Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision.

The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical-SVM-RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56-81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction.

Comparing Neuroplasticity Changes Between High and Low Frequency Gait Training in Subacute Stroke: Protocol for a Randomized, Single-Blinded, Controlled Study.

BACKGROUND: Walking recovery post stroke can be slow and incomplete. Determining effective stroke rehabilitation frequency requires the assessment of neuroplasticity changes. Neurobiological signals from electroencephalogram (EEG) can measure neuroplasticity through incremental changes of these signals after rehabilitation. However, changes seen with a different frequency of rehabilitation require further investigation. It is hypothesized that the association between the incremental changes from EEG signals and the improved functional outcome measure scores are greater in higher rehabilitation frequency, implying enhanced neuroplasticity changes. OBJECTIVE: The purpose of this study is to identify the changes in the neurobiological signals from EEG, to associate these with functional outcome measures scores, and to compare their associations in different therapy frequency for gait rehabilitation among subacute stroke individuals. METHODS: A randomized, single-blinded, controlled study among patients with subacute stroke will be conducted with two groups: an intervention group (IG) and a control group (CG). Each participant in the IG and CG will receive therapy sessions three times a week (high frequency) and once a week (low frequency), respectively, for a total of 12 consecutive weeks. Each session will last for an hour with strengthening, balance, and gait training. The main variables to be assessed are the 6-Minute Walk Test (6MWT), Motor Assessment Scale (MAS), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and quantitative EEG indices in the form of delta to alpha ratio (DAR) and delta-plus-theta to alpha-plus-beta ratio (DTABR). These will be measured at preintervention (R0) and postintervention (R1). Key analyses are to determine the changes in the 6MWT, MAS, BBS, MBI, DAR, and DTABR at R0 and R1 for the CG and IG. The changes in the DAR and DTABR will be analyzed for association with the changes in the 6MWT, MAS, BBS, and MBI to measure neuroplasticity changes for both the CG and IG. RESULTS: We have recruited 18 participants so far. We expect to publish our results in early 2023. CONCLUSIONS: These associations are expected to be positive in both groups, with a higher correlation in the IG compared to the CG, reflecting enhanced neuroplasticity changes and objective evaluation on the dose-response relationship. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27935.

Elucidating factors influencing machine learning algorithm prediction in spasticity assessment: a prospective observational study.

The Machine Learning Model (MLM) has garnered popularity in rehabilitation, ranging from developing algorithms in outcome prediction, prognostication, and training artificial intelligence. High-quality data plays a critical role in algorithm development. Limited studies have explored factors that may influence the MLM algorithm performance in predicting spasticity severity level. The objectives of this study were to train and validate a MLM algorithm for spasticity assessment and determine the algorithm's prediction performance in predicting ambiguous spasticity datasets. Forty-seven persons with central nervous system pathology that fulfilled the inclusion and exclusion criteria were recruited. Four biomechanical properties of spasticity were obtained using off-the-shelf wearable sensors. The data were analyzed individually, and ambiguous datasets were separated. The acceptable inertial data were used to train and validate MLM in predicting spasticity. The trained and validated MLM algorithm was later deployed to predict the ambiguous spasticity datasets. A series of MLM were applied, including Support Vector Machine, Decision Tree, and Random Forest. The MLM's performance accuracy of the validation data was 96%, 52%, and 72%, respectively. The validated MLM accuracy performance level predicting ambiguous datasets reduces to 20%, 23%, and 23%, respectively. This study elucidates data biases and variances of disease background, pathophysiological and anatomical factors that have to be considered in MLM training.

Revisiting Spasticity After Stroke: Clustering Clinical Characteristics for Identifying At-Risk Individuals.

PURPOSE: To collectively identify the clinical characteristics determining the risk of developing spasticity after stroke. PATIENTS AND METHODS: A cross-sectional study was conducted at a single rehabilitation outpatient clinic from June to December 2019. Inclusion criteria were stroke duration of over four weeks, aged 18 years and above. Exclusion criteria were presence of concurrent conditions other than stroke that could also lead to spasticity. Recruited patients were divided into "Spasticity" and "No spasticity" groups. Univariate analysis was deployed to identify significant predictive spasticity factors between the two groups followed by a two-step clustering approach for determining group of characteristics that collectively contributes to the risk of developing spasticity in the "Spasticity" group. RESULTS: A total of 216 post-stroke participants were recruited. The duration after stroke (p < 0.001) and the absence of hemisensory loss (p = 0.042) were two significant factors in the "Spasticity" group revealed by the univariate analysis. From a total of 98 participants with spasticity, the largest cluster of individuals (40 patients, 40.8%) was those within less than 20 months after stroke with moderate stroke and absence of hemisensory loss, while the smallest cluster was those within less than 20 months after severe stroke and absence of hemisensory loss (21 patients, 21.4%). CONCLUSION: Analyzing collectively the significant factors of developing spasticity may have the potential to be more clinically relevant in a heterogeneous post-stroke population that may assist in the spasticity management and treatment.

Brain-Derived Neurotrophic Factor Polymorphism and Aphasia after Stroke.

Stroke is one of the most deliberating causes of mortality and disability worldwide. Studies have implicated Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene as a genetic factor influencing stroke recovery. Still, the role of BDNF polymorphism in poststroke aphasia is relatively unclear. This review assesses the recent evidence on the association between the BDNF polymorphism and aphasia recovery in poststroke patients. The article highlights BNDF polymorphism characteristics, speech and language interventions delivered, and the influence of BNDF polymorphism on poststroke aphasia recovery. We conducted a literature search through PubMed and Google Scholar with the following terms: "brain derived-neurotrophic factor" and "aphasia" for original articles from January 2000 until June 2020. Out of 69 search results, a detailed selection process produced a total of 3 articles that met the eligibility criteria. All three studies included Val66Met polymorphism as the studied human BDNF gene. One of the studies demonstrated insufficient evidence to conclude that BDNF polymorphism plays a role in poststroke aphasia recovery. The remaining two studies have shown that Met allele genotype (either single or double nucleotides) was associated with poor aphasia recovery, in either acute or chronic stroke. Carriers of the Val66Met polymorphism of BDNF gave a poorer response to aphasia intervention and presented with more severe aphasia.

Factors affecting adherence to behaviours appropriate for the prevention of pressure injuries in people with spinal cord injury from Malaysia: a qualitative study.

STUDY DESIGN: Focus group qualitative study. OBJECTIVES: To explore factors affecting adherence to behaviours appropriate for the prevention of pressure injuries (PIs) in people with spinal cord injury (SCI) in Malaysia. SETTING: University Hospital, Malaysia METHODS: Four sets of focus group interviews were conducted, each with 5-10 participants, totalling 30 people with SCI. A trained interviewer used structured interviews designed to explore participants' experiences of complying with recommended behaviours for the prevention of PIs. All interviews were digitally recorded, transcribed, and analysed utilising thematic analysis. RESULTS: The factors that affected participants' adherence are classified into four main themes: (a) educational aspects, (b) internal drive, (c) social and environmental factors, and (d) post-SCI physiological changes. CONCLUSIONS: This qualitative study provides initial exploratory evidence regarding the thoughts, experience, and opinions pertaining to PI preventive behaviours within the Malaysian SCI population. The emerging themes contribute to an in-depth understanding of the competency of the Malaysian healthcare system in PI prevention, personal and societal factors influenced by the socio-demographic backgrounds, and disease-related factors that influence the adherence to such preventive interventions.