A hybrid method for real-time stimulation artefact removal during functional electrical stimulation with time-variant parameters.

Objective. In this study, a hybrid method combining hardware and software architecture is proposed to remove stimulation artefacts (SAs) and extract the volitional surface electromyography (sEMG) in real time during functional electrical stimulations (FES) with time-variant parameters.Approach. First, an sEMG detection front-end (DFE) combining fast recovery, detector and stimulator isolation and blanking is developed and is capable of preventing DFE saturation with a blanking time of 7.6 ms. The fragment between the present stimulus and previous stimulus is set as an SA fragment. Second, an SA database is established to provide six high-similarity templates with the current SA fragment. The SA fragment will be de-artefacted by a 6th-order Gram-Schmidt (GS) algorithm, a template-subtracting method, using the provided templates, and this database-based GS algorithm is called DBGS. The provided templates are previously collected SA fragments with the same or a similar evoking FES intensity to that of the current SA fragment, and the lengths of the templates are longer than that of the current SA fragment. After denoising, the sEMG will be extracted, and the current SA fragment will be added to the SA database. The prototype system based on DBGS was tested on eight able-bodied volunteers and three individuals with stroke to verify its capacity for stimulation removal and sEMG extraction.Results.The average stimulus artefact attenuation factor, SA index and correlation coefficient between clean sEMG and extracted sEMG for 6th-order DBGS were 12.77 ± 0.85 dB, 1.82 ± 0.37 dB and 0.84 ± 0.33 dB, respectively, which were significantly higher than those for empirical mode decomposition combined with notch filters, pulse-triggered GS algorithm, 1st-order and 3rd-order DBGS. The sEMG-torque correlation coefficients were 0.78 ± 0.05 and 0.48 ± 0.11 for able-bodied volunteers and individuals with stroke, respectively.Significance.The proposed hybrid method can extract sEMG during dynamic FES in real time.

An Anti Stimulation Artifacts and M-waves Surface Electromyography Detector with a Short Blanking Time.

A surface electromyography (sEMG) detector, that not only removes stimulation artifacts entirely but also increases the recording time, has been developed in this paper. The sEMG detector consists of an sEMG detection circuit and a stimulation isolator. The sEMG detection circuit employs a stimulus isolate switch (SIS), a blanking (BLK) and non-linear feed-back (NFB) circuit to remove the artifacts and to increase the recording time. In the SIS, the connection between stimulator and stimulation electrodes, along with the stimulation electrodes and the ground are controlled by an opto-isolator, and the connection of instrument amplifier and the recording electrodes are controlled by CMOS-based switches. The mode switches of the BLK and the NFB circuit also employs CMOS-based switches. By an accurate timing adjustment, the voluntary EMG can be recorded during electrical stimulation. Two 6 able-bodied experiments have been performed to test the three anti-artifact sEMG detector: BLK, BLK&SIS, BLK&SIS&NFB. The results indicate that the BLK&SIS&NFB proposed in this work effectively removes stimulus artifacts and M-waves, and has a longer recording time compared with BLK and BLK&SIS circuits.

Electromyographic bridge for promoting the recovery of hand movements in subacute stroke patients: A randomized controlled trial.

OBJECTIVE: The electromyographic bridge (EMGB) detects surface electromyographic signals from a non-paretic limb. It then generates electric pulse trains according to the electromyographic time domain features, which can be used to stimulate a paralysed or paretic limb in real time. This strategy can be used for the contralateral control of neuromuscular electrical stimulation (NMES) to improve motor function after stroke. The aim of this study was to compare the treat-ment effects of EMGB vs cyclic NMES on wrist and finger impairments in subacute stroke patients. METHODS: A total of 42 hemiplegic patients within 6 months of their cerebrovascular accidents were randomly assigned to 4-week treatments with EMGB or cyclic NMES. Each group underwent a standard rehabilitation programme and 10 sessions per week of hand training with EMGB or cyclic NMES. Outcome measures were: Brunnstrom stage, upper extremity components of the Fugl-Meyer Assessment, Motor Status Scale, voluntary surface electromyographic ratio and active range of motion of the wrist and finger joints. RESULTS: The EMGB group showed significantly greater improvements than the cyclic NMES group on the following measures: Brunnstrom stages for the hand, upper extremity - Fugl-Meyer Assessment, Motor Status Scale, and the voluntary surface electromyographic ratio of wrist and finger extensors. Eleven and 4 participants of the EMGB group who had no active wrist and finger movements, respectively, at the start of the treatment could perform measurable wrist and finger extensions after EMGB training. The corresponding numbers in the cyclic NMES group were only 4 and 1. CONCLUSION: In the present group of subacute stroke patients, the results favour EMGB over cyclic NMES for augmenting the recovery of volitional wrist and finger motion.

Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method.

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training.