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.

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An external electroenterogram (EEnG) is the recording of the small bowel myoelectrical signal using contact electrodes placed on the abdominal surface. It is a weak signal affected by possible movements and by the interferences of respiration and, principally, of the cardiac signal. In this paper an adaptive filtering technique was proposed to identify and subsequently cancel ECG interference on canine surface EEnGs by means of a signal averaging process time-locked with the R-wave. Twelve recording sessions were carried out on six conscious dogs in the fasting state. The adaptive filtering technique used increases the signal-to-interference ratio of the raw surface EEnG from 16.7 +/- 6.5 dB up to 31.9 +/- 4.0 dB. In addition to removing ECG interference, this technique has been proven to respect intestinal SB activity, i.e. the EEnG component associated with bowel contractions, despite the fact that they overlap in the frequency domain. In this way, more robust non-invasive intestinal motility indicators can be obtained with correlation coefficients of 0.68 +/- 0.09 with internal intestinal activity. The method proposed here may also be applied to other biological recordings affected by cardiac interference and could be a very helpful tool for future applications of non-invasive recordings of gastrointestinal signals.

The electroenterogram (EEnG) is a surface recording of the myoelectrical activity of the smooth muscle layer of the small intestine. It is made up of two signals: a low-frequency component, known as the slow wave (SW), and high-frequency signals, known as spike bursts (SB). Most methods of studying bowel motility are invasive due to the difficult anatomic access of the intestinal tract. Abdominal surface EEnG recordings could be a noninvasive solution for monitoring human intestinal motility. However, surface EEnG recordings in humans present certain problems, such as the low amplitude of the signals and the influence of physiological interference such as the electrocardiogram (ECG) and respiration. In this study, a discrete estimation of the abdominal surface Laplacian potential was obtained using Hjorth's method. The objective was to analyze the enhancement given by Laplacian EEnG estimation compared to bipolar recordings. Eight recording sessions were carried out on eight healthy human volunteers in a state of fasting. First, the ECG interference content present in the bipolar signals and in the Laplacian estimation were quantified and compared. Secondly, to identify the SW component of the EEnG, respiration interference was removed by using an adaptive filter, and spectral estimation techniques were applied. The following parameters were obtained: the dominant frequency (DF) of the signals, stability of the rhythm (RS) of the DF detected and the percentage of DFs within the typical frequency range for the SW (TFSW). Results show the better ability of the Laplacian estimation to attenuate ECG interference, as compared to bipolar recordings. As regards the identification of the SW component of the EEnG, after removing respiration interference, the mean value of the DF in all abdominal surface recording channels and in their Laplacian estimation ranged from 0.12 to 0.14 Hz (7.3 to 8.4 cycles min(-1) (cpm)). Furthermore in 80% of the cases, the detected DFs were inside the typical human SW frequency range, and the ratio of frequency change in the surface bipolar and Laplacian estimation signals, in 90% of the cases, was within the frequency change accepted for human SW. Significant statistical differences were also found between the DF of all surface signals (bipolar and Laplacian estimation) and the DF of respiration. In conclusion, it was demonstrated that the discrete Laplacian potential estimation attenuated the physiological interference present in bipolar surface recordings, especially ECG. Furthermore, a slow frequency component, whose frequency, rhythm stability and amplitude fitted with the SW patterns in humans, was identified in bipolar and Laplacian estimation signals. This could be a useful non-invasive tool for monitoring intestinal activity by abdominal surface recordings.

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

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