A system and method for online high-resolution mapping of gastric slow-wave activity

A framework for the design of a closed-loop gastric pacemaker for treating conduction block

BACKGROUND AND OBJECTIVE

Gastrointestinal (GI) motility disorders can be significantly detrimental to the quality of life. Pacing, or long pulse gastric electrical stimulation, is a potential treatment option for treating GI motility disorders by modulating the slow wave activity. Open-loop pacing of the GI tract is the current standard for modulating dysrhythmic patterns, but it is known to be suboptimal and inefficient. Recent work on sensing intracellular potentials and pacing accordingly in a closed-loop has been shown to be effective at modulating dysrhythmic patterns. However, capturing intracellular potentials in an in-vivo setting is not viable. Therefore a closed-loop gastric electrical stimulation that can sense extracellular potentials and pace accordingly to modulate dysrhythmic patterns is required. This paper presents a closed-loop Gastric Electrical Stimulator (GES) design framework, which comprises of extracellular potential generation, sensing, and closed-loop actuation.

METHODS

This work leverages a pre-existing high-fidelity two-dimensional Interstitial Cells of Cajal (ICC) network modeling framework to mimic several normal and dysrhythmic patterns observed in experimental recordings of patients suffering from GI tract diseases. The activation patterns of the of the ICC network are captured by an extracellular potential generation model and is integrated with the GES in a closed-loop to validate the efficacy of the developed pacing algorithms. The proposed GES pacing algorithms extend existing offline filtering and activation detection methods to process the sensed extracellular potentials in real time. The GES detects bradygastric rhythms based on the sensed extracellular potentials and actuates the ICC network via pacing to rectify dysrhythmic patterns.

RESULTS

The proposed GES model is able to sense and process the generated noisy extracellular potentials, detect the bradygastric patterns, and modulate the slow wave activities to normal propagation effectively.

CONCLUSIONS

A closed-loop GES design, which can be applied in an experimental and clinical setting is developed and validated through the ICC network model. The proposed GES model has the ability to modulate a variety of bradygastric patterns, including conduction block effectively in a closed-loop.

Quantification of Gastric Slow Wave Velocity using Bipolar High-Resolution Recordings

OBJECTIVE

Gastric bio-electrical slow waves are, in part, responsible for coordinating motility. High-resolution (HR) in vivo recordings can be used to capture the wavefront velocity of the propagating slow waves. A standard marking-and-grouping approach is typically employed along with manual review. Here, a bipolar velocity estimation (BVE) method was developed, which utilized local directional information to estimate the wavefront velocity in an efficient manner.

METHODS

With this approach, unipolar in vivo HR recordings were used to construct bipolar recordings in different directions. Then, the local directionality of the slow wave was extracted by calculating time delay information. The accuracy of the method was verified using synthetic data and then validated with in vivo HR pig experimental recordings.

RESULTS

Against ventilator noise amplitude of 0% - 70% of the average slow wave amplitude, the direction and speed error increased from 4.4 and 0.9 mm/s to 8.6 and 1.4 mm/s. For signals added with high-frequency noise with signal-to-noise ratios of 60 dB - 12 dB, the error increased from 8.0 and 1.0 mm/s to 9.8 and 1.2 mm/s. For experimental signals, the BVE algorithm resulted in 19.2 1.7 of direction error and 2.0 0.2 mm/s of speed error, when compared to the standard marking-and-grouping method.

CONCLUSION

Gastric slow wave wavefront velocities were estimated rapidly using the BVE algorithm with minimal errors.

SIGNIFICANCE

The BVE algorithm enables the ability to estimate wavefront velocities in HR recordings in an efficient manner.

Multi-day, multi-sensor ambulatory monitoring of gastric electrical activity

OBJECTIVE

Bioelectrial signals known as slow waves play a key role in coordinating gastric motility. Slow wave dysrhythmias have been associated with a number of functional motility disorders. However, there have been limited human recordings obtained in the consious state or over an extended period of time. This study aimed to evaluate a robust ambulatory recording platform.

APPROACH

A commercially available multi-sensor recording system (Shimmer3, ShimmerSensing) was applied to acquire slow wave information from the stomach of six humans and four pigs. First, acute experiments were conducted in pigs to verify the accuracy of the recording module by comparing to a standard widely employed electrophysiological mapping system (ActiveTwo, BioSemi). Then, patients with medically refractory gastroparesis undergoing temporary gastric stimulator implantation were enrolled and gastric slow waves were recorded from mucosally-implanted electrodes for 5 d continuously. Accelerometer data was also collected to exclude data segments containing excessive patient motion artefact.

MAIN RESULTS

Slow wave signals and activation times from the Shimmer3 module were closely comparable to a standard electrophysiological mapping system. Slow waves were able to be recorded continuously for 5 d in human subjects. Over the 5 d, slow wave frequency was 2.8  ±  0.6 cpm and amplitude was 0.2  ±  0.3 mV.

SIGNIFICANCE

A commercial multi-sensor recording module was validated for recording electrophysiological slow waves for 5 d, including in ambulatory patients. Multiple modules could be used simultaneously in the future to track the spatio-temporal propagation of slow waves. This framework can now allow for patho-electrophysiological studies to be undertaken to allow symptom correlation with dysrhythmic slow wave events.

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.

Simultaneous anterior and posterior serosal mapping of gastric slow-wave dysrhythmias induced by vasopressin

NEW FINDINGS

What is the central question of this study? This study aimed to provide the first comparison of simultaneous high-resolution mapping of anterior and posterior gastric serosa over sustained periods. What is the main finding and its importance? Episodes of spontaneous gastric slow-wave dysrhythmias increased significantly following intravenous infusion of vasopressin compared with the baseline state. A number of persistent dysrhythmias were defined, including ectopic activation, conduction block, rotor, retrograde and collision/merger of wavefronts. Slow-wave dysrhythmias could occur either simultaneously or independently on the anterior and posterior gastric serosa, and interacted depending on activation-repolarization and frequency dynamics. High-resolution mapping enables mechanistic insights into gastric slow-wave dysrhythmias and is now achieving clinical translation. However, previous studies have focused mainly on dysrhythmias occurring on the anterior gastric wall. The present study simultaneously mapped the anterior and posterior gastric serosa during episodes of dysrhythmias induced by vasopressin to aid understanding of dysrhythmia initiation, maintenance and termination. High-resolution mapping (8 × 16 electrodes on each serosa; 20-74 cm2 ) was performed in anaesthetized dogs. Baseline recordings (21 ± 8 min) were followed by intravenous infusion of vasopressin (0.1-0.5 IU ml-1 at 60-190 ml h-1 ) and further recordings (22 ± 13 min). Slow-wave activation maps, amplitudes, velocity, interval and frequency were calculated, and differences compared between baseline and postinfusion. All dogs demonstrated an increased prevalence of dysrhythmic events following infusion of vasopressin (17 versus 51%). Both amplitude and velocity demonstrated significant differences (baseline versus postinfusion: 3.6 versus 2.2 mV; 7.7 versus 6.5 mm s-1 ; P < 0.05 for both). Dysrhythmias occurred simultaneously or independently on the anterior and posterior serosa, and then interacted according to frequency dynamics. A number of persistent dysrhythmias were compared, including the following: ectopic activation (n = 2 animals), conduction block (n = 1), rotor (n = 2), retrograde (n = 3) and collision/merger of wavefronts (n = 2). We conclude that infusion of vasopressin induces gastric dysrhythmias, which occur across a heterogeneous range of frequencies and patterns. The results demonstrate that different classes of gastric dysrhythmias may arise simultaneously or independently in one or both surfaces of the serosa, then interact according to their relative frequencies. These results will help to inform interpretation of clinical dysrhythmia.

Acute Slow Wave Responses to High-Frequency Gastric Electrical Stimulation in Patients With Gastroparesis Defined by High-Resolution Mapping

BACKGROUND AND AIMS

High-frequency gastric electrical stimulation (GES) has emerged as a therapy for gastroparesis, but the mechanism(s) of action remain unclear. There is a need to refine stimulation protocols for clinical benefit, but a lack of accurate techniques for assessing mechanisms in clinical trials, such as slow wave modulation, has hindered progress. We thereby aimed to assess acute slow wave responses to GES in gastroparesis patients using high-resolution (HR) (multi-electrode) mapping, across a range of stimulation doses achievable by the Enterra stimulation device (Medtronic Inc., MN, USA).

MATERIALS AND METHODS

Patients with medically refractory gastroparesis (n = 8) undergoing device implantation underwent intraoperative HR mapping (256 electrodes). Baseline recordings were followed by four protocols of increasing stimulation intensity, with washout periods. Slow wave patterns, frequency, velocity, amplitude, and dysrhythmia rates were quantified by investigators blinded to stimulation settings.

RESULTS

There was no difference in slow wave pattern, frequency, velocity, or amplitude between baseline, washout, and stimulation periods (all p > 0.5). Dysrhythmias included ectopic pacemakers, conduction blocks, retrograde propagation, and colliding wavefronts, and dysrhythmia rates were unchanged with stimulation off vs. on (31% vs. 36% duration dysrhythmic; p > 0.5). Symptom scores and gastric emptying were improved at 5.8 month follow-up (p < 0.05).

CONCLUSIONS

High-frequency GES protocols achievable from a current commercial device did not acutely modulate slow wave activity or dysrhythmias. This study advances clinical methods for identifying and assessing therapeutic GES parameters, and can be applied in future studies on higher-energy protocols and devices.

Time-Delay Mapping of High-Resolution Gastric Slow-Wave Activity

GOAL

Analytic monitoring of electrophysiological data has become an essential component of efficient and accurate clinical care. In the gastrointestinal (GI) field, recent advances in high-resolution (HR) mapping are now providing critical information about spatiotemporal profiles of slow-wave activity in normal and disease (dysrhythmic) states. The current approach to analyze GI HR electrophysiology data involves the identification of individual slow-wave events in the electrode array, followed by tracking and clustering of events to create a spatiotemporal map. This method is labor and computationally intensive and is not well suited for real-time clinical use or chronic monitoring.

METHODS

In this study, an automated novel technique to assess propagation patterns was developed. The method utilized time delays of the slow-wave signals which was computed through cross correlations to calculate velocity. Validation was performed with both synthetic and human and porcine experimental data.

RESULTS

The slow-wave profiles computed via the time-delay method compared closely with those computed using the traditional method (speed difference: 7.2% ± 2.6%; amplitude difference: 8.6% ± 3.5%, and negligible angle difference).

CONCLUSION

This novel method provides rapid and intuitive analysis and visualization of slow-wave activity.

SIGNIFICANCE

This techniques will find major applications in the clinical translation of acute and chronic HR electrical mapping for motility disorders, and act as a screening tool for detailed detection and tracking of individual propagating wavefronts, without the need for comprehensive standard event-detection analysis.

Iterative Covariance-Based Removal of Time-Synchronous Artifacts: Application to Gastrointestinal Electrical Recordings

OBJECTIVE

The aim of this study was to develop, validate, and apply a fully automated method for reducing large temporally synchronous artifacts present in electrical recordings made from the gastrointestinal (GI) serosa, which are problematic for properly assessing slow wave dynamics. Such artifacts routinely arise in experimental and clinical settings from motion, switching behavior of medical instruments, or electrode array manipulation.

METHODS

A novel iterative Covariance-Based Reduction of Artifacts (COBRA) algorithm sequentially reduced artifact waveforms using an updating across-channel median as a noise template, scaled and subtracted from each channel based on their covariance.

RESULTS

Application of COBRA substantially increased the signal-to-artifact ratio (12.8 ± 2.5 dB), while minimally attenuating the energy of the underlying source signal by 7.9% on average ( -11.1 ± 3.9 dB).

CONCLUSION

COBRA was shown to be highly effective for aiding recovery and accurate marking of slow wave events (sensitivity = 0.90 ± 0.04; positive-predictive value = 0.74 ± 0.08) from large segments of in vivo porcine GI electrical mapping data that would otherwise be lost due to a broad range of contaminating artifact waveforms.

SIGNIFICANCE

Strongly reducing artifacts with COBRA ultimately allowed for rapid production of accurate isochronal activation maps detailing the dynamics of slow wave propagation in the porcine intestine. Such mapping studies can help characterize differences between normal and dysrhythmic events, which have been associated with GI abnormalities, such as intestinal ischemia and gastroparesis. The COBRA method may be generally applicable for removing temporally synchronous artifacts in other biosignal processing domains.

Extending the automated gastrointestinal analysis pipeline: Removal of invalid slow wave marks in gastric serosal recordings

Gastric contractions are governed by a bioelectrical event known as slow waves. High-resolution electrical mapping has recently been applied to study complex gastric slow wave spatiotemporal propagations in detail. As these methods are translated to clinical and experimental applications, it is evident that efficient and automated methods are a necessity for analysis. Despite automated methods to detect slow wave events, manual review and correction remains necessary due to the presence of experimental noise in the recordings. Manual deletion of invalid slow wave events is time consuming and inefficient. We have therefore developed an algorithm to eliminate invalid markers of slow waves, via the use of frequency and morphological analysis. The techniques were validated with experimental data using serosal gastric slow wave recordings from animals and humans with a sensitivity of 90% and specificity of 85%. It is anticipated these methods will facilitate analyzing high-resolution slow wave mapping data and accelerate clinical translation of electrical mapping to clinical and diagnostic gastroentrology.

Detection of the Recovery Phase of in vivo gastric slow wave recordings

Gastric motility is coordinated by bio-electrical events known as slow waves. Abnormalities in slow waves are linked to major functional and motility disorders. In recent years, the use of high-resolution (HR) recordings have provided a unique view of spatiotemporal activation profiles of normal and dysrhythmic slow wave activity. To date, in vivo studies of gastric slow wave activity have primarily focused on the activation phase of the slow wave event. In this study, the recovery phase of slow waves was investigated through the use of HR recording techniques. The recovery phase of the slow wave event was detected through the use of the signal derivative, computed via a wavelet transform. The activation to recovery interval (ARi) metric was computed as a difference between the recovery time and activation time. The detection method was validated with synthetic slow wave signals of varying morphologies with the addition of synthetic ventilator and high frequency noise. The methods was then applied to HR experimental porcine gastric slow wave recordings. Ventilator noise more than 10% of the slow wave amplitude affected the estimation of the ARi metric. Signal to noise ratio below 3 dB affected the ARi metric, but with minor deviation in accuracy. Experimental ARi values ranged from 3.7-4.7 s from three data sets, with significant differences across them.

The virtual intestine: in silico modeling of small intestinal electrophysiology and motility and the applications

The intestine comprises a long hollow muscular tube organized in anatomically and functionally discrete compartments, which digest and absorb nutrients and water from ingested food. The intestine also plays key roles in the elimination of waste and protection from infection. Critical to all of these functions is the intricate, highly coordinated motion of the intestinal tract, known as motility, which is coregulated by hormonal, neural, electrophysiological and other factors. The Virtual Intestine encapsulates a series of mathematical models of intestinal function in health and disease, with a current focus on motility, and particularly electrophysiology. The Virtual Intestine is being cohesively established across multiple physiological scales, from sub/cellular functions to whole organ levels, facilitating quantitative evaluations that present an integrative in silico framework. The models are also now finding broad physiological applications, including in evaluating hypotheses of slow wave pacemaker mechanisms, smooth muscle electrophysiology, structure-function relationships, and electromechanical coupling. Clinical applications are also beginning to follow, including in the pathophysiology of motility disorders, diagnosing intestinal ischemia, and visualizing colonic dysfunction. These advances illustrate the emerging potential of the Virtual Intestine to effectively address multiscale research challenges in interdisciplinary gastrointestinal sciences.

A system for automated quantification of cutaneous electrogastrograms

Clinical evaluation of cutaneous electrogastrograms (EGG) is important for understanding the role of slow waves in functional motility disorders and may be a useful diagnostic aid. An automated software package has been developed which computes metrics of interest from EGG and from slow wave recordings from the gastric mucosa and serosa in a reliable and efficient manner. In particular, the frequency and amplitude of the gastric slow waves were computed, after which signal integrity checks were performed to assess if the signals are valid. For validation, manual estimates of the frequency and amplitude were compared to automated estimates. The methods were packaged into a software executable which processes the data and presents the results in an intuitive graphical and a spreadsheet formats. Automated EGG analysis allows for clinical translation of bio-electrical analysis for potential diagnostics, as commonly used in the cardiac field.