Evaluating quiet standing posture of post-stroke patients by classifying cerebral infarction and cerebral hemorrhage patients

ABSTRACT Strokes are the third leading cause of disability worldwide. Currently, several types of performance assessment, such as the Fugl–Meyer index, are used to explore the overall difference between cerebral infarction (CI) and cerebral hemorrhage (CH) post-stroke patients. However, these performance assessments ignore subtle differences in the limbs of patients, which could be helpful for rehabilitation training. This study was designed to determine and evaluate the differences between the limbs of CI and CH patients. First, we collected the kinematic data of patients and extracted the spatio-temporal features. Then, we developed four different models to classify the CI and CH patients, in which a linear support vector machine (LSVM) classifier method achieved an 80.1% classification accuracy. Finally, we calculated the decision boundary of the shoulder and ankle marker position features separately based on the LSVM model. From the decision boundary, we determined that the CI patients' shoulder position appeared to be anterior to that of the CH patients, and the CH patients had a wider stance width compared to the CI patients. Such findings can serve as guidance for doctors and help provide professional rehabilitation courses for post-stroke patients. GRAPHICAL ABSTRACT

[1]  M. Elkind,et al.  Comparison of outcomes after intracerebral hemorrhage and ischemic stroke. , 2010, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[2]  Lalit Arora,et al.  Efficacy of Trunk Rehabilitation and Balance Training On Trunk Control, Balance and Gait in Post Stroke Hemiplegic Patients: A Randomized Controlled Trial , 2014 .

[3]  Jian Pei,et al.  Classification: Advanced Methods , 2012 .

[4]  R W Bohannon,et al.  Lower extremity weight bearing under various standing conditions in independently ambulatory patients with hemiparesis. , 1985, Physical therapy.

[5]  Anh-Cang Phan,et al.  Automatic Detection and Classification of Brain Hemorrhages , 2018, ACIIDS.

[6]  V. de Groot,et al.  Functional recovery differs between ischaemic and haemorrhagic stroke patients. , 2008, Journal of rehabilitation medicine.

[7]  Richard W. Bohannon,et al.  Trunk muscle strength is impaired multidirectionally after stroke , 1995 .

[8]  F. Horak,et al.  Direction-specific postural instability in subjects with Parkinson's disease , 2005, Experimental Neurology.

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  Estela Bicho,et al.  Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease , 2016, Comput. Intell. Neurosci..

[11]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  M. Owolabi,et al.  Stroke: a global response is needed , 2016, Bulletin of the World Health Organization.

[14]  G. Turnbull,et al.  Symmetry of weight distribution in normals and stroke patients using digital weigh scales , 1986 .

[15]  L. Baxter.  Random Fields on a Network: Modeling, Statistics, and Applications , 1996 .

[16]  D. Black,et al.  Do Stroke Patients With Intracerebral Hemorrhage Have a Better Functional Outcome Than Patients With Cerebral Infarction? , 2009, PM & R : the journal of injury, function, and rehabilitation.

[17]  E. Troisi,et al.  Functional Outcome of Ischemic and Hemorrhagic Stroke Patients After Inpatient Rehabilitation: A Matched Comparison , 2003, Stroke.

[18]  R. Paillex,et al.  Changes in the standing posture of stroke patients during rehabilitation. , 2005, Gait & posture.

[19]  Marimuthu Palaniswami,et al.  Support vector machines for automated gait classification , 2005, IEEE Transactions on Biomedical Engineering.

[20]  J. Broad,et al.  Inadequacy of clinical scoring systems to differentiate stroke subtypes in population-based studies. , 1995, Stroke.

[21]  D Boisson,et al.  Predominance of postural imbalance in left hemiparetic patients. , 1997, Scandinavian journal of rehabilitation medicine.

[22]  David E. Johnson,et al.  Maximizing Text-Mining Performance , 1999 .

[23]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[24]  Fabio Babiloni,et al.  A LightGBM-Based EEG Analysis Method for Driver Mental States Classification , 2019, Comput. Intell. Neurosci..

[25]  A. Danielsson,et al.  Upper extremity recovery after ischaemic and haemorrhagic stroke: Part of the SALGOT study , 2016, European stroke journal.

[26]  Benno M Nigg,et al.  Marker-based classification of young–elderly gait pattern differences via direct PCA feature extraction and SVMs , 2013, Computer methods in biomechanics and biomedical engineering.

[27]  Ali Kashif Bashir,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2013, ICIRA 2013.

[28]  Ray-Yau Wang,et al.  The relation between ankle impairments and gait velocity and symmetry in people with stroke. , 2006, Archives of physical medicine and rehabilitation.

[29]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[30]  Joo Soo Yoon,et al.  The Effect of Ankle Joint Muscle Strengthening Training and Static Muscle Stretching Training on Stroke Patients’ C.O.P Sway Amplitude , 2013, Journal of physical therapy science.

[31]  Sukmin Lee,et al.  Correlations among trunk impairment, functional performance, and muscle activity during forward reaching tasks in patients with chronic stroke , 2015, Journal of physical therapy science.

[32]  Min Kyun Sohn,et al.  Effect of Transcranial Direct Current Stimulation on Postural Stability and Lower Extremity Strength in Hemiplegic Stroke Patients , 2013, Annals of rehabilitation medicine.

[33]  Analysis of strategies used by hemiplegic stroke patients to achieve toe clearance , 2016 .

[34]  Joarder Kamruzzaman,et al.  Support Vector Machines and Other Pattern Recognition Approaches to the Diagnosis of Cerebral Palsy Gait , 2006, IEEE Transactions on Biomedical Engineering.

[35]  Ke Li,et al.  Evaluation of postural instability in stroke patient during quiet standing , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[36]  Luciano Fasotti,et al.  Recovery of standing balance in postacute stroke patients: a rehabilitation cohort study. , 2004, Archives of physical medicine and rehabilitation.

[37]  Yuchun Lee,et al.  Handwritten Digit Recognition Using K Nearest-Neighbor, Radial-Basis Function, and Backpropagation Neural Networks , 1991, Neural Computation.

[38]  J. Mizrahi,et al.  Postural stability in stroke patients: Vectorial expression of asymmetry, sway activity and relative sequence of reactive forces , 1989, Medical and Biological Engineering and Computing.

[39]  N. Arunkumar,et al.  Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease , 2018, Future Gener. Comput. Syst..

[40]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[41]  Ju Wang,et al.  PCA-based SVM for automatic recognition of gait patterns. , 2008, Journal of applied biomechanics.

[42]  Tak-Chung Fu,et al.  Pattern discovery from stock time series using self-organizing maps , 2016 .

[43]  Bart Nienhuis,et al.  Effects of visual center of pressure feedback on postural control in young and elderly healthy adults and in stroke patients. , 2003, Human movement science.

[44]  J. Medeiros,et al.  [Relationship between lower extremity muscle strength and dynamic balance in people post-stroke]. , 2003, Medicina.