Background Instrumented pendulum test is an objective and repeatable biomechanical method of assessment for spasticity. However, multitude of sensor technologies and plenty of suggested outcome measures, confuse those interested in implementing this method in practice. Lack of a standard agreement on the definition of experimental setup and outcome measures adds to this ambiguity and causes the results of one study not to be directly attainable by a group that uses a different setup. In this systematic review of studies, we aim to reduce the confusion by providing pros and cons of the available choices, and also by standardizing the definitions. Methods A literature search was conducted for the period of 1950 to the end of 2019 on PubMed, Science Direct, Google Scholar and IEEE explore; with keywords of “pendulum test” and “Spasticity”. Results Twenty-eight studies with instrumented pendulum test for assessment of spasticity met the inclusion criteria. All the suggested methods of implementation were compared and advantages and disadvantages were provided for each sensor technology. An exhaustive list categorized outcome measures in three groups of angle-based, angular velocity-based, and angular acceleration-based measures with all different names and definitions. Conclusions With the aim of providing standardized methodology with replicable and comparable results, sources of dissimilarity and ambiguity among research strategies were found and explained with the help of graphical representation of pendulum movement stages and corresponding parameters on the angular waveforms. We hope using the provided tables simplify the choices when implementing pendulum test for spasticity evaluation, improve the consistency when reporting the results, and disambiguate inconsistency in the literature.
Objective. Alexithymia, as a fundamental notion in the diagnosis of psychiatric disorders, is characterized by deficits in emotional processing and, consequently, difficulties in emotion recognition. Traditional tools for assessing alexithymia, which include interviews and self-report measures, have led to inconsistent results due to some limitations as insufficient insight. Therefore, the purpose of the present study was to propose a new screening tool that utilizes machine learning models based on the scores of facial emotion recognition task. Method. In a cross-sectional study, 55 students of the University of Tabriz were selected based on the inclusion and exclusion criteria and their scores in the Toronto Alexithymia Scale (TAS-20). Then, they completed the somatization subscale of Symptom Checklist-90 Revised (SCL-90-R), Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II), and the facial emotion recognition (FER) task. Afterwards, support vector machine (SVM) and feedforward neural network (FNN) classifiers were implemented using K-fold cross validation to predict alexithymia, and the model performance was assessed with the area under the curve (AUC), accuracy, sensitivity, specificity, and F1-measure. Results. The models yielded an accuracy range of 72.7–81.8% after feature selection and optimization. Our results suggested that ML models were able to accurately distinguish alexithymia and determine the most informative items for predicting alexithymia. Conclusion. Our results show that machine learning models using FER task, SCL-90-R, BDI-II, and BAI could successfully diagnose alexithymia and also represent the most influential factors of predicting it and can be used as a clinical instrument to help clinicians in diagnosis process and earlier detection of the disorder.
Background: Exposure to small confined spaces evokes physiological responses such as increased heart rate in claustrophobic patients. However, little is known about electrocortical activity while these people are functionally exposed to such phobic situations. The aim of this study was to examine possible changes in electrocortical activity in this population. Method: Two highly affected patients with claustrophobia and two healthy controls participated in this in vivo study during which electroencephalographic (EEG) activity was continuously recorded. Relative power spectral density (rPSD) was compared between two situations of being relaxed in a well-lit open area, and sitting in a relaxed chair in a small (90 cm × 180 cm × 155 cm) chamber with a dim light. This comparison of rPSDs in five frequency bands of EEG was intended to investigate possible patterns of change in electrical activity during fear-related situation. This possible change was also compared between claustrophobic patients and healthy controls in all cortical areas. Results: Statistical models showed that there is a significant interaction between groups of participants and experimental situations in all frequency bands ( P < 0.01). In other words, claustrophobic patients showed significantly different changes in electrical activity while going from rest to the test situation. Clear differences were observed in alpha and theta bands. In the theta band, while healthy controls showed an increase in rPSD, claustrophobic patients showed an opposite decrease in the power of electrical activity when entering the confined chamber. In alpha band, both groups showed an increase in rPSD, though this increase was significantly higher for claustrophobic patients. Conclusion: The effect of in vivo exposure to confined environments on EEG activity is different in claustrophobic patients than in healthy controls. Most of this contrast is observed in central and parietal areas of the cortex, and in the alpha and theta bands.
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