Functional force stimulation alters motor neuron discharge patterns.

Introduction: Beneficial effects have been observed for mechanical vibration stimulation (MVS), which are mainly attributed to tonic vibration reflex (TVR). TVR is reported to elicit synchronized motor unit activation during locally applied vibration. Similar effects are also observed in a novel vibration system referred to as functional force stimulation (FFS). However, the manifestation of TVR in FFS is doubted due to the use of global electromyography (EMG) features in previous analysis. Our study aims to investigate the effects of FFS on motor unit discharge patterns of the human biceps brachii by analyzing the motor unit spike trains decoded from the high-density surface EMG. Methods: Eighteen healthy subjects volunteered in FFS training with different amplitudes and frequencies. One hundred and twenty-eight channel surface EMG was recorded from the biceps brachii and then decoded after motion-artifact removal. The discharge timings were extracted and the coherence between different motor unit spike trains was calculated to quantify synchronized activation. Results and discussion: Significant synchronization within the vibration cycle and/or its integer multiples is observed for all FFS trials, which increases with increased FFS amplitude. Our results reveal the basic physiological mechanism involved in FFS, providing a theoretical foundation for analyzing and introducing FFS into clinical rehabilitation programs.

Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography.

OBJECTIVE: Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework. METHODS: A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared. RESULTS: The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction. SIGNIFICANCE: Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring.

Influence of Electrode Configuration on Muscle-Fiber-Conduction-Velocity Estimation Using Surface Electromyography.

OBJECTIVE: Muscle fiber conduction velocity (MFCV) is an important myoelectric parameter and can be estimated by analysing surface electromyography (EMG). Among many factors, electrode configuration plays a key role on MFCV estimation. Most studies adopt bipolar configuration (BC) for CV estimation. However, a thorough understanding of the underlying mechanism is lacking, confusing the design of the most appropriate EMG measurement setup for CV estimation. The aim of this study is therefore to systematically investigate the influence of electrode configuration on MFCV estimation. METHODS: Four possible configurations are considered, including BC, monopolar configuration (MC), common average reference (CAR), and a special monopolar configuration (SMC) using a fixed channel on the active muscle as reference. For each configuration, mathematical models computing the time delay between adjacent channels are derived and evaluated by dedicated simulation as well as real EMG measurements. MFCV was calculated using the maximum likelihood algorithm with and without channel normalization. RESULTS: The simulation results are in line with the mathematical models. The CVs estimated from the real EMG with and without normalization are 4.3 ±0.7 and 7.2 ±3.7 m/s, 5.7 ±1.3 and 20.4 ±4.7 m/s, 9.0 ±3.4 and 20.6 ±9.8 m/s, and 5.5 ±2.5 and 5.5 ±2.4 m/s for BC, MC, SMC, and CAR, respectively. CONCLUSION: Our results show normalized BC to produce the most accurate CV estimation, in line with the mathematical models and the simulation results. SIGNIFICANCE: These findings enable a better understanding of the influence of electrode configuration on MFCV estimation, providing useful information for EMG measurement setup design aiming at MFCV studies.