Antagonist Activation Measurement in Triceps Surae Using High-Density and Bipolar Surface EMG in Chronic Hemiparesis.

After a stroke, antagonist muscle activation during agonist command impedes movement. This study compared measurements of antagonist muscle activation using surface bipolar EMG in the gastrocnemius medialis (GM) and high-density (HD) EMG in the GM and soleus (SO) during isometric submaximal and maximal dorsiflexion efforts, with knee flexed and extended, in 12 subjects with chronic hemiparesis. The coefficients of antagonist activation (CAN) of GM and SO were calculated according to the ratio of the RMS amplitude during dorsiflexion effort to the maximal agonist effort for the same muscle. Bipolar CAN (BipCAN) was compared to CAN from channel-specific (CsCAN) and overall (OvCAN) normalizations of HD-EMG. The location of the CAN centroid was explored in GM, and CAN was compared between the medial and lateral portions of SO. Between-EMG system differences in GM were observed in maximal efforts only, between BipCAN and CsCAN with lower values in BipCAN (p < 0.001), and between BipCAN and OvCAN with lower values in OvCAN (p < 0.05). The CAN centroid is located mid-height and medially in GM, while the CAN was similar in medial and lateral SO. In chronic hemiparesis, the estimates of GM hyperactivity differ between bipolar and HD-EMGs, with channel-specific and overall normalizations yielding, respectively, higher and lower CAN values than bipolar EMG. HD-EMG would be the way to develop personalized rehabilitation programs based on individual antagonist activations.

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans.

Electromyography-based gesture recognition has become a challenging problem in the decoding of fine hand movements. Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user's gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals.

Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies.

Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed.

Force and Neuromuscular Responses During a Handgrip Hold to Failure Anchored to a Moderate Perceptual Intensity in Males.

OBJECTIVES: The current study investigated performance fatigability (PF) and time course of changes in force, electromyographic amplitude (EMG AMP) and frequency (EMG MPF), and neuromuscular efficiency (NME) during a sustained, isometric, handgrip hold to failure (HTF) using the rating of perceived exertion (RPE)-Clamp Model. METHODS: Twelve males performed a handgrip HTF anchored to RPE=5. The time to task failure (Tlim), force (N), EMG AMP and MPF, and NME (normalized force/ normalized EMG AMP) were recorded. Analyses included a paired samples t-test for PF at an alpha of p<0.05, 1-way repeated measures ANOVA across time and post-hoc t-tests (p<0.0025) for force, EMG AMP and MPF, and NME responses. RESULTS: The PF (pre- to post- maximal force % decline) was 38.2±11.5%. There were decreases in responses, relative to 0% Tlim, from 40% to 100% Tlim (force), at 30%, 60%, and 100% Tlim (EMG AMP), from 10% to 100% Tlim(EMP MPF), and from 50% to 65%, and 80% to 100% Tlim (NME) (p<0.0025). CONCLUSIONS: The RPE-Clamp Model in this study demonstrated that pacing strategies may be influenced by the integration of anticipatory, feedforward, and feedback mechanisms, and provided insights into the relationship between neuromuscular and perceptual responses, and actual force generating capacity.

Neural Drive and Motor Unit Characteristics of the Serratus Anterior in Individuals With Scapular Dyskinesis.

OBJECTIVE: Scapular dyskinesis is one of the causes of shoulder disorders and involves muscle weakness in the serratus anterior. This study investigated whether motor unit (MU) recruitment and firing property, which are important for muscle exertion, have altered in serratus anterior of the individuals with scapular dyskinesis. METHODS: Asymptomatic adults with (SD) and without (control) scapular dyskinesis were analyzed. Surface electromyography (sEMG) waveforms were collected at submaximal voluntary contraction of the serratus anterior. The sEMG waveform was decomposed into MU action potential amplitude (MUAPAMP), mean firing rate (MFR), and recruitment threshold. MUs were divided into low, moderate, and high thresholds, and MU recruitment and firing properties of the groups were compared. RESULTS: High-threshold MUAPAMP was significantly smaller in the SD group than in the control group. The control group also exhibited recruitment properties that reflected the size principle, however, the SD group did not. Furthermore, the SD group had a lower MFR than the control group. CONCLUSIONS: Individuals with scapular dyskinesis exhibit altered MU recruitment properties and lower firing rates of the serratus anterior; this may be detrimental to muscle performance. Thus, it may be necessary to improve the neural drive of the serratus anterior when correcting scapular dyskinesis.

Effects of Bilateral or Unilateral Plyometric Training of Lower Limbs on the Bilateral Deficit During Explosive Efforts.

OBJECTIVES: Bilateral Deficit (BLD) occurs when the force generated by both limbs together is smaller than the sum of the forces developed separately by the two limbs. BLD may be modulated by physical training. Here, were investigated the effects of unilateral or bilateral plyometric training on BLD and neuromuscular activation during lower limb explosive extensions. METHODS: Fourteen young males were randomized into the unilateral (UL_) or bilateral (BL_) training group. Plyometric training (20 sessions, 2 days/week) was performed on a sled ergometer, and consisted of UL or BL consecutive, plyometric lower limb extensions (3-to-5 sets; 8-to-10 repetitions). Before and after training, maximal explosive efforts with both lower limbs or with each limb separately were assessed. Electromyography of representative lower limb muscles was measured. RESULTS: BL_training significantly and largely decreased BLD (p=0.003, effect size=1.63). This was accompanied by the reversion from deficit to facilitation of the electromyography amplitude of knee extensors during bilateral efforts (p=0.007). Conversely, UL_training had negligible effects on BLD (p=0.781). Also, both groups showed similar improvements in their maximal explosive power generated after training. CONCLUSIONS: Bilateral plyometric training can mitigate BLD, and should be considered for training protocols focused on improving bilateral lower limb motor performance.

Effectiveness of the Copenhagen adduction modified position stage 1: EMG measurements and hand-held dynamometer analysis in young and healthy men.

OBJECTIVES: To verify the effectiveness of the use of a modified position of the Copenhagen Adduction (CA) stage 1 compared to the original position. DESIGN: Cross-sectional study. SETTING: Laboratory. PARTICIPANTS: 31 healthy men aged 23.7 ± 1.9 years with no recent or chronic general pathology. MAIN OUTCOME MEASURES: Differences between EMG amplitudes for the adductor longus (AL), rectus femoris (RF) and semi tendinous (ST) during dynamic contractions and adductor maximal isometric voluntary contraction (MIVC) force values between CA stage 1 standard and modified positions were assessed with either Wilcoxon or paired t-test. RESULTS: No significant differences were observed for EMG amplitudes of the AL (p-value = 0.724) and for the RF muscle (p-value = 0.337) and for the adductor force (p-value = 0.361) between the two positions. A significant difference was obtained for the ST (p-value<0.001) mainly explained by the adapted position of the non-dominant leg which unlocked the hip joint and generated less muscle activity in the hamstrings. CONCLUSIONS: Muscle activity of the AL muscle and adductor force being similar in both positions, the CA stage 1 modified position could be of interest for rehabilitation after adductor injury or strengthening of the adductors in elite athletes.

Semitendinosus and biceps femoris long head activity during the single leg bridge test in healthy individuals.

INTRODUCTION: The single leg bridge test (SLBT) has been suggested as a clinical test to examine function, screen injury risk, and monitor the effectiveness of rehabilitation programes targeting the hamstring. This study aimed to determine the inter-day reliability and repeatability of both SLBT performance, semitendinosus (ST), and biceps femoris long head (BFlh) surface electromyography (sEMG) responses and characterise the BFlh and ST electrical activity during the SLBT performed until exhaustion in healthy individuals. METHODS: Twelve physically active young men without previous hamstring injury were tested for the number of repetitions attained, and sEMG signal median frequency and amplitude in both ST and BFlh of each lower limb, randomly in two sessions, with a seven-day interval between sessions. RESULTS: High reliability [ICC = 0.85] was found for the number of SLBT repetitions attained. Reliability of sEMG outcomes showed better results for ST (ICC = 0.62-0.91) than for BFlh (ICC = 0.39-0.81), and a high to very-high repeatability was found for both ST (ICC = 0.91-0.84) and BFlh (ICC = 0.91-0.85). sEMG median frequency decreased and amplitude increased for both BFlh (p ≤ 0.001) and ST (p ≤ 0.039) at the end of SLBT, suggesting localised fatigue. CONCLUSIONS: The SLBT performed by healthy individuals until exhaustion proved to be reliable and to induce fatigue in both BFlh and ST, where the sEMG median frequency and amplitude can be measured on different days with acceptable reliability and high repeatability, suggesting its potential future use in both practical and clinical settings.

Posterior thigh muscles activity during the active H-test: An electromyographic and kinematic analysis.

The Askling's H-test is considered a useful return to play criterion after a hamstring muscle injury (HMI). However, it assesses only the active and passive flexibility of posterior thigh muscles. This may lead the practitioner to underestimate a compensation or abnormal movement pattern. The aim of this study was to analyze these kinematic aspects and their reliability, and evaluate the hamstring (HM) and gluteus maximus (GM) muscles' activities. Twelve healthy male volunteers were tested during two session of three trials for passive and active tests. Dynamic flexibility (97.2 ± 6.0°) was significantly greater than the passive one (70.5 ± 14.7°) (p < 0.001), and good intra-individual reproductibility for most kinematic characteristics was observed. Biceps Femoris long head, semitendinosus and GM mean activities (20.1 ± 11.2%; 14.3 ± 7.3% and 25.2 ± 22.1%, respectively) were found to be low to moderate, indicating that only a moderate level of activity occurred during the active H-test, in comparison to other movements such as sprinting itself. In addition, the activity of the posterior thigh muscles during the active H-test appeared to be variable among the volunteers. These findings suggest that the H-test should be interpreted on an individual basis rather than relying on general characteristics, and be considered as an intermediate tool before more strenuous activities such as returning to sprint. With this comprehensive approach, clinicians can gain a more accurate understanding of their patients' progress and make more informed decisions about their readiness to return to play.

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study-a 42-year-old man with left shoulder disarticulation-EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model's ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users.

Portable Facial Expression System Based on EMG Sensors and Machine Learning Models.

One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy computational resources, far from final users or where the dataset was made. On the other side, sensors can capture muscle reactions and respond on the spot, preserving information locally without using robust computers. Therefore, the research subject is the recognition of the six primary human emotions using electromyography sensors in a portable device. They are placed on specific facial muscles to detect happiness, anger, surprise, fear, sadness, and disgust. The experimental results showed that when working with the CortexM0 microcontroller, enough computational capabilities were achieved to store a deep learning model with a classification store of 92%. Furthermore, we demonstrate the necessity of collecting data from natural environments and how they need to be processed by a machine learning pipeline.

Investigation of Motor Learning Effects Using a Hybrid Rehabilitation System Based on Motion Estimation.

Upper-limb paralysis requires extensive rehabilitation to recover functionality for everyday living, and such assistance can be supported with robot technology. Against such a background, we have proposed an electromyography (EMG)-driven hybrid rehabilitation system based on motion estimation using a probabilistic neural network. The system controls a robot and functional electrical stimulation (FES) from movement estimation using EMG signals based on the user's intention, enabling intuitive learning of joint motion and muscle contraction capacity even for multiple motions. In this study, hybrid and visual-feedback training were conducted with pointing movements involving the non-dominant wrist, and the motor learning effect was examined via quantitative evaluation of accuracy, stability, and smoothness. The results show that hybrid instruction was as effective as visual feedback training in all aspects. Accordingly, passive hybrid instruction using the proposed system can be considered effective in promoting motor learning and rehabilitation for paralysis with inability to perform voluntary movements.

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.

Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human-computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed methods for classifying hand gestures. Despite traditional machine learning methods already achieving impressive performance, it is still a huge amount of work to carry out feature extraction manually. The existing deep learning methods utilize complex neural network architectures to achieve higher accuracy, which will suffer from overfitting, insufficient adaptability, and low recognition accuracy. To improve the existing phenomenon, a novel lightweight model named dual stream LSTM feature fusion classifier is proposed based on the concatenation of five time-domain features of EMG signals and raw data, which are both processed with one-dimensional convolutional neural networks and LSTM layers to carry out the classification. The proposed method can effectively capture global features of EMG signals using a simple architecture, which means less computational cost. An experiment is conducted on a public DB1 dataset with 52 gestures, and each of the 27 subjects repeats every gesture 10 times. The accuracy rate achieved by the model is 89.66%, which is comparable to that achieved by more complex deep learning neural networks, and the inference time for each gesture is 87.6 ms, which can also be implied in a real-time control system. The proposed model is validated using a subject-wise experiment on 10 out of the 40 subjects in the DB2 dataset, achieving a mean accuracy of 91.74%. This is illustrated by its ability to fuse time-domain features and raw data to extract more effective information from the sEMG signal and select an appropriate, efficient, lightweight network to enhance the recognition results.

Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance.

Effective feature extraction and selection are crucial for the accurate classification and prediction of hand gestures based on electromyographic signals. In this paper, we systematically compare six filter and wrapper feature evaluation methods and investigate their respective impacts on the accuracy of gesture recognition. The investigation is based on several benchmark datasets and one real hand gesture dataset, including 15 hand force exercises collected from 14 healthy subjects using eight commercial sEMG sensors. A total of 37 time- and frequency-domain features were extracted from each sEMG channel. The benchmark dataset revealed that the minimum Redundancy Maximum Relevance (mRMR) feature evaluation method had the poorest performance, resulting in a decrease in classification accuracy. However, the RFE method demonstrated the potential to enhance classification accuracy across most of the datasets. It selected a feature subset comprising 65 features, which led to an accuracy of 97.14%. The Mutual Information (MI) method selected 200 features to reach an accuracy of 97.38%. The Feature Importance (FI) method reached a higher accuracy of 97.62% but selected 140 features. Further investigations have shown that selecting 65 and 75 features with the RFE methods led to an identical accuracy of 97.14%. A thorough examination of the selected features revealed the potential for three additional features from three specific sensors to enhance the classification accuracy to 97.38%. These results highlight the significance of employing an appropriate feature selection method to significantly reduce the number of necessary features while maintaining classification accuracy. They also underscore the necessity for further analysis and refinement to achieve optimal solutions.

Advanced waveform analysis of diaphragm surface EMG allows for continuous non-invasive assessment of respiratory effort in critically ill patients at different PEEP levels.

BACKGROUND: Respiratory effort should be closely monitored in mechanically ventilated ICU patients to avoid both overassistance and underassistance. Surface electromyography of the diaphragm (sEMGdi) offers a continuous and non-invasive modality to assess respiratory effort based on neuromuscular coupling (NMCdi). The sEMGdi derived electrical activity of the diaphragm (sEAdi) is prone to distortion by crosstalk from other muscles including the heart, hindering its widespread use in clinical practice. We developed an advanced analysis as well as quality criteria for sEAdi waveforms and investigated the effects of clinically relevant levels of PEEP on non-invasive NMCdi. METHODS: NMCdi was derived by dividing end-expiratory occlusion pressure (Pocc) by sEAdi, based on three consecutive Pocc manoeuvres at four incremental (+ 2 cmH2O/step) PEEP levels in stable ICU patients on pressure support ventilation. Pocc and sEAdi quality was assessed by applying a novel, automated advanced signal analysis, based on tolerant and strict cut-off criteria, and excluding inadequate waveforms. The coefficient of variations (CoV) of NMCdi after basic manual and automated advanced quality assessment were evaluated, as well as the effect of an incremental PEEP trial on NMCdi. RESULTS: 593 manoeuvres were obtained from 42 PEEP trials in 17 ICU patients. Waveform exclusion was primarily based on low sEAdi signal-to-noise ratio (Ntolerant = 155, 37%, Nstrict = 241, 51% waveforms excluded), irregular or abrupt cessation of Pocc (Ntolerant = 145, 35%, Nstrict = 145, 31%), and high sEAdi area under the baseline (Ntolerant = 94, 23%, Nstrict = 79, 17%). Strict automated assessment allowed to reduce CoV of NMCdi to 15% from 37% for basic quality assessment. As PEEP was increased, NMCdi decreased significantly by 4.9 percentage point per cmH2O. CONCLUSION: Advanced signal analysis of both Pocc and sEAdi greatly facilitates automated and well-defined identification of high-quality waveforms. In the critically ill, this approach allowed to demonstrate a dynamic NMCdi (Pocc/sEAdi) decrease upon PEEP increments, emphasising that sEAdi-based assessment of respiratory effort should be related to PEEP dependent diaphragm function. This novel, non-invasive methodology forms an important methodological foundation for more robust, continuous, and comprehensive assessment of respiratory effort at the bedside.

A distributed, high-channel-count, implanted bidirectional system for restoration of somatosensation and myoelectric control.

Objective. We intend to chronically restore somatosensation and provide high-fidelity myoelectric control for those with limb loss via a novel, distributed, high-channel-count, implanted system.Approach.We have developed the implanted Somatosensory Electrical Neurostimulation and Sensing (iSens®) system to support peripheral nerve stimulation through up to 64, 96, or 128 electrode contacts with myoelectric recording from 16, 8, or 0 bipolar sites, respectively. The rechargeable central device has Bluetooth® wireless telemetry to communicate to external devices and wired connections for up to four implanted satellite stimulation or recording devices. We characterized the stimulation, recording, battery runtime, and wireless performance and completed safety testing to support its use in human trials.Results.The stimulator operates as expected across a range of parameters and can schedule multiple asynchronous, interleaved pulse trains subject to total charge delivery limits. Recorded signals in saline show negligible stimulus artifact when 10 cm from a 1 mA stimulating source. The wireless telemetry range exceeds 1 m (direction and orientation dependent) in a saline torso phantom. The bandwidth supports 100 Hz bidirectional update rates of stimulation commands and data features or streaming select full bandwidth myoelectric signals. Preliminary first-in-human data validates the bench testing result.Significance.We developed, tested, and clinically implemented an advanced, modular, fully implanted peripheral stimulation and sensing system for somatosensory restoration and myoelectric control. The modularity in electrode type and number, including distributed sensing and stimulation, supports a wide variety of applications; iSens® is a flexible platform to bring peripheral neuromodulation applications to clinical reality. ClinicalTrials.gov ID NCT04430218.

Scapular dyskinesis and overhead athletes: A systematic review of electromyography studies.

OBJECTIVE: Understanding how the main scapular muscles behave in overhead athletes with scapular dyskinesis (SD). DESIGN: Systematic Review. SETTING: Electronic searches were performed in Pubmed (MedLine), Embase, CINAHL, and SPORTDiscus databases. PARTICIPANTS: Overhead athletes with SD. MAIN OUTCOME MEASURES: Electromyographic activity of the upper (UT), middle (MT), and lower (LT) trapezius, and serratus anterior (SA). RESULTS: Eight studies were included in this review. The UT activity showed a tended to increase its activity mainly during tasks over 90° compared to 45°. SA activity had similar behavior, mainly during isometric tasks. The MT also increased its activity mainly in tasks with overhead angulations when compared to lower angulations. The LT activation tended to decrease its EMG activity at angulations below 60° in overhead athletes with SD. CONCLUSIONS: The EMG behaviour of UT and SA for non-athletes appears to differ from what has already been described in the literature. The MT seems to be the most neglected muscle for scapular stabilization in overhead athletes with SD. The decrease in LT activity suggests that this may have implications for the performance of these athletes.

Optimizing Mouse Urodynamic Techniques for Improved Accuracy.

Accurate measurement of urinary parameters in awake mice is crucial for understanding lower urinary tract (LUT) dysfunction, particularly in conditions like neurogenic bladder post-traumatic spinal cord injury (SCI). However, conducting cystometry recordings in mice presents notable challenges. When mice are in a prone and restricted position during recording sessions, urine tends to be absorbed by the fur and skin, leading to an underestimation of voided volume (VV). The goal of this study was to enhance the accuracy of cystometry and external urethral sphincter electromyography (EUS-EMG) recordings in awake mice. We developed a unique method utilizing cyanoacrylate adhesive to create a waterproof skin barrier around the urethral meatus and abdomen, preventing urine absorption and ensuring precise measurements. Results show that after applying the cyanoacrylate, the sum of VV and RV remained consistent with the infused saline volume, and there were no wet areas observed post-experiment, indicating successful prevention of urine absorption. Additionally, the method simultaneously stabilized the electrodes connected with the external urethral sphincter (EUS), ensured stable electromyography (EMG) signals, and minimized artifacts caused by the movement of the awakened mouse and manipulation of the experimenter. Methodological details, results, and implications are discussed, highlighting the importance of improving urodynamic techniques in preclinical research.

Pediatric Intraoperative Electromyographic Responses at the Adductor Pollicis and Flexor Hallucis Brevis Muscles: A Prospective, Comparative Analysis.

BACKGROUND: Peripheral nerve stimulation with a train-of-four (TOF) pattern can be used intraoperatively to evaluate the depth of neuromuscular block and confirm recovery from neuromuscular blocking agents (NMBAs). Quantitative monitoring can be challenging in infants and children due to patient size, equipment technology, and limited access to monitoring sites. Although the adductor pollicis muscle is the preferred site of monitoring, the foot is an alternative when the hands are unavailable. However, there is little information on comparative evoked neuromuscular responses at those 2 sites. METHODS: Pediatric patients undergoing inpatient surgery requiring NMBA administration were studied after informed consent. Electromyographic (EMG) monitoring was performed simultaneously in each participant at the hand (ulnar nerve, adductor pollicis muscle) and the foot (posterior tibial nerve, flexor hallucis brevis muscle). RESULTS: Fifty patients with a mean age of 3.0 ± standard deviation (SD) 2.9 years were studied. The baseline first twitch amplitude (T1) of TOF at the foot (12.46 mV) was 4.47 mV higher than at the hand (P <.0001). The baseline TOF ratio (TOFR) before NMBA administration and the maximum TOFR after antagonism with sugammadex were not different at the 2 sites. The onset time until the T1 decreased to 10% or 5% of the baseline value (T1) was delayed by approximately 90 seconds (both P =.014) at the foot compared with the hand. The TOFR at the foot recovered (TOFR ≥0.9) 191 seconds later than when this threshold was achieved at the hand (P =.017). After antagonism, T1 did not return to its baseline value, a typical finding with EMG monitoring, but the fractional recovery (maximum T1 at recovery divided by the baseline T1) at the hand and foot was not different, 0.81 and 0.77, respectively (P =.68). The final TOFR achieved at recovery was approximately 100% and was not different between the 2 sites. CONCLUSIONS: Although this study in young children demonstrated the feasibility of TOF monitoring, interpretation of the depth of neuromuscular block needs to consider the delayed onset and the delayed recovery of TOFR at the foot compared to the hand. The delay in achieving these end points when monitoring the foot may impact the timing of tracheal intubation and assessment of adequate recovery of neuromuscular block to allow tracheal extubation (ie, TOFR ≥0.9).