Neurotechnologies for Upper Limb Rehabilitation in Subacute and Chronic Stroke Patients

Abstract

Introduction: There is a need for improved and optimized rehabilitation due to a group of stroke patients who do not achieve full function again both in the subacute and chronic phase after stroke. Neurorehabilitation technologies and predictions of functional outcome looks promising to overcome this challenge. Aim: Sub-study 1 aimed to investigate the effect of the ArmeoSpring exoskeleton on improving upper limb function in subacute stroke patients as well as identifying underlying factors important for recovery and use these to create predic-tion models to predict the end outcome of the clinical scores Fugl-Meyer upper extremity (FM-UE) and action research arm test (ARAT). Sub-study 2 presents an intensive four-week combination therapy consisting of brain-computer inter-face (BCI), ArmeoSpring, and mirror therapy to improve upper limb function in chronic stroke patients. Method: For sub-study 1, data from BCI-STAR project including 48 subacute stroke patients were used. To investigate the effect of ArmeoSpring, the patients were divided into an intervention group and a control group. Clinical scores FM-UE and ARAT were used to assess the upper limb function. Furthermore, an exploratory factor analysis (EFA) was per-formed on the same data to identify underlying factors. Lastly, a stacking model trained with data from BCI-STAR project was used to create the predictions models. For sub-study 2, a single group pre-post study with an intensive four-week training program consisting of BCI, ArmeoSpring, and mirror therapy is presented. 30 chronic stroke patients will be recruited. FM-UE, ARAT and transcranial magnetic stimulation (TMS) will be used to evaluate the effect of the combina-tion therapy. Results: Sub-study 1 showed that both the ArmeoSpring intervention and control groups significantly improved their clinical scores (p<.001), but there was no significant difference between the groups (p=0.673). The EFA showed three underlying factors identified as severity of stroke, quality of rehabilitation and age. The prediction models for FM-UE and ARAT received a MAE score of 3.47 and 3.86 points, respectively, meaning they are capable of predicting the outcome of the arm function accurately. Conclusion: This thesis showed a tendency towards more intense training with ArmeoSpring can improve recovery fol-lowing stroke. Furthermore, a rapid start of rehabilitation after stroke and motor evoked potential (MEP) status seems to be important factors for a greater recovery. The prediction models were able to predict clinical outcome precisely. Finally, it is proposed that a combination therapy consisting of BCI, ArmeoSpring and mirror therapy will significantly increase upper limb function in chronic stroke patients.

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A master thesis from Aalborg University

Neurotechnologies for Upper Limb Rehabilitation in Subacute and Chronic Stroke Patients