Article  Google Scholar 

  13. (1996) Unified Huntington’s Disease Rating Scale: reliability and consistency. Huntington Study Group. Mov Disord 11(2):136–142. https://doi.org/10.1002/mds.870110204

  14. Gaßner H, Jensen D, Marxreiter F, Kletsch A, Bohlen S, Schubert R, Muratori LM, Eskofier B, Klucken J, Winkler J, Reilmann R, Kohl Z (2020) Gait variability as digital biomarker of disease severity in Huntington’s disease. J Neurol 267(6):1594–1601. https://doi.org/10.1007/s00415-020-09725-3

    Article  Google Scholar 

  15. Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AL, Kaliton D, Goldberger AL (2000) Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J Appl Physiol (1985) 88(6):2045–2053. https://doi.org/10.1152/jappl.2000.88.6.2045

    Article  Google Scholar 

  16. Hausdorff JM (2009) Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos 19(2):026113. https://doi.org/10.1063/1.3147408

    Article  MathSciNet  Google Scholar 

  17. Stergiou N, Decker LM (2011) Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 30(5):869–888. https://doi.org/10.1016/J.HUMOV.2011.06.002

    Article  Google Scholar 

  18. Hausdorff JM (2007) Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. Hum Mov Sci 26(4):555–589. https://doi.org/10.1016/J.HUMOV.2007.05.003

    Article  Google Scholar 

  19. Vajiha Begum SA, Pushpa Rani M (2020) Recognition of neurodegenerative diseases with gait patterns using double feature extraction methods. Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020, pp 332–338. https://doi.org/10.1109/ICICCS48265.2020.9120920

  20. Selzler R, Green JR, Goubran R (2018) Neurodegenerative disease prediction based on gait analysis signals acquired with force-sensitive resistors. 2018 IEEE Life Sciences Conference, LSC 2018, pp 122–125. https://doi.org/10.1109/LSC.2018.8572063

  21. Yang M, Zheng H, Wang H, McClean S (2009) Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. In: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare, pp 1–7. https://doi.org/10.4108/ICST.PERVASIVEHEALTH2009.6053

  22. El Maachi I, Bilodeau G-A, Bouachir W (2020) Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Syst Appl 143:113075. https://doi.org/10.1016/j.eswa.2019.113075

    Article  Google Scholar 

  23. Paragliola G, Coronato A (2018) Gait anomaly detection of subjects with Parkinson’s disease using a deep time series-based approach. IEEE Access 6:73280–73292. https://doi.org/10.1109/ACCESS.2018.2882245

    Article  Google Scholar 

  24. Setiawan F, Lin C-W (2021) Identification of neurodegenerative diseases based on vertical ground reaction force classification using time–frequency spectrogram and deep learning neural network features. Brain Sci 11(7):Art. no. 7. https://doi.org/10.3390/brainsci11070902

    Article  Google Scholar 

  25. Pham TD (2018) Texture classification and visualization of time series of gait dynamics in patients with neuro-degenerative diseases. IEEE Trans Neural Syst Rehabil Eng 26(1):188–196. https://doi.org/10.1109/TNSRE.2017.2732448

    Article  Google Scholar 

  26. Erdaş ÇB, Sümer E, Kibaroğlu S (2021) Neurodegenerative disease detection and severity prediction using deep learning approaches. Biomed Signal Process Control 70:103069. https://doi.org/10.1016/j.bspc.2021.103069

    Article  Google Scholar 

  27. Trockman A, Kolter JZ (2022) Patches are all you need?. arXiv:2201.09792 [cs]. Accessed 20 Feb 2022. [Online]. Available: http://arxiv.org/abs/2201.09792

  28. Hausdorff JM (2005) Gait variability: methods, modeling and meaning. J Neuroeng Rehabil 2(1):19. https://doi.org/10.1186/1743-0003-2-19

    Article  Google Scholar 

  29. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):E215–E220. https://doi.org/10.1161/01.cir.101.23.e215

    Article  Google Scholar 

  30. Bhidayasiri R, Tarsy D (2012) Parkinson’s disease: Hoehn and Yahr scale. In: Bhidayasiri R, Tarsy D (eds) Movement disorders: a video atlas: a video atlas. Humana Press, Totowa, pp 4–5. https://doi.org/10.1007/978-1-60327-426-5_2

    Chapter  Google Scholar 

  31. Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis - PubMed. https://pubmed.ncbi.nlm.nih.gov/10846017/. Accessed 19 Feb 2022

  32. Kourtis LC, Regele OB, Wright JM, Jones GB (2019) Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. NPJ Digital Med 2(1):Art. no. 1. https://doi.org/10.1038/s41746-019-0084-2

    Article  Google Scholar 

  33. Hausdorff JM, Ladin Z, Wei JY (1995) Footswitch system for measurement of the temporal parameters of gait. J Biomech 28(3):347–351. https://doi.org/10.1016/0021-9290(94)00074-E

    Article  Google Scholar 

  34. Faisal MAA, Abir FF, Ahmed MU (2021) Sensor Dataglove for real-time static and dynamic hand gesture recognition. In: 2021 Joint 10th international conference on informatics, electronics vision (ICIEV) and 2021 5th international conference on imaging, vision pattern recognition (icIVPR), pp 1–7. https://doi.org/10.1109/ICIEVicIVPR52578.2021.9564226

  35. Siraj MS et al (2020) UPIC: user and position independent classical approach for locomotion and transportation modes recognition. In: Adjunct proceedings of the 2020 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2020 ACM international symposium on wearable computers, New York, NY, USA, pp 340–345. https://doi.org/10.1145/3410530.3414343

  36. Faisal MdAA, Siraj MdS, Abdullah MdT, Shahid O, Abir FF, Ahad MAR (2020) A pragmatic signal processing approach for nurse care activity recognition using classical machine learning. In: Adjunct proceedings of the 2020 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2020 ACM international symposium on wearable computers, New York, NY, USA, pp 396–401. https://doi.org/10.1145/3410530.3414337

  37. Faisal MAA et al (2022) An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning. Comput Biol Med 142:105184. https://doi.org/10.1016/j.compbiomed.2021.105184

    Article  Google Scholar 

  38. Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, Abbas TO, Alam MF, Kashem SB, Islam MT, Zughaier SM, Chowdhury MEH (2022) QCovSML: a reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model. Comput Biol Med 143:105284. https://doi.org/10.1016/j.compbiomed.2022.105284

    Article  Google Scholar 

  39. Xia Y, Gao Q, Lu Y, Ye Q (2016) A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis. Med Biol Eng Comput 54(9):1399–1408. https://doi.org/10.1007/s11517-015-1413-5

    Article  Google Scholar 

  40. Nam Nguyen QD, Liu A-B, Lin C-W (2020) Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers. Entropy 22(12):Art. no. 12. https://doi.org/10.3390/e22121340

    Article  Google Scholar 

  41. Schreiber C, Moissenet F (2019) A multimodal dataset of human gait at different walking speeds established on injury-free adult participants. Sci Data 6(1):Art. no. 1. https://doi.org/10.1038/s41597-019-0124-4

    Article  Google Scholar 

  42. Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds | Scientific Data. https://www.nature.com/articles/s41597-021-00881-3. Accessed 20 Feb 2022

  43. Elden RH, Ghoneim VF, Al-Atabany W (2018) A computer aided diagnosis system for the early detection of neurodegenerative diseases using linear and non-linear analysis. In: 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), pp 116–121. https://doi.org/10.1109/MECBME.2018.8402417

  44. Joshi D, Khajuria A, Joshi P (2017) An automatic non-invasive method for Parkinson’s disease classification. Comput Methods Prog Biomed 145:135–145. https://doi.org/10.1016/j.cmpb.2017.04.007

    Article  Google Scholar 

  45. Qi CR, Su H, Mo K, Guibas LJ (2017) PointNet: deep learning on point sets for 3d classification and segmentation. arXiv:1612.00593 [cs]. Accessed 20 Feb 2022. [Online]. Available: http://arxiv.org/abs/1612.00593

  46. Vaswani A et al (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30. Accessed 05 June 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

  47. Hendrycks D, Gimpel K (2020) Gaussian Error Linear Units (GELUs). arXiv:1606.08415 [cs]. Accessed: 20 Feb 2022. [Online]. Available: http://arxiv.org/abs/1606.08415

  48. Abadi M et al (2015) TensorFlow: large-scale machine learning on heterogeneous distributed systems. Accessed: 22 Oct 2022. [Online]. Available: http://download.tensorflow.org/paper/whitepaper2015.pdf

  49. Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. arXiv:1412.6980 [cs]. Accessed 20 Feb 2022. [Online]. Available: http://arxiv.org/abs/1412.6980

  50. Altman DG, Bland JM (1994) Statistics notes: diagnostic tests 1: sensitivity and specificity. BMJ 308(6943):1552. https://doi.org/10.1136/bmj.308.6943.1552

    Article  Google Scholar 

  51. Paragliola G, Coronato A (2021) A deep learning-based approach for the classification of gait dynamics in subjects with a neurodegenerative disease. In: Intelligent systems and applications, Cham, pp 452–468. https://doi.org/10.1007/978-3-030-55190-2_34

  52. Felix JP, do Nascimento HAD, Guimarães NN, Pires EDO, Vieira G da S, Alencar W de S (2020) An Effective and automatic method to aid the diagnosis of amyotrophic lateral sclerosis using one minute of gait signal, presented at the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 2745–2751. https://doi.org/10.1109/BIBM49941.2020.9313308

  53. Félix JP et al (2019) A Parkinson’s disease classification method: an approach using gait dynamics and detrended fluctuation analysis. In: 2019 IEEE Canadian conference of electrical and computer engineering (CCECE), pp 1–4. https://doi.org/10.1109/CCECE.2019.8861759

  54. Ye Q, Xia Y, Yao Z (2018) Classification of gait patterns in patients with neurodegenerative disease using adaptive neuro-fuzzy inference system. Comput Math Methods Med 2018:e9831252. https://doi.org/10.1155/2018/9831252

    Article  MATH  Google Scholar 

  55. Ren P, Tang S, Fang F, Luo L, Xu L, Bringas-Vega ML, Yao D, Kendrick KM, Valdes-Sosa PA (2017) Gait rhythm fluctuation analysis for neurodegenerative diseases by empirical mode decomposition. IEEE Trans Biomed Eng 64(1):52–60. https://doi.org/10.1109/TBME.2016.2536438

    Article  Google Scholar 

  56. Ren P, Zhao W, Zhao Z, Bringas-Vega ML, Valdes-Sosa PA, Kendrick KM (2016) Analysis of gait rhythm fluctuations for neurodegenerative diseases by phase synchronization and conditional entropy. IEEE Trans Neural Syst Rehabil Eng 24(2):291–299. https://doi.org/10.1109/TNSRE.2015.2477325

    Article  Google Scholar 

  57. Daliri MR (2012) Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement 45(7):1729–1734. https://doi.org/10.1016/j.measurement.2012.04.013

    Article  Google Scholar 

  58. Refaeilzadeh P, Tang L, Liu H (2009) Cross-validation. In: LIU L, ÖZSU MT (eds) Encyclopedia of database systems. Springer US, Boston, pp 532–538. https://doi.org/10.1007/978-0-387-39940-9_565

    Chapter  Google Scholar 

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Acknowledgments

This work was supported in part by the Qatar National Research Fund under Grant NPRP12S-0227-190164 and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021- 001. The statements made herein are solely the responsibility of the authors. This open-access publication is supported by Qatar National Library.

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Correspondence to Muhammad E. H. Chowdhury, Shona Pedersen or Mosabber Uddin Ahmed.

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Faisal, M.A.A., Chowdhury, M.E.H., Mahbub, Z.B. et al. NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. Appl Intell 53, 20034–20046 (2023). https://doi.org/10.1007/s10489-023-04557-w

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