Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.

[1]  Anil K. Jain,et al.  Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  A. Simmons,et al.  Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment , 2013, Psychiatry Research: Neuroimaging.

[3]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[4]  E. Amaro,et al.  Use of SVM methods with surface-based cortical and volumetric subcortical measurements to detect Alzheimer's disease. , 2010, Journal of Alzheimer's disease : JAD.

[5]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[6]  Gert Jan Wijlhuizen,et al.  Falls incidence underestimates the risk of fall-related injuries in older age groups: a comparison with the FARE (Falls risk by Exposure). , 2012, Age and ageing.

[7]  Estela Bicho,et al.  Compensatory Postural Adjustments in an Oculus Virtual Reality Environment and the Risk of Falling in Alzheimer's Disease , 2016, Dementia and Geriatric Cognitive Disorders Extra.

[8]  H. Semlitsch,et al.  Discrimination between demented patients and normals based on topographic EEG slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers. , 1994, Electroencephalography and clinical neurophysiology.

[9]  Pedro Macedo,et al.  An Enhanced Reservation-Based MAC Protocol for IEEE 802.15.4 Networks , 2011, Sensors.

[10]  Rasmus Berg Palm,et al.  Prediction as a candidate for learning deep hierarchical models of data , 2012 .

[11]  Nikhil R. Pal,et al.  Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease , 2015, Bioinform..

[12]  Yi Su,et al.  Heterogeneous multimodal biomarkers analysis for Alzheimer’s disease via Bayesian network , 2016, EURASIP J. Bioinform. Syst. Biol..

[13]  F. Prince,et al.  Intrasession reliability of center of pressure measures of postural steadiness in healthy elderly people. , 2004, Archives of physical medicine and rehabilitation.

[14]  Placido Bramanti,et al.  Elman neural network for the early identification of cognitive impairment in Alzheimer's disease. , 2014, Functional neurology.

[15]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[16]  Robert Koprowski,et al.  Machine learning, medical diagnosis, and biomedical engineering research - commentary , 2014, Biomedical engineering online.

[17]  Y. Stern.  Cognitive reserve in ageing and Alzheimer's disease , 2012, The Lancet Neurology.

[18]  Keinosuke Fukunaga,et al.  Effects of Sample Size in Classifier Design , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[20]  Koichi Shimizu,et al.  Analysis of Postural Adjustment Responses to Perturbation Stimulus by Surface Tilts in the Feet-together Position , 2011 .

[21]  D A Winter,et al.  Sampling duration effects on centre of pressure summary measures. , 2001, Gait & posture.

[22]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[23]  Sidong Liu,et al.  Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease , 2015, IEEE Transactions on Biomedical Engineering.

[24]  F. Horak,et al.  Postural perturbations: new insights for treatment of balance disorders. , 1997, Physical therapy.

[25]  A. Mechelli,et al.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.

[26]  Hojjat Adeli,et al.  New diagnostic EEG markers of the Alzheimer’s disease using visibility graph , 2010, Journal of Neural Transmission.

[27]  D. Winter,et al.  Assessment of balance control in humans. , 1990, Medical progress through technology.

[28]  Seong-Whan Lee,et al.  Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.

[29]  S. Darby,et al.  Numerical simulation of bank erosion and channel migration in meandering rivers , 2002 .

[30]  Pertti Era,et al.  Force Platform Measurements as Predictors of Falls among Older People – A Review , 2006, Gerontology.

[31]  C. Jack,et al.  Anterior temporal lobes and hippocampal formations: normative volumetric measurements from MR images in young adults. , 1989, Radiology.

[32]  I. Santana,et al.  Montreal Cognitive Assessment (MoCA): Normative study for the Portuguese population , 2011, Journal of clinical and experimental neuropsychology.

[33]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[34]  Andrea Merlo,et al.  Postural stability and history of falls in cognitively able older adults: the Canton Ticino study. , 2012, Gait & posture.

[35]  Torsten Bumgarner,et al.  Biomechanics and Motor Control of Human Movement , 2013 .

[36]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[37]  E. Fatemizadeh,et al.  Discrimination between alzheimer's disease and control group in MR-images based on texture analysis using artificial neural network , 2006, 2006 International Conference on Biomedical and Pharmaceutical Engineering.

[38]  Luca Palmerini,et al.  Feature Selection for Accelerometer-Based Posture Analysis in Parkinson's Disease , 2011, IEEE Transactions on Information Technology in Biomedicine.

[39]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[40]  Christoph Lehmann,et al.  Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.

[41]  Jin Wang,et al.  An Optimal Decisional Space for the Classification of Alzheimer's Disease and Mild Cognitive Impairment , 2014, IEEE Transactions on Biomedical Engineering.

[42]  Holger R. Maier,et al.  Optimal division of data for neural network models in water resources applications , 2002 .

[43]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Tipu Z. Aziz,et al.  Parkinson's Disease tremor classification - A comparison between Support Vector Machines and neural networks , 2012, Expert Syst. Appl..

[45]  M. Woollacott,et al.  Attention and the control of posture and gait: a review of an emerging area of research. , 2002, Gait & posture.

[46]  Estela Bicho,et al.  Analysis of postural kinetics data using Artificial Neural Networks in Alzheimer's Disease , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[47]  Estela Bicho,et al.  Postural Stability Analysis with Inertial Measurement Units in Alzheimer's Disease , 2014, Dementia and Geriatric Cognitive Disorders Extra.

[48]  Simon Rogers,et al.  A First Course in Machine Learning , 2011, Chapman and Hall / CRC machine learning and pattern recognition series.

[49]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[50]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[51]  Margarida Silveira,et al.  Longitudinal FDG-PET features for the classification of Alzheimer's disease , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[52]  J. P. Paul,et al.  What is balance? , 2000, Clinical rehabilitation.

[53]  M. Woollacott,et al.  Attentional demands and postural control: the effect of sensory context. , 2000, The journals of gerontology. Series A, Biological sciences and medical sciences.

[54]  Marco Schieppati,et al.  Balance in Parkinson's disease under static and dynamic conditions , 2006, Movement disorders : official journal of the Movement Disorder Society.

[55]  Michael G. Madden,et al.  The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data , 2005, Knowl. Based Syst..

[56]  Michael Hornberger,et al.  A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia , 2016, Front. Aging Neurosci..

[57]  P. Kannus,et al.  Prevention of falls and consequent injuries in elderly people , 2005, The Lancet.

[58]  K. Coburn,et al.  EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures. , 1994, Electroencephalography and clinical neurophysiology.

[59]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[60]  Luigi Baratto,et al.  Balance features in Alzheimer's disease and amnestic mild cognitive impairment. , 2009, Journal of Alzheimer's disease : JAD.

[61]  Javier Escudero,et al.  Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[62]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[63]  David A. Winter,et al.  Biomechanics and Motor Control of Human Movement , 1990 .

[64]  Jie Guan,et al.  Independent component analysis-based multimodal classification of Alzheimer's disease versus healthy controls , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[65]  Sidong Liu,et al.  Early diagnosis of Alzheimer's disease with deep learning , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).