Post-insufflation diaphragm contractions in patients receiving various modes of mechanical ventilation.

BACKGROUND: During mechanical ventilation, post-insufflation diaphragm contractions (PIDCs) are non-physiologic and could be injurious. PIDCs could be frequent during reverse-triggering, where diaphragm contractions follow the ventilator rhythm. Whether PIDCs happens with different modes of assisted ventilation is unknown. In mechanically ventilated patients with hypoxemic respiratory failure, we aimed to examine whether PIDCs are associated with ventilator settings, patients' characteristics or both. METHODS: One-hour recordings of diaphragm electromyography (EAdi), airway pressure and flow were collected once per day for up to five days from intubation until full recovery of diaphragm activity or death. Each breath was classified as mandatory (without-reverse-triggering), reverse-triggering, or patient triggered. Reverse triggering was further subclassified according to EAdi timing relative to ventilator cycle or reverse triggering leading to breath-stacking. EAdi timing (onset, offset), peak and neural inspiratory time (Tineuro) were measured breath-by-breath and compared to the ventilator expiratory time. A multivariable logistic regression model was used to investigate factors independently associated with PIDCs, including EAdi timing, amplitude, Tineuro, ventilator settings and APACHE II. RESULTS: Forty-seven patients (median[25%-75%IQR] age: 63[52-77] years, BMI: 24.9[22.9-33.7] kg/m2, 49% male, APACHE II: 21[19-28]) contributed 2 ± 1 recordings each, totaling 183,962 breaths. PIDCs occurred in 74% of reverse-triggering, 27% of pressure support breaths, 21% of assist-control breaths, 5% of Neurally Adjusted Ventilatory Assist (NAVA) breaths. PIDCs were associated with higher EAdi peak (odds ratio [OR][95%CI] 1.01[1.01;1.01], longer Tineuro (OR 37.59[34.50;40.98]), shorter ventilator inspiratory time (OR 0.27[0.24;0.30]), high peak inspiratory flow (OR 0.22[0.20;0.26]), and small tidal volumes (OR 0.31[0.25;0.37]) (all P ≤ 0.008). NAVA was associated with absence of PIDCs (OR 0.03[0.02;0.03]; P < 0.001). Reverse triggering was characterized by lower EAdi peak than breaths triggered under pressure support and associated with small tidal volume and shorter set inspiratory time than breaths triggered under assist-control (all P < 0.05). Reverse triggering leading to breath stacking was characterized by higher peak EAdi and longer Tineuro and associated with small tidal volumes compared to all other reverse-triggering phenotypes (all P < 0.05). CONCLUSIONS: In critically ill mechanically ventilated patients, PIDCs and reverse triggering phenotypes were associated with potentially modifiable factors, including ventilator settings. Proportional modes like NAVA represent a solution abolishing PIDCs.

A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles.

Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.

Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human-machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified.

The Kinematic and Electromyographic Analysis of Roller Skating at Different Speeds on a Treadmill: A Case Study.

Elite athletes in speed roller skates perceive skating to be a more demanding exercise for the groin when compared to other cyclic disciplines, increasing their risk of injury. The objective of this study was to monitor the kinematic and electromyographic parameters of roller speed skaters, linearly, on a treadmill, and to compare different skating speeds, one at 20 km/h and one at 32 km/h, at a 1° inclination. The acquisition was carried out by placing an inertial sensor at the level of the first sacral vertebra, and eight surface electromyographic probes on both lower limbs. The kinematic and electromyographic analysis on the treadmill showed that a higher speed requires more muscle activation, in terms of maximum and average values and co-activation, as it not only increases the intrinsic muscle demand in the district, but also the athlete's ability to coordinate the skating technique. The present study allows us to indicate not only how individual muscle districts are activated during skating on a surface different from the road, but also how different speeds affect the overall district load distributions concerning effective force, which is essential for the physiotherapist and kinesiologist for preventive and conditional purposes, while also considering possible variations in the skating technique in linear advancement.

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

Visual Deprivation's Impact on Dynamic Posture Control of Trunk: A Comprehensive Sensing Information Analysis of Neurophysiological Mechanisms.

Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion-extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future.

PDMS/CNT electrodes with bioamplifier for practical in-the-ear and conventional biosignal recordings.

Objective.Potential usage of dry electrodes in emerging applications such as wearable devices, flexible tattoo circuits, and stretchable displays requires that, to become practical solutions, issues such as easy fabrication, strong durability, and low-cost materials must be addressed. The objective of this study was to propose soft and dry electrodes developed from polydimethylsiloxane (PDMS) and carbon nanotube (CNT) composites.Approach.The electrodes were connected with both conventional and in-house NTAmp biosignal instruments for comparative studies. The performances of the proposed dry electrodes were evaluated through electromyogram, electrocardiogram, and electroencephalogram measurements.Main results.Results demonstrated that the capability of the PDMS/CNT electrodes to receive biosignals was on par with that of commercial electrodes (adhesive and gold-cup electrodes). Depending on the type of stimuli, a signal-to-noise ratio of 5-10 dB range was achieved.Significance.The results of the study show that the performance of the proposed dry electrode is comparable to that of commercial electrodes, offering possibilities for diverse applications. These applications may include the physical examination of vital medical signs, the control of intelligent devices and robots, and the transmission of signals through flexible materials.

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features.

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

The Impact of Different Telerehabilitation Methods on Peripheral Muscle Strength and Aerobic Capacity in COPD Patients: A Randomized Controlled Trial.

Lung diseases have profound effects on the aging population. We aimed to hypothesize and investigate the effect of remote pulmonary telerehabilitation and motor imagery (MI) and action observation (AO) methods on the clinical status of elderly chronic obstructive pulmonary disease (COPD) patients. Twenty-six patients were randomly assigned to pulmonary telerehabilitation (PtR) or cognitive telerehabilitation (CtR) groups. The programs were carried out 3 days a week for 8 weeks. The 6-min walk test (6MWT), modified Medical Research Council dyspnea score, blood lactate level (BLL), measurement of peripheral muscle strength (PMS), and electromyography activation levels of accessory respiratory muscles were the main outcomes. There was a statistically significant improvement (p < 0.05) in both groups in the 6MWT distance and in secondary results, except for BLL. Generally, in the mean muscle activity obtained from the electromyography measurement after the program, there were statistically significant increases in the PtR group and decreases in the CtR group (p < 0.05). There was a statistically significant increase in PMS in both groups. An active muscle-strengthening program has the same benefits as applying the muscle-strengthening program to the patient as MI and AO. CtR can be a powerful alternative rehabilitation method in respiratory patients who cannot tolerate active exercise programs.

Assessment of inter-rater and intra-rater reliability of the Luna EMG robot as a tool for assessing upper limb proprioception in patients with stroke-a prospective observational study.

Background: The aim of the study was to assess the inter-rater and intra-rater agreement of measurements performed with the Luna EMG (electromyography) multifunctional robot, a tool for evaluation of upper limb proprioception in individuals with stroke. Methods: The study was conducted in a group of patients with chronic stroke. A total of 126 patients participated in the study, including 78 women and 48 men, on average aged nearly 60 years (mean = 59.9). Proprioception measurements were performed using the Luna EMG diagnostic and rehabilitation robot to assess the left and right upper limbs. The examinations were conducted by two raters, twice, two weeks apart. The results were compared between the raters and the examinations. Results: High consistency of the measurements performed for the right and the left hand was reflected by the interclass correlation coefficients (0.996-0.998 and 0.994-0.999, respectively) and by Pearson's linear correlation which was very high (r = 1.00) in all the cases for the right and the left hand in both the inter-rater and intra-rater agreement analyses. Conclusions: Measurements performed by the Luna EMG diagnostic and rehabilitation robot demonstrate high inter-rater and intra-rater agreement in the assessment of upper limb proprioception in patients with chronic stroke. The findings show that Luna EMG is a reliable tool enabling effective evaluation of upper limb proprioception post-stroke.

A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography.

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems.

Development of Surface EMG for Gait Analysis and Rehabilitation of Hemiparetic Patients.

BACKGROUND: The quantification of electromyographic activity using surface electrodes is invaluable for understanding gait disorders in patients with central nervous system lesions. We propose to evaluate a commercially available low-cost system compared to a reference system in participants with stroke-related movement disorders in functional situations. METHODS: Three hemiparetic participants performed three functional tasks: two treadmill walks at different speeds and a sit-to-stand test. The vastus lateralis and gastrocnemius medialis muscles were equipped with two EMG sensors. The comparison between the two EMG systems was based on 883 identified cycles. Spearman's correlation coefficients (SCs), linear correlation coefficients (LCCs), and cross-correlation coefficients (CCCs) were calculated. RESULTS: The main results indicate good to very good similarity of the EMG signals collected from the two tested sEMG systems. In the comfortable-walking condition, an SC of 0.894 ± 0.091 and an LCC of 0.909 ± 0.094 were noted. In the fast-walking condition, an SC of 0.918 ± 0.064 and an LCC of 0.935 ± 0.056 were observed. For the 1 min sit-to-stand test, an SC of 0.880 ± 0.058 and an LCC of 0.881 ± 0.065 were noted. CONCLUSIONS: This study demonstrates good to very good similarity between the two sEMG systems, enabling the analysis of muscle activity during functional tasks.

Correlation between Muscular Activity and Vehicle Motion during Double Lane Change Driving.

The aim of this study was to compare the correlation between electromyography (EMG) activity and vehicle motion during double lane change driving. This study measured five vehicle motions: the steering wheel angle, steering wheel torque, lateral acceleration, roll angle, and yaw velocity. The EMG activity for 19 muscles and vehicle motions was applied for envelope detection. There was a significantly high positive correlation between muscles (mean correlation coefficient) for sternocleidomastoid (0.62) and biceps brachii (0.71) and vehicle motions for steering wheel angle, steering wheel torque, lateral acceleration, and yaw velocity, but a negative correlation between the muscles for middle deltoid (-0.75) and triceps brachii long head (-0.78) and these vehicle motions. The ANOVA test was used to analyze statistically significant differences in the main and interaction effects of muscle and vehicle speed. The mean absolute correlation coefficient exhibited an increasing trend with the increasing vehicle speed for the muscles (increasing rate%): upper trapezius (30.5%), pectoralis major sternal (38.7%), serratus anterior (13.3%), and biceps brachii (11.0%). The mean absolute correlation coefficient showed a decreasing trend with increasing vehicle speed for the masseter (-9.6%), sternocleidomastoid (-12.9%), middle deltoid (-5.5%), posterior deltoid (-20.0%), pectoralis major clavicular (-13.4%), and triceps brachii long head (-6.3%). The sternocleidomastoid muscle may decrease with increasing vehicle speed as the neck rotation decreases. As shoulder stabilizers, the upper trapezius, pectoralis major sternal, and serratus anterior muscles are considered to play a primary role in maintaining body balance. This study suggests that the primary muscles reflecting vehicle motions include the sternocleidomastoid, deltoid, upper trapezius, pectoralis major sternal, serratus anterior, biceps, and triceps muscles under real driving conditions.

Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals.

Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. However, incomplete sEMG signals are prevalent on account of unstable data transmission, sensor malfunction, and electrode sheet detachment. These non-ideal factors severely compromise the accuracy of continuous motion recognition and the reliability of clinical applications. To tackle this challenge, this paper develops a multi-task parallel learning framework for continuous motion estimation with incomplete sEMG signals. Concretely, a residual network is incorporated into a recurrent neural network to integrate the information flow of hidden states and reconstruct random and consecutive missing sEMG signals. The attention mechanism is applied for redistributing the distribution of weights. A jointly optimized loss function is devised to enable training the model for simultaneously dealing with signal anomalies/absences and multi-joint continuous motion estimation. The proposed model is implemented for estimating hip, knee, and ankle joint angles of physically competent individuals and patients during diverse exercises. Experimental results indicate that the estimation root-mean-square errors with 60% missing sEMG signals steadily converges to below 5 degrees. Even with multi-channel electrode sheet shedding, our model still demonstrates cutting-edge estimation performance, errors only marginally increase 1 degree.

Hand gesture recognition using sEMG signals with a multi-stream time-varying feature enhancement approach.

Hand gesture recognition based on sparse multichannel surface electromyography (sEMG) still poses a significant challenge to deployment as a muscle-computer interface. Many researchers have been working to develop an sEMG-based hand gesture recognition system. However, the existing system still faces challenges in achieving satisfactory performance due to ineffective feature enhancement, so the prediction is erratic and unstable. To comprehensively tackle these challenges, we introduce a novel approach: a lightweight sEMG-based hand gesture recognition system using a 4-stream deep learning architecture. Each stream strategically combines Temporal Convolutional Network (TCN)-based time-varying features with Convolutional Neural Network (CNN)-based frame-wise features. In the first stream, we harness the power of the TCN module to extract nuanced time-varying temporal features. The second stream integrates a hybrid Long short-term memory (LSTM)-TCN module. This stream extracts temporal features using LSTM and seamlessly enhances them with TCN to effectively capture intricate long-range temporal relations. The third stream adopts a spatio-temporal strategy, merging the CNN and TCN modules. This integration facilitates concurrent comprehension of both spatial and temporal features, enriching the model's understanding of the underlying dynamics of the data. The fourth stream uses a skip connection mechanism to alleviate potential problems of data loss, ensuring a robust information flow throughout the network and concatenating the 4 stream features, yielding a comprehensive and effective final feature representation. We employ a channel attention-based feature selection module to select the most effective features, aiming to reduce the computational complexity and feed them into the classification module. The proposed model achieves an average accuracy of 94.31% and 98.96% on the Ninapro DB1 and DB9 datasets, respectively. This high-performance accuracy proves the superiority of the proposed model, and its implications extend to enhancing the quality of life for individuals using prosthetic limbs and advancing control systems in the field of robotic human-machine interfaces.

Performance of a Novel Muscle Synergy Approach for Continuous Motion Estimation on Untrained Motion.

When applying continuous motion estimation (CME) model based on sEMG to human-robot system, it is inevitable to encounter scenarios in which the motions performed by the user are different from the motions in the training stage of the model. It has been demonstrated that the prediction accuracy of the currently effective approaches on untrained motions will be significantly reduced. Therefore, we proposed a novel CME method by introducing muscle synergy as feature to achieve better prediction accuracy on untrained motion tasks. Specifically, deep non-smooth NMF (Deep-nsNMF) was firstly introduced on synergy extraction to improve the efficiency of synergy decomposition. After obtaining the activation primitives from various training motions, we proposed a redundancy classification algorithm (RC) to identify shared and task-specific synergies, optimizing the original redundancy segmentation algorithm (RS). NARX neural network was set as the regression model for training. Finally, the model was tested on prediction tasks of eight untrained motions. The prediction accuracy of the proposed method was found to perform better than using time-domain feature as input of the network. Through Deep-nsNMF with RS, the highest accuracy reached 99.7%. Deep-nsNMF with RC performed similarly well and its stability was relatively higher among different motions and subjects. Limitation of the approach is that the problem of positive correlation between the prediction error and the absolute value of real angle remains to be further addressed. Generally, this research demonstrates the potential for CME models to perform well in complex scenarios, providing the feasibility of dedicating CME to real-world applications.

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration.

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort. Additionally, it maps the global sEMG research landscape of diverse regional contributors and their impact on technological progress, focusing on the integration of Eastern manufacturing prowess with Western technological innovations and exploring both the opportunities and challenges in this global synergy. The integration of such textile-based sEMG innovations with artificial intelligence, nanotechnology, energy harvesting, and IoT connectivity is also anticipated as future prospects. Such advancements are poised to revolutionize personalized preventive healthcare. As the exploration of textile-based sEMG electrodes continues, the transformative potential not only promises to revolutionize integrated wellness and preventive healthcare but also signifies a seamless transition from laboratory innovations to real-world applications in sports medicine, envisioning the future of truly wearable material technologies.

A comparative study between EMG uroflowmetry with and without a catheter in children.

OBJECTIVES: To evaluate the effect of urethral catheterization on the accuracy of EMG uroflowmetry in children with non-neurogenic voiding disorders during pressure-flow (PF) studies compared to the non-invasive EMG uroflowmetry test. METHODS: A retrospective study of children undergoing a urodynamic evaluation at our institution between 8/2018 and 7/2022 was employed. Urination curves and pelvic floor muscle activity were compared between PF studies and non-invasive EMG uroflowmetry test. The non-invasive test was selected as the standard benchmark. RESULTS: 104 children were tested, with 34 children (33%) being able to urinate only in a non-invasive EMG uroflowmetry. The percentage of boys unable to urinate with a catheter was significantly higher than girls (54% vs. 13%, p-value < 0.001). In 70 children, a normal bell-shaped urination curve was found in 13 compared to 33 children in the PF studies and non-invasive uroflowmetry, respectively. PF studies demonstrated a specificity of 39% (95% CI 23-57) and a positive predictive value (PPV) of 61% (95% CI 53-67) in finding non-bell-shaped curves. Relaxation of pelvic muscles was found in 21 (30%) as opposed to 39 (55%) of children in invasive and non-invasive EMG uroflowmetry, respectively (p-value = 0.5). CONCLUSION: The accuracy of PF studies in children, primarily in boys, compared to the non-invasive uroflowmetry, was poor. This may pose potential errors in diagnosis and subsequent treatment. We recommend completing a non-invasive EMG uroflowmetry in cases where the child refused to urinate, or pathology was found, requiring a modification in treatment.

Differences in cervical sagittal parameters and muscular function among subjects with different cervical spine alignments: a surface electromyography-based cross-sectional study.

Background: We analyzed cervical sagittal parameters and muscular function in different cervical kyphosis types. Methods: This cross-sectional study enrolled subjects with cervical spine lordosis (cervical curvature < -4°) or degenerative cervical kyphosis (cervical curvature > 4°), including C-, S-, and R-type kyphosis. We recorded patients' general information (gender, age, body mass index), visual analog scale (VAS) scores, and the Neck Disability Index (NDI). Cervical sagittal parameters including C2-C7 Cobb angle (Cobb), T1 slope (T1S), C2-C7 sagittal vertical axis (SVA), spino-cranial angle (SCA), range of motion (ROM), and muscular function (flexion-relaxation ratio (FRR) and co-contraction ratio (CCR) of neck/shoulder muscles on surface electromyography). Differences in cervical sagittal parameters and muscular function in subjects with different cervical spine alignments, and correlations between VAS scores, NDI, cervical sagittal parameters, and muscular function indices were statistically analyzed. Results: The FRR of the splenius capitis (SPL), upper trapezius (UTr), and sternocleidomastoid (SCM) were higher in subjects with cervical lordosis than in subjects with cervical kyphosis. FRRSPL was higher in subjects with C-type kyphosis than in subjects with R- and S-type kyphosis (P < 0.05), and was correlated with VAS scores, Cobb angle, T1S, and SVA. FRRUTr was correlated with NDI, SCA, T1S, and SVA. FRRSCM was correlated with VAS scores and Cobb angle. CCR was correlated with SCA and SVA. Conclusion: Cervical sagittal parameters differed among different cervical kyphosis types. FRRs and CCRs were significantly worse in R-type kyphosis than other kyphosis types. Cervical muscular functions were correlated with cervical sagittal parameters and morphological alignment.

Peak amplitude of the normalized power spectrum of the electromyogram of the uterus in the low frequency band is an effective predictor of premature birth.

The current trends in the development of methods for non-invasive prediction of premature birth based on the electromyogram of the uterus, i.e., electrohysterogram (EHG), suggest an ever-increasing use of large number of features, complex models, and deep learning approaches. These "black-box" approaches rarely provide insights into the underlying physiological mechanisms and are not easily explainable, which may prevent their use in clinical practice. Alternatively, simple methods using meaningful features, preferably using a single feature (biomarker), are highly desirable for assessing the danger of premature birth. To identify suitable biomarker candidates, we performed feature selection using the stabilized sequential-forward feature-selection method employing learning and validation sets, and using multiple standard classifiers and multiple sets of the most widely used features derived from EHG signals. The most promising single feature to classify between premature EHG records and EHG records of all other term delivery modes evaluated on the test sets appears to be Peak Amplitude of the normalized power spectrum (PA) of the EHG signal in the low frequency band (0.125-0.575 Hz) which closely matches the known Fast Wave Low (FWL) frequency band. For classification of EHG records of the publicly available TPEHG DB, TPEHGT DS, and ICEHG DS databases, using the Partition-Synthesis evaluation technique, the proposed single feature, PA, achieved Classification Accuracy (CA) of 76.5% (AUC of 0.81). In combination with the second most promising feature, Median Frequency (MF) of the power spectrum in the frequency band above 1.0 Hz, which relates to the maternal resting heart rate, CA increased to 78.0% (AUC of 0.86). The developed method in this study for the prediction of premature birth outperforms single-feature and many multi-feature methods based on the EHG, and existing non-invasive chemical and molecular biomarkers. The developed method is fully automatic, simple, and the two proposed features are explainable.