Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., ${\mathrm{KR}}_2$ and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

Electrocardiography (ECG) measurements are essential components in clinical diagnosis and monitoring. Conventional ECG measurements produce discomfort to the patients due to the use of gel and direct skin contact. Capacitive electrodes can measure ECG signals through an isolation layer; they are especially suitable for long-term ambulatory monitoring. However, capacitive ECG measurements are severely affected by motion artifacts (MAs) due to variable coupling distance. Adaptive filtering has been widely used for MA reduction in ECG measurements. Unfortunately, a reference signal recorded by additional sensors is required for the existing adaptive-filtering methods, limiting their applicability in ambulatory settings. In this paper on capacitive ECG recordings, a novel adaptive-filtering method is proposed for MA removal, where the reference signal is extracted from the power-line interference (PLI). PLI is particularly evident in a capacitive ECG due to unbalanced coupling capacitance. Along with MAs, electrode movement causes variations in the PLI amplitude. By demodulating the PLI, a reference signal reflecting variations in the coupling capacitance can be extracted for adaptive MA removal. The proposed method was evaluated both by simulations and real data and compared with the acceleration-based adaptive-filtering method. Comparable or higher ECG signal-to-noise ratio was achieved by the proposed method with a computational cost of 79 μs/iteration, indicating effective MA removal. The proposed method may, therefore, lead to improved analysis of capacitive ECG signals in ambulatory settings.

Vibration exercise (VE) has been suggested as an effective methodology to improve muscle strength and power performance. Several studies link the effects of vibration training to enhanced neuromuscular demand, typically ascribed to involuntary reflex mechanisms. However, the underlying mechanisms are still unclear, limiting the identification of the most appropriate vibration training protocols. This study concerns the realization of a new vibration exercise system for the upper limbs. Amplitude, frequency, and baseline of the vibrating force, which is generated by an electromechanical actuator, can be adjusted independently. A second order model is employed to identify the relation between the generated force and the input voltage driving the actuator. Our results show a high correlation (0.99) between the second order model fit and the measured data, ensuring accurate control on the supplied force. The level of neuromuscular demand imposed by the system on the targeted muscles can be estimated by electromyography (EMG). However, EMG measurements during VE can be severely affected by motion artifacts. An adaptive least mean square algorithm is proposed to remove motion artifacts from the measured EMG data. Preliminary validation with seven volunteers showed excellent motion artifact removal, enabling reliable evaluation of the neuromuscular activation.

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.

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.