Blind separation of slow waves and spikes from gastrointestinal myoelectrical recordings

Real-Time Recording and Processing of Spike Electrical Activity of the Small Intestine in Experiments on Rats

Real-time recording technique and mathematical processing of the spike electrical activity in the small intestine were developed for chronic experiments on rats. Open-source software was employed to digitize electromyograms and to process them in a real-time mode with a fourth-order nonlinear differential energy operator. This method improved identification of spike electrical activity in the small intestine in experiments.

Methods for High-Resolution Electrical Mapping in the Gastrointestinal Tract

Over the last two decades, high-resolution (HR) mapping has emerged as a powerful technique to study normal and abnormal bioelectrical events in the gastrointestinal (GI) tract. This technique, adapted from cardiology, involves the use of dense arrays of electrodes to track bioelectrical sequences in fine spatiotemporal detail. HR mapping has now been applied in many significant GI experimental studies informing and clarifying both normal physiology and arrhythmic behaviors in disease states. This review provides a comprehensive and critical analysis of current methodologies for HR electrical mapping in the GI tract, including extracellular measurement principles, electrode design and mapping devices, signal processing and visualization techniques, and translational research strategies. The scope of the review encompasses the broad application of GI HR methods from in vitro tissue studies to in vivo experimental studies, including in humans. Controversies and future directions for GI mapping methodologies are addressed, including emerging opportunities to better inform diagnostics and care in patients with functional gut disorders of diverse etiologies.

Automated algorithm for GI spike burst detection and demonstration of efficacy in ischemic small intestine

We present a novel, fully-automated gastrointestinal spike burst detection algorithm. Following pre-processing with SALPA (Wagenaar and Potter, J. Neurosci. Methods 120:113-120, 2002) and a Savitzky-Golay filter to remove unwanted low and high frequency components, candidate spike waveforms are detected utilizing the non-linear energy operator. Candidate waveforms are classified as spikes or artifact by a support vector machine. The new method achieves highly satisfactory performance with >90% sensitivity and positive prediction value. We also demonstrate an application of the new method to detect changes in spike rate and spatial propagation patterns upon induction of mesenteric ischemia in the small intestine. Spike rates were observed to transiently increase 10-20 fold for a duration of ≈600 s, relative to baseline conditions. In ischemic conditions, spike activity propagation patterns included retrograde-longitudinal wavefronts with occasional spontaneous conduction blocks, as well as self-terminating concentric-circumferential wavefronts. Longitudinal and circumferential velocities were 6.8-8.0 cm/s and 18.7 cm/s, respectively.

Combined method for reduction of high frequency interferences in surface electroenterogram (EEnG)

Surface electroenterogram (EEnG) recording is a novel technique for monitoring intestinal motility non-invasively. However, surface EEnG recordings are contaminated by cardiac activity, the respiratory artefact, movement artefacts and other types of interference. The goal of this work is to remove electrocardiogram (ECG) interference and movement artefacts from surface EEnG by means of a combined method of empirical mode decomposition and independent component analysis. For this purpose, 11 recording sessions were conducted on animal models. In order to quantify the effectiveness of the proposed method, several parameters were calculated from each session: signal-to-ECG interference ratio (S/I), energy over 2 Hz (EF2) which quantifies the intestinal motility index of external EEnG recording and the variation of EF2. The proposed method removes both ECG interference and movement artefacts from surface EEnG, obtaining a significantly higher S/I ratio and considerably reducing the non-physiological variation of EF2. Furthermore, after applying the combined method, the correlation coefficient between internal recording EF2 and surface recording EF2 rises significantly. The proposed method could therefore be a useful tool to reduce high frequency interference in EEnG recording and to provide more robust non-invasive intestinal motility indexes.

Exogenous nitrergic pathway involved in the regulation of gastric myoelectrical activity in dogs

OBJECTIVE

Although the effect of nitric oxide (NO) on gastric motility has been investigated in numerous studies, its effects on gastric slow waves and spike activity which regulate gastric motility remained largely unknown. The aim of this study was to test the hypothesis that NO would impair gastric slow waves by reducing their regularity and amplitude as well as contraction-related spike activity.

MATERIAL AND METHODS

The study required four sessions in 8 dogs, 2 weeks after the implantation of four pairs of electrodes along the greater curvature of the stomach. In each session, saline, L-arginine (L-Arg) (75 mg/kg), NG-nitro-L-arginine methyl ester (L-NAME) (5 mg/kg) or L-Arg + L-NAME was given intravenously (IV) after a 30-min baseline recording in the fasting state. A solid test meal (200 g) was ingested 30 min after the IV injection of one of the medications. Gastric myoelectrical activity was recorded for 30 min at baseline, 30 min after the IV injection and 60 min after the meal.

RESULTS

The frequency, amplitude and rhythmicity of gastric slow waves were not affected by NO. L-NAME significantly increased spike activity in the fasting state but not in the fed state. L-Arg did not reduce the number of spike bursts per minute (NSPM) in the fed state. Postprandially, there was a significant decrease in slow wave frequency but a substantial increase in the strength and frequency of spike activity.

CONCLUSIONS

Exogenous NO has no effect on the frequency, amplitude or regularity of gastric slow waves; inhibition of NO increases spike activity in the fasting state but not in the fed state.

Study on gastric interdigestive pressure activity based on phase space reconstruction and FastICA algorithm

To investigate the features of the gastric interdigestive pressure activity under normal physiological conditions, we have developed the wireless radiotelemetry capsule based on a telemetry technique. Twelve healthy volunteers participated in this study. Pressure activity data which are an important index of gastric motility can be obtained from the wireless radiotelemetry capsule. But the capsule only records single-dimensional pressure time series which may contain a few interdependent components simultaneously. Automated embedding phase space reconstruction algorithm is employed to reconstruct multi-dimensional phase space. Then the dominant and separated component of the gastric contractions is identified using FastICA algorithm. Finally the use of Hilbert Huang transform method for analyzing the characters of gastric motility is investigated. The results show that the proposed method is an effective approach for the analysis of the gastric pressure series.

Partner-matching for the automated identification of reproducible ICA components from fMRI datasets: algorithm and validation

The analysis of functional magnetic resonance imaging (fMRI) data is complicated by the presence of a mixture of many sources of signal and noise. Independent component analysis (ICA) can separate these mixtures into independent components, each of which contains maximal information from a single, independent source of signal, whether from noise or from a discrete physiological or neural system. ICA typically generates a large number of components for each subject imaged, however, and therefore it generates a vast number of components across all of the subjects imaged in an fMRI dataset. The practical implementation of ICA has been limited by the difficulty in discerning which of these many components are spurious and which are reproducible, either within or across individuals of the dataset. We have developed a novel clustering algorithm, termed "Partner-Matching" (PM), which identifies automatically the independent components that are reproducible either within or between subjects. It identifies those components by clustering them according to robust measures of similarity in their spatial configurations either across different subjects of an fMRI dataset, within a single subject scanned across multiple scanning sessions, or within an individual subject scanned across multiple runs within a single scanning session. We demonstrate the face validity of our algorithm by applying it to the analysis of three fMRI datasets acquired in 13 healthy adults performing simple auditory, motor, and visual tasks. From among 50 independent components generated for each subject, our PM algorithm automatically identified, across all 13 subjects, components representing activity within auditory, motor, and visual cortices, respectively, as well as numerous other reproducible components outside of primary sensory and motor cortices, in functionally connected circuits that subserve higher-order cognitive functions, even in these simple tasks.

Spike separation from EEG/MEG data using morphological filter and wavelet transform

In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations.

Effect of ghrelin on gastric myoelectric activity and gastric emptying in rats

Ghrelin is a recently discovered peptide in the endocrine cells of the stomach, which may stimulate gastric motility via the vagal nerve pathway. However, the mechanism of ghrelin-induced changes in gastrointestinal motility has not been clearly defined. The purpose of this study was to investigate the pharmacological effects of ghrelin on gastric myoelectrical activity and gastric emptying in rats, and to investigate whether cholinergic activity is involved in the effects of ghrelin. The study was performed on Sprague-Dawley rats implanted with serosal electrodes for electrogastrographic recording. Gastric slow waves were recorded from fasting rats at baseline and after injection of saline, ghrelin, atropine, or atropine+ghrelin. Gastric emptying of non-caloric liquid was measured by the spectrophotometric method in conscious rats. Intravenous administration of rat ghrelin (20 microg/kg) increased not only dominant frequency, dominant power and regularity of the gastric slow wave but also the gastric emptying rate when compared with the control rats (P<0.01, P<0.05, P<0.05, P<0.001 respectively). These stimulatory actions of ghrelin on both gastric myoelectrical activity and gastric emptying were not fully eliminated by pretreatment with atropine sulphate. These results taken together suggest that ghrelin may play a physiological role in the enteric neurotransmission controlling gastric contractions in rats.

Mapping slow waves and spikes in chronically instrumented conscious dogs: automated on-line electrogram analysis

Myoelectric recordings from the gastrointestinal (GI) tract in conscious animals have been limited in duration and site. Recently, we have implanted 24 electrodes and obtained electrograms from these sites simultaneously (200 Hz sampling rate; 1.1 MB/min data stream). An automated electrogram analysis was developed to process this large amount of data. Myoelectrical recordings from the GI tract often consist of slow wave deflections followed by one or more action potentials (=spike deflections) in the same traces. To analyze these signals, a first module separates the signal into one containing only slow waves and a second one containing only spikes. The timings of these waveforms were then detected, in real time, for all 24 electrograms, in a separate slow wave detection module and a separate spike-detection module. Basic statistics such as timing and amplitudes and the number of spikes per slow wave were performed and displayed on-line. In summary, with this online analysis, it is possible to study for long periods of time and under various experimental conditions major components of gastrointestinal motility.

Magnetogastrographic detection of gastric electrical response activity in humans

The detection and characterization of gastric electrical activity has important clinical applications, including the early diagnosis of gastric diseases in humans. In mammals, this phenomenon has two important features: an electrical control activity (ECA) that manifests itself as an electric slow wave (with a frequency of 3 cycles per minute in humans) and an electrical response activity (ERA) that is characterized by spiking potentials during the plateau phase of the ECA. Whereas the ECA has been recorded in humans both invasively and non-invasively (magnetogastrography-MGG), the ERA has never been detected non-invasively in humans before. In this paper, we report on our progress towards the non-invasive detection of ERA from the human stomach using a procedure that involves the application of principal component analysis to MGG recordings, which were acquired in our case from ten normal human patients using a Superconducting QUantum Interference Device (SQUID) magnetometer. Both pre- and post-prandial recordings were acquired for each patient and 20 min of recordings (10 min of pre-prandial and 10 min of post-prandial data) were analysed for each patient. The mean percentage of ECA slow waves that were found to exhibit spikes of suspected ERA origin was 41% and 61% for pre- and post-prandial recordings, respectively, implying a 47% ERA increase post-prandially (P < 0.0001 at a 95% confidence level). The detection of ERA in humans is highly encouraging and points to the possible use of non-invasive ERA recordings as a valuable tool for the study of human gastric disorders.

Artifact reduction in magnetogastrography using fast independent component analysis

The analysis of magnetogastrographic (MGG) signals has been limited to epochs of data with limited interference from extraneous signal components that are often present and may even dominate MGG data. Such artifacts can be of both biological (cardiac, intestinal and muscular activities, motion artifacts, etc) and non-biological (environmental noise) origin. Conventional methods-such as Butterworth and Tchebyshev filters-can be of great use, but there are many disadvantages associated with them as well as with other typical filtering methods because a large amount of useful biological information can be lost, and there are many trade-offs between various filtering methods. Moreover, conventional filtering cannot always fully address the physicality of the signal-processing problem in terms of extracting specific signals due to particular biological sources of interest such as the stomach, heart and bowel. In this paper, we demonstrate the use of fast independent component analysis (FICA) for the removal of both biological and non-biological artifacts from multi-channel MGG recordings acquired using a superconducting quantum intereference device (SQUID) magnetometer. Specifically, we show that the signal of gastric electrical control activity (ECA) can be isolated from SQUID data as an independent component even in the presence of severe motion, cardiac and respiratory artifacts. The accuracy of the method is analyzed by comparing FICA-extracted versus electrode-measured respiratory signals. It is concluded that, with this method, reliable results may be obtained for a wide array of magnetic recording scenarios.

Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancement

The electrogastrogram (EGG), a cutaneous measurement of gastric electrical activity, is a mixture of gastric slow waves and noise. To detect the propagation of gastric slow waves, it is desired to obtain gastric slow waves in each of multichannel EGGs. Recently, independent component analysis (ICA) has shown its efficiency in separating the gastric slow wave from noisy multichannel EGGs. However, this method is not able to recover gastric slow waves in each of the multichannel EGGs. In this paper, a two-stage combined method was proposed for extracting gastric slow waves. First, ICA was performed to separate the gastric slow wave component from noisy multichannel EGGs. Second, adaptive signal enhancement with a reference input derived by the ICA in the first stage was employed to extract gastric slow waves in each channel. Quantitative analysis showed that, with the proposed method, the maximum root-mean-square error between the estimated time lag and its theoretical value in the simulations was only 0.65. The results from real EGG data demonstrated that the combined method was able to extract gastric slow waves from individual channels of EGGs which are important to identify the slow wave propagation. Therefore, the proposed method can be used to detect propagation of gastric slow waves from multichannel EGGs.

Robust time delay estimation of bioelectric signals using least absolute deviation neural network

The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum L1-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of L1-norm model superior to Lp-norm (p > 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC).

Effects of enhanced viscosity on canine gastric and intestinal motility

BACKGROUND AND AIM

The aim of this study was to investigate the effect of enhanced viscosity on gastric emptying and gastrointestinal motor and myoelectrical activities in dogs.

METHOD

The study was performed in eight healthy female hound dogs chronically implanted with four pairs of gastric and two pairs of intestinal serosal electrodes and a duodenal fistula. Each dog was studied in three sessions and fed with three test meals with different viscosity. Gastric emptying was monitored for 2 h simultaneously with gastric and intestinal myoelectrical recordings.

RESULTS

The liquid test meal containing either 0.78% or 1.21% of galactomannan significantly delayed gastric emptying but had no effect on postprandial blood glucose levels in comparison with the meal containing no galactomannan. The liquid test meal containing either 0.78% or 1.21% of galactomannan significantly increased the frequency and strength of intestinal motility but had no effect on intestinal slow wave rhythms. The product with enhanced viscosity had no effect on gastric motor activity or gastric slow waves.

CONCLUSION

It was concluded that enhanced viscosity delays gastric emptying, increases postprandial intestinal but not gastric motility, and has no effects on gastric or intestinal slow waves.

Chaotic Behavior of Gastric Migrating Myoelectrical Complex

Nonlinear feedback induces oscillation, whereas dynamic equilibrium between positive and negative nonlinear feedback generates rhythm. Physiological rhythms are central to life. No absolutely stable or periodic rhythm exists in living tissues. It has been extensively reported that many rhythms in human and animal organs, such as the heart and brain, are, in fact chaotic. The aim of this paper is to investigate whether the migrating myoelectrical complex (MMC) of the stomach was chaotic. The study was performed in eight healthy female hound dogs (15-22 kg), implanted with four pairs of bipolar electrodes on the serosa of the stomach along the greater curvature. After the dogs were completely recovered from the surgery, one complete cycle of gastric MMC was recorded from the serosal electrodes. Using Takens' embedding theorem, two parameters reflecting chaotic behavior, the attractor and the Lyapunov exponent of the myoelectrical recording, were reconstructed and computed, respectively. Statistical analysis was performed to investigate the difference in the Lyapunov exponents among different phases of the MMC. The results show that the MMC of the stomach is chaotic. Different phases of the MMC are characterized with different shapes of the attractors and different values of Lyapunov exponents. The characteristic chaotic behavior of the gastric MMC may be utilized for the identification of different phases.

Detection of gastric slow wave uncoupling from multi-channel electrogastrogram: validations and applications

Current methodology of single channel electrogastrography is unable to detect coupling or uncoupling of gastric slow waves, which is crucial for gastric emptying. In this study, a new methodology, called cross-spectral analysis method, was established to compute the coupling percentage of multi-channel gastric slow waves recorded using serosal electrodes and electrogastrogram (EGG). Two experiments were performed to validate the method and demonstrate its applications in clinical research. In experiment 1, simultaneous recordings of gastric slow waves were made in five dogs from serosal electrodes and cutaneous electrodes. In experiment 2, four-channel fasting EGGs were made in 10 volunteers for 30 min during waking and 30 min during non-rapid eye movement (REM) sleep. The validation study (experiment 1) showed that the slow wave coupling calculated from the EGGs was correlated with that computed from the serosal recordings. The gastric slow wave coupling percentages detected from both serosal and cutaneous recordings were significantly impaired during vasopressin infusion (6.3 +/- 2.6 vs 62.4 +/- 6.3, P < 0.001 for serosal recordings; 6.7 +/- 3.0 vs 57.2 +/- 2.7, P < 0.001 for cutaneous recordings), and the coupling percentages respectively calculated from serosal and cutaneous recordings were significantly correlated during the baseline recording period (R = 0.922, P < 0.05) and vasopressin infusion period (R = 0.916, P < 0.05). In experiment 2, the gastric slow wave became less coupled when healthy volunteers fell asleep. The percentage of slow wave coupling calculated from the EGGs was 68.2 +/- 17.9% during waking but 41.9 +/- 20.8 during non-REM sleep (P < 0.05). The method developed in this study is reliable for the detection of slow wave uncoupling from multi-channel EGGs. Gastric slow wave coupling is impaired during vasopressin infusion and sleep. These data suggest that this method has potential applications in physiological and clinical studies.

Collagen fiber angle in the submucosa of small intestine and its application in gastroenterology

AIM: To propose a simple and effective method suitable for analyzing the angle and distribution of 2-dimensional collagen fiber in larger sample of small intestine and to investigate the relationship between the angles of collagen fiber and the pressure it undergoes.

METHODS: A kind of 2-dimensional visible quantitative analyzing technique was described. Digital image-processing method was utilized to determine the angle of collagen fiber in parenchyma according to the changes of area analyzed and further to investigate quantitatively the distribution of collagen fiber. A series of intestinal slice’s images preprocessed by polarized light were obtained with electron microscope, and they were processed to unify each pixel. The approximate angles between collagen fibers were obtained via analyzing the images and their corresponding polarized light. The relationship between the angles of collagen fiber and the pressure it undergoes were statistically summarized.

RESULTS: The angle of collagen fiber in intestinal tissue was obtained with the quantitative analyzing method of calculating the ratio of different pixels. For the same slice, with polarized light angle’s variation, the corresponding ratio of different pixels was also changed; for slices under different pressures, the biggest ratio of collagen fiber area was changed either.

CONCLUSION: This study suggests that the application of stress on the intestinal tissue will change the angle and content of collagen fiber. The method of calculating ratios of different pixel values to estimate collagen fiber angle was practical and reliable. The quantitative analysis used in the present study allows a larger area of soft tissue to be analyzed with relatively low cost and simple equipment.

Electroacupuncture accelerates gastric emptying in association with changes in vagal activity

Gastroparesis is a disorder with a lack of treatment options and this study investigated the effect of electroacupuncture on gastric emptying and involved mechanisms. Dogs implanted with a duodenal cannula and serosal electrodes were fed with Ensure mixed with phenol red, and the gastric effluent was collected. Electroacupuncture was performed from 30 min before until 45 min after the meal. Gastric myoelectrical activity and electrocardiogram were recorded. Gastric emptying was significantly improved with electroacupuncture. Vagal activity assessed from the spectral analysis of heart rate variability was markedly increased with electroacupuncture. Electroacupuncture increased the regularity of gastric slow waves in both the proximal and distal stomach. It also increased the number of spike bursts in the distal but not proximal stomach. Electroacupuncture accelerates gastric emptying of liquid in dogs and its potential for treating gastroparesis may be explored. The effect may be attributed to improvement in gastric slow-wave rhythmicity and antral contractile (spike) activity and may possibly involve the vagal pathway.