Abstract
Neuro-degenerative diseases (NDD) continue to increase globally and have significant impact on health, developmental and financial fronts. Recent studies have shown that gait impairment as one of the earliest signs of the disease. However, classification of multiple types of NDD becomes more challenging because of the high overlapping symptoms specifically at early stages. This paper entails a composite of signal processing and machine intelligence algorithms to process the gait data captured through multi-sensors for a reliable classification different types of NDD. The captured dataset used in this research consisted of 60 patients’ records representing three different types of NDD. Our simulation results indicated that the proposed approach outperformed existing works in this domain. The proposed work might help the mitigation plans for NDD, reliable monitoring of the disease progression and can assist the evaluation of possible therapy and treatments that would benefit the individuals, associated families, society and healthcare services.
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National Institute of Environmental Health Sciences (2017) Neurodegenerative Diseases. National Institute of Environmental Health Services [Online]. https://www.niehs.nih.gov/research/supported/health/neu rodegenerative/index.cfm. Accessed 05 Dec 2017
The EU Joint Programme—Neurodegenerative Disease Research (JPND). What is neurodegenerative disease?. https://www.neurodegenerationresearch.eu/. Accessed 1 June 2020
Office of Communications and Public Liaison (2018) Amyotrophic Lateral Sclerosis (ALS) Fact Sheet | National Institute of Neurological Disorders and Stroke. National Institute of Neurological Disorders and Stroke [Online]. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Amyotrophic-Lateral-Sclerosis-ALS-Fact-Sheet. Accessed 02 Jan 2018
E. NAQVI, “Parkinson’s Disease Statistics,” Parkinson’s News Today. https://parkinsonsnewstoday.com/parkinsons-disease-statistics/. Accessed 1 June 2020
Shahbakhi M, Far DT, Tahami E (2014) Speech analysis for diagnosis of Parkinson’s disease using genetic algorithm and support vector machine. J Biomed Sci Eng 7(4):147–156
Elkouzi A (2015) What is Parkinson’s? Parkinson’s Foundation [Online]. http://www.parkinson.org/understanding-parkinsons/what-is-parkinsons. Accessed 02 Jan 2018
Bhosale MPG, Patil ST (2013) Classification of EEG signals using wavelet transform and hybrid classifier for Parkinson’s disease detection. Int J Eng 2(1):106–112
Salvatore C et al (2014) Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy. J Neurosci Methods 222:230–237
Hass CJ, Buckley TA, Pitsikoulis C, Barthelemy EJ (2012) Progressive resistance training improves gait initiation in individuals with Parkinson’s disease. Gait Posture 35(4):669–673
McNeely ME, Earhart GM (2013) Medication and subthalamic nucleus deep brain stimulation similarly improve balance and complex gait in Parkinson disease. Parkinsonism Relat Disord 19(1):86–91
Picelli A et al (2012) Robot-assisted gait training in patients with Parkinson disease: a randomized controlled trial. Neurorehabil Neural Repair 26(4):353–361
Eskofier BM et al (2017) An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring. Appl Sci 7(10):986
Genetics Home (2018) Huntington disease. National library of Medicine [Online]. https://ghr.nlm.nih.gov/condition/huntington-disease. Accessed 03 Jan 2018
Kieburtz K et al (2001) Unified Huntington’s disease rating scale: reliability and consistency. Neurology 11(2):136–142
Long JD, Paulsen JS, Marder K, Zhang Y, Kim J-I, Mills JA (2014) Tracking motor impairments in the progression of Huntington’s disease. Mov Disord 29(3):311–319
Mannini A, Trojaniello D, Cereatti A, Sabatini AM (2016) A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and huntington’s disease patients. Sensors 16(1):134
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
Barnéoud P, Curet O (1999) Beneficial effects of lysine acetylsalicylate, a soluble salt of aspirin, on motor performance in a transgenic model of amyotrophic lateral sclerosis. Exp Neurol 155(2):243–251
Cho C-W, Chao W-H, Lin S-H, Chen Y-Y (2009) A vision-based analysis system for gait recognition in patients with Parkinson’s disease. Expert Syst Appl 36(3):7033–7039
Chen H-L et al (2013) An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40(1):263–271
Das R (2010) A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl 37(2):1568–1572
Ene M (2008) Neural network-based approach to discriminate healthy people from those with Parkinson’s disease. Ann Univ Craiova-Math Comput Sci Ser 35:112–116
Bilgin S (2017) The impact of feature extraction for the classification of amyotrophic lateral sclerosis among neurodegenerative diseases and healthy subjects. Biomed Signal Process Control 31:288–294
Zhou H et al (2016) Towards real-time detection of gait events on different terrains using time-frequency analysis and peak heuristics algorithm. Sensors 16(10):1634
Smidt GL, Deusinger RH, Arora J, Albright JP (1977) An automated accelerometry system for gait analysis. J Biomech 10(5):367–375
Wagg DK, Nixon MS (2004) On automated model-based extraction and analysis of gait. In: Automatic face and gesture recognition, Proceedings. Sixth IEEE international conference, pp 11–16
Begg RK, Palaniswami M, Owen B (2005) Support vector machines for automated gait classification. IEEE Trans Biomed Eng 52(5):828–838
Bovi G, Rabuffetti M, Mazzoleni P, Ferrarin M (2011) A multiple-task gait analysis approach: kinematic, kinetic and EMG reference data for healthy young and adult subjects. Gait Posture 33(1):6–13
PhysioNet [Online]. https://physionet.org/. Accessed 01 Dec 2017
Chen P-H, Wang R-L, Liou D-J, Shaw J-S (2013) Gait disorders in Parkinson’s disease: assessment and management. Int J Gerontol 7(4):189–193
Alaskar HM (2014) Dynamic self-organised neural network inspired by the immune algorithm for financial time series prediction and medical data classification. PhD Thesis, Liverpool John Moores University
Alaskar H, Hussain AJ, Paul FH, Al-Jumeily D, Tawfik H and Hamdan H (2014) Feature analysis of uterine electrohystography signal using dynamic self-organised multilayer network inspired by the immune algorithm. In: International conference on intelligent computing, pp 206–212
Alasker H, Alharkan S, Alharkan W, Zaki A, Riza LS (2017) Detection of kidney disease using various intelligent classifiers. In: Science in information technology (ICSITech), 2017 3rd international conference, pp 681–684
Khalaf M et al (2016) Training neural networks as experimental models: classifying biomedical datasets for sickle cell disease. In: International conference on intelligent computing, pp 784–795
Hausdorff JM et al (1997) Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J Appl Physiol 82(1):262–269
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 88(6):2045–2053
Shetty S, Rao YS (2016) SVM based machine learning approach to identify Parkinson’s disease using gait analysis. In: International conference on inventive computation technologies (ICICT), vol 2, pp 1–5
Mannini A, Trojaniello D, Della Croce U and Sabatini AM (2015) Hidden Markov model-based strategy for gait segmentation using inertial sensors: application to elderly, hemiparetic patients and Huntington’s disease patients. In: Engineering in medicine and biology society (EMBC), 2015 37th annual international conference of the IEEE, pp 5179–5182
Zheng H, Yang M, Wang H and McClean S (2009) Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. In: Intelligent patient management. Springer, pp 57–70
Lakany H (2008) Extracting a diagnostic gait signature. Pattern Recognit 41(5):1627–1637
Bonora G, Carpinella I, Cattaneo D, Chiari L, Ferrarin M (2015) A new instrumented method for the evaluation of gait initiation and step climbing based on inertial sensors: a pilot application in Parkinson’s disease. J Neuroeng Rehabil 12(1):45
Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N (2013) The appropriate use of approximate entropy and sample entropy with short data sets. Ann Biomed Eng 41(2):349–365
Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, Iram S (2018) Prediction of Preterm Deliveries from EHG Signals Using—Google Scholar [Online]. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Prediction+of+Preterm+Deliveries+from+EHG+Sig nals+Using&btnG=. Accessed 22 Mar 2018
Elreedy D, Atiya AF (2019) A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci 505:32–64
Vavoulas G, Pediaditis M, Chatzaki C, Spanakis EG, Tsiknakis M (2017) Artificial intelligence: concepts, methodologies, tools, and applications. IGI Global
Sánchez-Delacruz E, Parra PP (2018) Machine learning-based classification for diagnosis of neurodegenerative diseases. In: Proceedings of the eleventh Latin American workshop on logic/languages, algorithms and new methods of reasoning, Puebla
Ye Q, Xia Y and Yao Z (2018) Classification of gait patterns in patients with neurodegenerative disease using adaptive neuro-fuzzy inference system. Comput Math Methods Med
Wu Y, Ng SC (2010) A PDF-based classification of gait cadence patterns in patients with amyotrophic lateral sclerosis. In: 32nd annual international conference of the IEEE EMBS Buenos Aires, August 31–September 4
Di Biase L, Di Santo A, Caminiti ML, De Liso A, Shah SA, Ricci L, Di Lazzaro V (2020) Gait analysis in Parkinson’s disease: an overview of the most accurate markers for diagnosis and symptoms monitoring. Sensors (Basel)20(12): 3529. https://doi.org/10.3390/s20123529
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
Khorasani A, Daliri MR MR (2014) HMM for classification of Parkinson’s disease based on the raw gait data. J Med Syst 38(12):147. https://doi.org/10.1007/s10916-014-0147-5
Bouça-Machado R, Jalles C, Guerreiro D et al (2020) Gait kinematic parameters in Parkinson’s disease: a systematic review. J Parkinsons Dis 10(3):843–853. https://doi.org/10.3233/JPD-201969
Joshi D, Khajuria A, Joshi P (2017) An automatic non-invasive method for Parkinson’s disease classification. Comput Methods Programs Biomed 145:135–145. https://doi.org/10.1016/j.cmpb.2017.04.007
Coronato A, De Pietro G, Paragliola G (2014) A situation-aware system for the detection of motion disorders of patients with autism spectrum disorders. Expert Syst Appl 41(17):7868–7877
Coronato A, De Pietro G (2012) Detection of motion disorders of patients with autism spectrum disorders. In: IWAAL’12: Proceedings of the 4th international conference on ambient assisted living and home care, pp 415–422
Acknowledgements
This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project no. 2020/01/11744.
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Alaskar, H., Hussain, A.J., Khan, W. et al. A data science approach for reliable classification of neuro-degenerative diseases using gait patterns. J Reliable Intell Environ 6, 233–247 (2020). https://doi.org/10.1007/s40860-020-00114-1
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DOI: https://doi.org/10.1007/s40860-020-00114-1