Analysis of Gait Rhythm Fluctuations for Neurodegenerative Diseases by Phase Synchronization and Conditional Entropy

Previous studies have revealed that gait rhythm fluctuations convey important information, which is useful for understanding certain types of neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD). However, previous investigations only focused on the locomotor patterns of each individual foot rather than the relations between both feet. Therefore, in our study, phase synchronization (the index ρ) and conditional entropy (Hc) were applied to the five types of time series pairs of gait rhythms (stride time, swing time, stance time, % swing time and % stance time). The results revealed that compared with the patients with ALS, HD and PD, gait rhythms of normal subjects have the strongest phase synchronization property and minimum conditional entropy value. In addition, the indices ρ and Hc cannot only significantly differentiate among the four groups of subjects (ALS, HD, PD and control) but also have the ability to discriminate between any two of these subject groups. Finally, three representative classifiers were utilized in order to evaluate the possible capabilities of the indices ρ and Hc to distinguish the patients with neurodegenerative diseases from the healthy subjects, and achieved maximum area under the curve (AUC) values of 0.959, 0.928 and 0.824 for HD, PD and ALS detection, respectively. In summary, our study provides insight into the relational analysis between gait rhythms measured from both feet, and suggests that it should be considered seriously in the future studies investigating the impact of neurodegenerative disease and potential therapeutic intervention.

[1]  R. Gray.  Entropy and Information Theory , 1990, Springer New York.

[2]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[3]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[4]  Zhuang Jian-Jun,et al.  Decrease in Hurst exponent of human gait with aging and neurodegenerative diseases , 2008 .

[5]  Jim Richards,et al.  Whittle's gait analysis , 2012 .

[6]  M. Bates,et al.  Heart Rate Variability Biofeedback as a Strategy for Dealing with Competitive Anxiety : A Case Study , 2008 .

[7]  Shahram Jafari,et al.  An Expert System for Detection of Breast Cancer Using Data Preprocessing and Bayesian Network , 2011 .

[8]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[9]  P. Thangaraj,et al.  Mining rare event classes in noisy EEG by over sampling techniques , 2010, 2010 International Conference on Innovative Computing Technologies (ICICT).

[10]  Jane S. Paulsen,et al.  Unified Huntington's disease rating scale: Reliability and consistency , 1996, Movement disorders : official journal of the Movement Disorder Society.

[11]  David Moratal Pérez,et al.  Practical Biomedical Signal Analysis using MATLAB , 2012 .

[12]  C. Ioana,et al.  Wavelet-based characterization of gait signal for neurological abnormalities. , 2015, Gait & posture.

[13]  Huiru Zheng,et al.  Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[14]  廣瀬雄一,et al.  Neuroscience , 2019, Workplace Attachments.

[15]  Jeffrey M. Hausdorff,et al.  Gait variability and basal ganglia disorders: Stride‐to‐stride variations of gait cycle timing in parkinson's disease and Huntington's disease , 1998, Movement disorders : official journal of the Movement Disorder Society.

[16]  K. Newell,et al.  Long range correlations in the stride interval of running. , 2006, Gait & posture.

[17]  K. Matsuoka,et al.  Effect of prolonged free-walking fatigue on gait and physiological rhythm. , 2004, Journal of biomechanics.

[18]  O. Hardiman,et al.  Amyotrophic lateral sclerosis , 2011, The Lancet.

[19]  Jeffrey M. Hausdorff,et al.  Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. , 2000, Journal of applied physiology.

[20]  L. Amaral,et al.  Multifractality in human heartbeat dynamics , 1998, Nature.

[21]  T. Cover,et al.  Entropy, Relative Entropy and Mutual Information , 2001 .

[22]  Jürgen Kurths,et al.  Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography , 1998 .

[23]  M. Akay,et al.  Discrimination of walking patterns using wavelet-based fractal analysis , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  E. Siemers,et al.  Rate of functional decline in Huntington’s disease , 2000, Neurology.

[25]  Jane S. Paulsen,et al.  Unified Huntington's disease rating scale: Reliability and consistency , 1996, Movement disorders : official journal of the Movement Disorder Society.

[26]  C. Ross,et al.  Huntington's disease: from molecular pathogenesis to clinical treatment , 2011, The Lancet Neurology.

[27]  Jeffrey M. Hausdorff.  Gait variability: methods, modeling and meaning , 2005, Journal of NeuroEngineering and Rehabilitation.

[28]  Fazlollah M. Reza,et al.  Introduction to Information Theory , 2004, Lecture Notes in Electrical Engineering.

[29]  G. Kwakkel,et al.  Effects of external rhythmical cueing on gait in patients with Parkinson's disease: a systematic review , 2005, Clinical rehabilitation.

[30]  H. Stanley,et al.  Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Francis O. Walker.  Huntington's Disease. , 2007 .

[32]  D. Sternad,et al.  Local dynamic stability versus kinematic variability of continuous overground and treadmill walking. , 2001, Journal of biomechanical engineering.

[33]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[34]  Resul Das,et al.  A comparison of multiple classification methods for diagnosis of Parkinson disease , 2010, Expert Syst. Appl..

[35]  Geoffrey I. Webb,et al.  Not So Naive Bayes: Aggregating One-Dependence Estimators , 2005, Machine Learning.

[36]  Kurt Hornik,et al.  Neural Network Models , 2011 .

[37]  Liborio Parrino,et al.  Cyclic alternating pattern (CAP) and epilepsy during sleep: how a physiological rhythm modulates a pathological event , 2000, Clinical Neurophysiology.

[38]  T. Asano,et al.  ENTROPY , RELATIVE ENTROPY , AND MUTUAL INFORMATION , 2008 .

[39]  Matthew C Kiernan,et al.  Palliative care in amyotrophic lateral sclerosis , 2015, The Lancet Neurology.

[40]  Daniel Vélez Día,et al.  Biomechanics and Motor Control of Human Movement , 2013 .

[41]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[42]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[43]  Philippe De Wilde.  Neural Network Models , 1996 .

[44]  Sally McClean,et al.  Machine Learning and Statistical Approaches to Support the Discrimination of Neuro-degenerative Diseases Based on Gait Analysis , 2009 .

[45]  Wim van Drongelen,et al.  Signal processing for Neuroscientists : introduction to the analysis of physiological signals , 2007 .

[46]  Sridhar Krishnan,et al.  Computer-aided analysis of gait rhythm fluctuations in amyotrophic lateral sclerosis , 2009, Medical & Biological Engineering & Computing.

[47]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[48]  Alfred D. Grant.  Gait Analysis: Normal and Pathological Function , 2010 .

[49]  Jeffrey M. Hausdorff.  Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. , 2007, Human movement science.

[50]  Jeffrey M. Hausdorff.  Gait dynamics in Parkinson's disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. , 2009, Chaos.

[51]  J Hausdroff.  Gait variability : methods, modeling and meaning , 2005 .

[52]  S. Koh,et al.  The functional deficiency of bone marrow mesenchymal stromal cells in ALS patients is proportional to disease progression rate , 2012, Experimental Neurology.

[53]  I. Jolliffe.  Principal Component Analysis , 2002 .

[54]  Jeffrey M. Hausdorff,et al.  Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently , 2005, Journal of NeuroEngineering and Rehabilitation.