Intestinal myoelectrical activity (IMA), which determines bowel mechanical activity, is the result of two components: a low-frequency component [slow wave (SW)] that is always present, and a high-frequency component [spike bursts (SB)] which is associated with bowel contractions. Despite of the diagnostic significance of internal recordings of IMA, clinical application of this technique is limited due to its invasiveness. Thus, surface recording of IMA which is also called electroenterogram (EEnG) could be a solution for noninvasive monitoring of intestinal motility. The aim of our work was to identify slow wave and spike burst activity on surface EEnG in order to quantify bowel motor activity. For this purpose, we conducted simultaneous recordings of IMA in bowel serosa and on abdominal surface of five Beagle dogs in fast state. Surface EEnG was studied in spectral domain and frequency bands for slow wave and spike burst energy were determined. Maximum signal-to-interference ratio (7.5 dB +/- 36%) on SB frequency band was obtained when reducing upper frequency limit of signal analysis. Energy of external EEnG in reduced SB frequency band (2-7.9 Hz) presented a high correlation (0.71 +/- 7%) with internal intensity of contractions. Our results suggest that energy of SB can be quantified on external EEnG which could provide a noninvasive method for monitoring intestinal mechanical activity.

This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.

As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (< 7 days) in women with threatened preterm labor undergoing tocolytic therapy, using both EHG-burst and whole EHG window analyses, by calculating temporal, spectral, and non-linear parameters. Only two non-linear EHG-burst parameters and four whole EHG window analysis parameters were able to distinguish the women who delivered < 7 days from the others, showing that EHG can provide relevant information on the approach of labor, even in women with threatened preterm labor under the effects of tocolytic therapy. The whole EHG window outperformed the EHG-burst analysis and is seen as a step forward in the development of real-time EHG systems able to predict imminent labor in clinical praxis. Graphical abstract The ability of EHG recordings to predict imminent labor (< 7 days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7 days from those who did not.

The conduction velocity and propagation patterns of the electrohysterogram (EHG) provide fundamental information on the electrophysiological condition of the uterus. However, the accuracy of these measurements can be impaired by both the poor spatial selectivity and sensitivity to the relative direction of the contraction propagation associated with conventional disc electrodes. Concentric ring electrodes could overcome these limitations. The aim of this study was to examine the feasibility of picking up surface EHG signals using a new flexible tripolar concentric ring electrode (TCRE), and to compare these signals with conventional bipolar recordings. Simultaneous recording of conventional bipolar signals and bipolar concentric EHG (BC-EHG) were carried out on 22 pregnant women. Signal bursts were characterized and compared. No significant differences were found between the channels in either duration or dominant frequency in the Fast Wave High frequency range. Nonetheless, the high pass filtering effect of the BC-EHG recordings gave lower frequency content between 0.1 and 0.2 Hz. Although the BC-EHG signal amplitude was about 5-7 times smaller than that of bipolar recordings, a similar signal-to-noise ratio was obtained. These results suggest that the flexible TCRE is able to pick up uterine electrical activity and could provide additional information for deducing the uterine electrophysiological condition.

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Study design: This was a cross-sectional study. Objectives: The main objective of this study was to develop and test classification algorithms based on machine learning using accelerometers to identify the activity type performed by manual wheelchair users with spinal cord injury (SCI). Setting: The study was conducted in the Physical Therapy department and the Physical Education and Sports department of the University of Valencia. Methods: A total of 20 volunteers were asked to perform 10 physical activities, lying down, body transfers, moving items, mopping, working on a computer, watching TV, arm-ergometer exercises, passive propulsion, slow propulsion and fast propulsion, while fitted with four accelerometers placed on both wrists, chest and waist. The activities were grouped into five categories: sedentary, locomotion, housework, body transfers and moderate physical activity. Different machine learning algorithms were used to develop individual and group activity classifiers from the acceleration data for different combinations of number and position of the accelerometers. Results: We found that although the accuracy of the classifiers for individual activities was moderate (55-72%), with higher values for a greater number of accelerometers, grouped activities were correctly classified in a high percentage of cases (83.2-93.6%). Conclusions: With only two accelerometers and the quadratic discriminant analysis algorithm we achieved a reasonably accurate group activity recognition system (490%). Such a system with the minimum of intervention would be a valuable tool for studying physical activity in individuals with SCI.

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While progress has been made in design optimization of concentric ring electrodes maximizing the accuracy of the surface Laplacian estimation, it was based exclusively on the negligible dimensions model of the electrode. Recent proof of concept of the new finite dimensions model that adds the radius of the central disc and the widths of concentric rings to the previously included number of rings and inter-ring distances provides an opportunity for more comprehensive design optimization. In this study, the aforementioned proof of concept was developed into a framework allowing direct comparison of any two concentric ring electrodes of the same size and with the same number of rings. The proposed framework is illustrated on constant and linearly increasing inter-ring distances tripolar concentric ring electrode configurations and validated on electrocardiograms from 20 human volunteers. In particular, ratios of truncation term coefficients between the two electrode configurations were used to demonstrate the similarity between the negligible and the finite dimension models analytically (p = 0.077). Laplacian estimates based on the two models were calculated on electrocardiogram data for emulation of linearly increasing inter-ring distances tripolar concentric ring electrode. The difference between the estimates was not statistically significant (p >> 0.05) which is consistent with the analytic result.