Revealing stroke survivor gait deficits during rehabilitation using ensemble empirical mode decomposition of surface electromyography signals. (August 2020) - Tan, Ming-Gui; Ho, Jee-Hou; Goh, Hui-Ting; Ng, Hoon Kiat; Abdul Latif, Lydia; Mazlan, Mazlina
Highlights: Gait sEMG is decomposed using EEMD. IMFs reveal detailed information of muscle activations in various frequencies. Abnormal gaits in foot drop and prolonged stance phase are explained. Case studies of stroke survivors in a 6-month recovery period are discussed. Abstract: In this paper, an attempt is made to explain the stroke survivor gait recovery mechanism using Ensemble Empirical Mode Decomposition (EEMD) of surface electromyography signals (sEMG). The existing gait functionality indices could describe the gait recovery through the improvement of gait parameters such as stride length, heel clearance, stance and swing time etc. However these indices reveal little information related to the kinesiology status. To address this knowledge gap, we propose an approach to decompose the sEMG signals acquired during the rehabilitation treatment using EEMD so as to reveal gait deficits in the perspective of motor unit recruitment and its firing patterns. 15 stroke survivors were recruited and their sEMG signals acquired from Gastrocnemius Lateral (GL) and Tibialis Anterior (TA) muscles were further decomposed into different intrinsic mode functions (IMF) using EEMD. Each IMF contains superimposed motor unit action potential (MUAP) with its specific frequency range. The evolvement of IMFs over three recovery stages was observed. Results show that foot drop can be caused by lack of high frequency IMF components in TA during the swing phase. Besides that, spasticity was observed in all IMF components from GL muscle that leads to counteract with TA muscle. Lack of high frequency IMF components in GL during the stance phase would increase the stance time. Co-activation from TA during the stance phase could contribute to this effect as well. In conclusion, the proposed approach could reveal additional information at kinesiology level to explain how well a stroke survivor recovers.