Introduction: COVID-19 was the turning point of 2020, endangering the health of the entire population around the world. Among other therapeutic methods and supportive measures, physiotherapy represents a useful intervention applied on COVID-19 patients suffering from respiratory symptoms, this being supported by recent literature data. Materials and Methods: The study was performed on 45 patients diagnosed with COVID-19 (28 men, 17 women, mean age = 65.03, standard deviation = 14.83). They participated for 2 weeks (the required period of hospitalization) to a series of 14 physiotherapy sessions, which included: position changing, respiratory control, passive joint movements, bed workout, and walking exercises. Depending on the status of the patient, two distinct types of physiotherapy were performed (mild and active). The status of the patients was assessed through a basic assessment of the vital signs, range of motion, degree of dyspnea, and also through the UZ Leuven Start To Move protocol (STMP). The statistical analysis of the data was performed using the Statistica 10 program and included the Spearman correlations (for measuring the strength and direction of association between the ranked variables), the Mann-Whitney test (for measuring the significance of the differences between the groups of patients who undergone light vs. active physiotherapy) and factor analysis (for assessing the changes of the clinical parameters investigated in the study, depending on the type of applied therapy). All differences were considered significant at p < 0.05. Results: The majority of patients (n = 38) benefited from physiotherapy, with the complete disappearance of symptoms met only in the group of patients who followed active physiotherapy. These effects depended on the applied type of physiotherapy (mild vs. active, p = 0.47). In contrast, all patients who were unable to perform physiotherapy (n = 7) remained symptomatic at discharge. Conclusion: The results of this study point out the significant additional role of physiotherapy for a better management of COVID-19 patients. More studies are needed to investigate not only the impact that physiotherapy has on the symptoms of this disease, but also its effects on effort capacity, muscle strength and lung capacity.
The purpose of this research is to evaluate the performances of some features extraction methods and classification algorithms for the electroencephalographic (EEG) signals recorded in a motor task imagery paradigm. The sessions were performed by the same subject in eight consecutive years. Modeling the EEG signal as an autoregressive process (by means of Itakura distance and symmetric Itakura distance), amplitude modulation (using the amplitude modulation energy index) and phase synchronization (measuring phase locking value, phase lag index and weighted phase lag index) are the methods used for getting the appropriate information. The extracted features are classified using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance, support vector machine and k nearest neighbor classifiers. The highest classifications rates are achieved when Itakura distance with Mahalanobis distance based classifier are applied. The outcomes of this research may improve the design of assistive devices for restoration of movement and communication strength for physically disabled patients in order to rehabilitate their lost motor abilities and to improve the quality of their daily life.
The paper proposes an approach based on higher order statistics and phase synchronization for detection and classification of relevant features in electroencephalographic (EEG) signals recorded during the subjects are performing motor tasks. The method was tested on two different datasets and the performance was evaluated using k nearest neighbor classifier. The results (classification rates higher than 90%) have shown that the method can be used for discriminating right and left motor imagery tasks as an offline analysis for EEG in a brain computer interface system.
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