An electrocardiogram (ECG) signal may be affected by different types of disturbances/noises e.g. Power Line Interference (PLI), baseline wander, motion artifacts etc. Removing such disturbing signals from an ECG signal is the key to its accurate analysis leading to diagnosis of potential disease(s). In this paper, we present State Space Recursive Least Squares (SSRLS) filter based method for removal of 50Hz PLI noise from an ECG signal. The results are encouraging when compared with notch filter of varying attenuation levels. Under varying PLI noise levels, the SSRLS filter is seen to show superior performance as compared to notch filter. This work is part of National Information & Communication Technologies Research and Development (ICT R&D) fund project done in collaboration with National Institute of Heart Diseases (NIHD), Pakistan.
Improving the hand motor skills in post-stroke patients through rehabilitation based on movement intention derived signals from the brain in conjunction with robot-assistive technologies are explored. The experimental work is conducted using Electroencephalogram based Brain-Computer Interface (EEG-BCI) system and the AMADEO hand rehabilitation robotic device. Two protocols using visual-cues and then using a 2-Dimensional (2D) interactive game is presented on a computer screen to healthy subjects as well as post-stroke patients performing the hand movements. The movement intention signals during hand movement are detected through the Support Vector Machine (SVM) classifier. The intent signals produced at six distinct electrodes are investigated to determine electrodes contributing most to the SVM classifier's performance. Overall, the game protocol shows better classification results for both healthy and stroke patients compared to the visual-cues protocol. FC3 is found to be the most consistent electrode site for the detection of the motor intention of the hand for both protocols. In the experimental work, average classification accuracy for the visual-cues protocol of 67.56% for healthy subjects and 56.24% for stroke patients were obtained. For the game protocol, the classifier accuracy produced for healthy participants was 79.7% and for the post-stroke patients was 66.64%. The results confirm that the intention signal is more pronounced during more engaging activities, such as playing games, for both healthy and stroke subjects. Therefore, the effectiveness of rehabilitation therapy for post-stroke patients could be significantly enhanced using interactive and engaging exercise protocols.
BackgroundRehabilitation of post-stroke patients with motor impairments promotes re-learning of lost motor functions through the brain neuroplasticity. Monitoring of electroencephalogram (EEG) signals has the potential to show neuroplasticity changes that take place during motor training.MethodsIn this study, an EEG-derived time-domain pattern namely movement-related cortical potential (MRCP) was deployed to assess the effect of motor training in seven post-stroke patients. Patients were divided into two groups; group A comprising four subjects with supratentorial lesions and group B consisting of three subjects with infratentorial lesions. Both groups participated in motor training with an AMADEO hand rehabilitation device. During pre and post-training periods, EEG signals at eight selected electrodes were recorded. In addition, hand-kinematic parameters, and clinical tests were measured at the beginning and the end of all training sessions.ResultsThe negative peak of the MRCP signals decreased at all electrodes and reached significance in seven of eight electrodes for group A after 12 training sessions, while it was decreased at all electrodes and reached significance in two of eight electrodes for group B after 24 sessions according to paired t-test (p < 0.05). Moreover, these MRCP changes correlated with improvements in kinematic parameters and clinical test results for both groups.ConclusionsThis study shows that robot-assisted training that improves clinical outcomes is associated with MRCP pattern changes. Subjects with infratentorial strokes improved slower clinically compared to subjects with supratentorial strokes. This was consistent with the longer rehabilitation required for this group of patients to produce significant changes in MRCP. The reduction of negative peaks of the MRCP signal indicates that neurological pathways are established and less cortical resources are needed for motor tasks. This study demonstrates the significance of EEG as a practical and low-cost tool in detecting patterns associated with brain neuroplasticity in the course of motor re-learning. Ethics ApprovalThe procedures performed in this study were approved by the University of Wollongong Ethics Committee (Ethics application number: 2014/400) on 03/07/2017.
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