Full Length ArticleUpper-limb functional assessment after stroke using mirror contraction: A pilot study
Introduction
Stroke is a common global health-care problem that has had serious negative impact on the life quality of the patients [1]. Accurate and prompt evaluation of stroke will increase eligibility of patients to receive proper therapy. According to the basis that whether the output depends on the therapists clinical experience as well as their subjective judgment of the patients’ limb movements, the assessment methods of upper limb and hand motor function are categorized as subjective and objective evaluations. The subjective evaluations in clinical settings mainly include different scales, such as Fugl–Meyer Assessment [2], Brunnstrom Assessment [3], modified Ashworth Scale [4], and Barthel Index [5], which are manually performed by experienced therapist focusing on muscle, motor pattern, changes of upper limb or hand function evaluation. Though the human-administered clinical scales are the accepted standard for quantifying motor performance after stroke, these measurement methods depend on the clinical experience of therapist causing limitation of interrater and intrarater reliability. They are also limited in terms of sensitivity for reflecting muscle changes and being time-consuming to apply clinically. Therefore, the objective method is required to improve the efficiency, sensitivity and reliability for the upper-limb function evaluation in stroke rehabilitation.
The objective evaluations for stroke are based on the state-of-the-art human–machine systems, which assess stroke through human biological signal analysis and the interaction between stroke patients and machine. For example, computed tomography (CT) as well as magnetic resonance imaging (MRI) protocols provide excellent tools for the evaluation of acute ischemic stroke [6], Golestani et al. reported that the functional magnetic resonance imaging (fMRI) demonstrated the impact of stroke on functional connections throughout the brain, which could be used for stroke evaluation even during the resting state of a patient [7]. Robotics have been proved to be a powerful measurement tool to assess the rehabilitation process of stroke [8], Semrau et al. used robotics to characterize the magnitude and timing of proprioceptive as well as motor recovery, and found robotic measures were correlated with clinical measures [9]. Besides, ultrasound is another kind of alternative biomedical signals applied in human–machine interface for neural rehabilitation and shows promising results in detecting muscle deformation [10], [11]. However, the aforementioned methods are along with expensive, complicated and bulky devices. Some other stroke assessment methods are more portable and easier to apply, such as the inertial measurement unit [12], surface electromyography (sEMG) and electroencephalogram (EEG) [13]. And compared with EEG, sEMG is with higher signal-to-noise ratio and more convenient to acquire.
The sEMG signals, containing a wealth of physiological information, have been widely applied in human–machine interfaces [14], [15], [16]. Many research efforts have been focused on applying EMG to stroke assessment and rehabilitation. Zhang et al. proposed the EMG-based closed-loop torque control in functional electrical stimulation (FES) showing a potential for limb function reconstruction after stroke [17]. Li et al. applied muscle and motor unit (MU) indices extracted from sEMG to evaluate muscle changes post stroke [18]. Kallenberg et al. applied high-density sEMG to investigate MU characteristics of the biceps brachii in poststroke patients and found that ratio of root-mean-square (RMS) value of MU action potentials on the affected side divided by that on the unaffected side correlated significantly with the Fugl–Meyer score [19]; Hu et al. made use of EMG representing the affected hand muscles activation levels and their co-contraction indexes for a quantitative evaluation of motor functional recovery process in chronic stroke patients during wrist training [20], and they also monitored the variations in the muscular coordination patterns by EMG parameters across the whole EMG-driven robot hand training sessions to understand the progress of the training quantitatively [21]. Besides, motor impairment after stroke typically affects one side of the body [1], thus the healthy side is involved in the rehabilitation process and the bimanual training is an effective rehabilitation strategy based on natural inter-limb coordination [22]. Leonardis et al. applied the sEMG of unaffected side to drive robotic hand exoskeleton for bilateral rehabilitation [23]. And the sEMG bias of the two arms was applied to drive FES for stroke rehabilitation in [24]. These studies indicate that EMG signals can be used to represent neuromuscular pathological state and motion intention in stroke rehabilitation. However, currently, sEMG technologies are mainly applied to be the trigger signal for rehabilitation devices or as a kind of metrics for monitoring the intra-patients rehabilitation process in clinical settings and researches [19]. It is challenging for inter-patients sEMG analysis, because sEMG is a kind of time-varying and non-stationary signal with huge variation across different people. Thus, there are still lack of effective and universal stoke auxiliary diagnosis methods based on sEMG.
Based on the aforementioned understandings, the upper-limb function evaluation method based on bilateral-arms sEMG was proposed and tested in this study, in order to drive the stroke auxiliary diagnosis research frontier boundary forward. As shown in Fig. 1, the aim of this pilot study is to investigate the feasibility of stroke patients recognition based on pattern recognition techniques and to assess the severity of stroke quantitatively, based on the sEMG features and their bias between bilateral arms.
Section snippets
Subjects
Eleven healthy subjects (seven males and four females, aged 23–28) and six stroke patients (five males and one female, aged from 50 to 70 years old, referenced as P1–P6) were recruited to participate in the experiment. All the subjects gave written informed consent prior to the experiment. The information of the patients are summarized in Table 1. The patients with Fugl–Meyer scores lower than 10 were excluded because the severity condition after stroke made them hard to move their affected
Bilateral arm feature comparison
The feature comparison results across all subjects are shown in Fig. 5. The MAV, RMS and WL bias of the two arms showed similar patterns, no matter for patients or healthy subjects. The scopes of bias across all target muscles for different features between the dominant arm and non-dominant arm are shown in Fig. 6. Since RMS, MAV and WL are related to the sEMG amplitude, they represent the activation of the MU. Thus, the bilateral bias of the above three features for patients shown in Fig. 5
Discussion
This study presents the stroke auxiliary assessment method based on the sEMG of bilateral arms during mirror contraction. The results prove the feasibility of applying pattern recognition techniques to distinguish stroke patients with acceptable accuracy and the SI index calculated by BBDA is correlated with Fugl–Meyer score, demonstrating the excellent quantitative assessment capability of the proposed algorithm.
The sEMG differences between the bilateral arms were obvious after stroke
Conclusion
This paper proposes a framework for upper-limb stroke auxiliary diagnosis. The framework is based on the sEMG of bilateral mirror contraction and realized stroke patients qualitative recognition and followed by the stroke severity quantitative evaluation. The results of the proposed objective stroke evaluation method were linear correlated with Fugl–Meyer scores indicating a potential application to assist therapist in improving the efficiency of individualized stroke diagnostic and
Conflict of interest
No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication. The work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors have approved the submission of the manuscript.
Acknowledgments
The authors would like to thank all the volunteers for their participation in the study. This work is supported by the National Natural Science Foundation of China (Grant Nos.51575338 and 61733011).
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