Jonathan C. Erickson

Falling-edge, variable threshold (FEVT) method for the automated detection of gastric slow wave events in high-resolution serosal electrode recordings.

High resolution (HR) multi-electrode mapping is increasingly being used to evaluate gastrointestinal slow wave behaviors. To create the HR activation time (AT) maps from gastric serosal electrode recordings that quantify slow wave propagation, it is first necessary to identify the AT of each individual slow wave event. Identifying these ATs has been a time consuming task, because there has previously been no reliable automated detection method. We have developed an automated AT detection method termed falling-edge, variable threshold (FEVT) detection. It computes a detection signal transform to accentuate the high ‘energy’ content of the falling edges in the serosal recording, and uses a running median estimator of the noise to set the time-varying detection threshold. The FEVT method was optimized, validated, and compared to other potential algorithms using in vivo HR recordings from a porcine model. FEVT properly detects ATs in a wide range of waveforms, making its performance substantially superior to the other methods, especially for low signal-to-noise ratio (SNR) recordings. The algorithm offered a substantial time savings (>100 times) over manual-marking whilst achieving a highly satisfactory sensitivity (0.92) and positive-prediction value (0.89).

The gastrointestinal electrical mapping suite (GEMS): software for analyzing and visualizing high-resolution (multi-electrode) recordings in spatiotemporal detail

BackgroundGastrointestinal contractions are controlled by an underlying bioelectrical activity. High-resolution spatiotemporal electrical mapping has become an important advance for investigating gastrointestinal electrical behaviors in health and motility disorders. However, research progress has been constrained by the low efficiency of the data analysis tasks. This work introduces a new efficient software package: GEMS (Gastrointestinal Electrical Mapping Suite), for analyzing and visualizing high-resolution multi-electrode gastrointestinal mapping data in spatiotemporal detail.ResultsGEMS incorporates a number of new and previously validated automated analytical and visualization methods into a coherent framework coupled to an intuitive and user-friendly graphical user interface. GEMS is implemented using MATLAB®, which combines sophisticated mathematical operations and GUI compatibility. Recorded slow wave data can be filtered via a range of inbuilt techniques, efficiently analyzed via automated event-detection and cycle clustering algorithms, and high quality isochronal activation maps, velocity field maps, amplitude maps, frequency (time interval) maps and data animations can be rapidly generated. Normal and dysrhythmic activities can be analyzed, including initiation and conduction abnormalities. The software is distributed free to academics via a community user website and forum (http://sites.google.com/site/gimappingsuite).ConclusionsThis software allows for the rapid analysis and generation of critical results from gastrointestinal high-resolution electrical mapping data, including quantitative analysis and graphical outputs for qualitative analysis. The software is designed to be used by non-experts in data and signal processing, and is intended to be used by clinical researchers as well as physiologists and bioengineers. The use and distribution of this software package will greatly accelerate efforts to improve the understanding of the causes and clinical consequences of gastrointestinal electrical disorders, through high-resolution electrical mapping.

Automated Gastric Slow Wave Cycle Partitioning and Visualization for High-resolution Activation Time Maps

High-resolution (HR) multi-electrode mapping has become an important technique for evaluating gastrointestinal (GI) slow wave (SW) behaviors. However, the application and uptake of HR mapping has been constrained by the complex and laborious task of analyzing the large volumes of retrieved data. Recently, a rapid and reliable method for automatically identifying activation times (ATs) of SWs was presented, offering substantial efficiency gains. To extend the automated data-processing pipeline, novel automated methods are needed for partitioning identified ATs into their propagation cycles, and for visualizing the HR spatiotemporal maps. A novel cycle partitioning algorithm (termed REGROUPS) is presented. REGROUPS employs an iterative REgion GROwing procedure and incorporates a Polynomial-surface-estimate Stabilization step, after initiation by an automated seed selection process. Automated activation map visualization was achieved via an isochronal contour mapping algorithm, augmented by a heuristic 2-step scheme. All automated methods were collectively validated in a series of experimental test cases of normal and abnormal SW propagation, including instances of patchy data quality. The automated pipeline performance was highly comparable to manual analysis, and outperformed a previously proposed partitioning approach. These methods will substantially improve the efficiency of GI HR mapping research.

Experimental and Automated Analysis Techniques for High-resolution Electrical Mapping of Small Intestine Slow Wave Activity.

Background/Aims Small intestine motility is governed by an electrical slow wave activity, and abnormal slow wave events have been associated with intestinal dysmotility. High-resolution (HR) techniques are necessary to analyze slow wave propagation, but progress has been limited by few available electrode options and laborious manual analysis. This study presents novel methods for in vivo HR mapping of small intestine slow wave activity. Methods Recordings were obtained from along the porcine small intestine using flexible printed circuit board arrays (256 electrodes; 4 mm spacing). Filtering options were compared, and analysis was automated through adaptations of the falling-edge variable-threshold (FEVT) algorithm and graphical visualization tools. Results A Savitzky-Golay filter was chosen with polynomial-order 9 and window size 1.7 seconds, which maintained 94% of slow wave amplitude, 57% of gradient and achieved a noise correction ratio of 0.083. Optimized FEVT parameters achieved 87% sensitivity and 90% positive-predictive value. Automated activation mapping and animation successfully revealed slow wave propagation patterns, and frequency, velocity, and amplitude were calculated and compared at 5 locations along the intestine (16.4 ± 0.3 cpm, 13.4 ± 1.7 mm/sec, and 43 ± 6 µV, respectively, in the proximal jejunum). Conclusions The methods developed and validated here will greatly assist small intestine HR mapping, and will enable experimental and translational work to evaluate small intestine motility in health and disease.

Detection of Small Bowel Slow-Wave Frequencies From Noninvasive Biomagnetic Measurements

We report a novel method for identifying the small intestine electrical activity slow-wave frequencies (SWFs) from noninvasive biomagnetic measurements. Superconducting quantum interference device magnetometer measurements are preprocessed to remove baseline drift and high-frequency noise. Subsequently, the underlying source signals are separated using the well-known second-order blind identification (SOBI) algorithm. A simple classification scheme identifies and assigns some of the SOBI components to a section of small bowel. SWFs were clearly identified in 10 out of 12 test subjects to within 0.09-0.25 cycles per minute. The method is sensitive at the 40.3 %-55.9% level, while false positive rates were 0%-8.6 %. This technique could potentially be used to help diagnose gastrointestinal ailments and obviate some exploratory surgeries.

Diabetic gastroparesis alters the biomagnetic signature of the gastric slow wave.

Gastroparesis is characterized by delayed gastric emptying without mechanical obstruction, but remains difficult to diagnose and distinguish from other gastrointestinal (GI) disorders. Gastroparesis affects the gastric slow wave, but non‐invasive assessment has been limited to the electrogastrogram (EGG), which reliably characterizes temporal dynamics but does not provide spatial information.

Mapping small intestine bioelectrical activity using high-resolution printed-circuit-board electrodes

In this study, novel methods were developed for the in-vivo high-resolution recording and analysis of small intestine bioelectrical activity, using flexible printed-circuit-board (PCB) electrode arrays. Up to 256 simultaneous recordings were made at multiple locations along the porcine small intestine. Data analysis was automated through the application and tuning of the Falling-Edge Variable-Threshold algorithm, achieving 92% sensitivity and a 94% positive-predictive value. Slow wave propagation patterns were visualized through the automated generation of animations and isochronal maps. The methods developed and validated in this study are applicable for use in humans, where future studies will serve to improve the clinical understanding of small intestine motility in health and disease.

Effective Stimulus Parameters for Directed Locomotion in Madagascar Hissing Cockroach Biobot

Swarms of insects instrumented with wireless electronic backpacks have previously been proposed for potential use in search and rescue operations. Before deploying such biobot swarms, an effective long-term neural-electric stimulus interface must be established, and the locomotion response to various stimuli quantified. To this end, we studied a variety of pulse types (mono- vs. bipolar; voltage- vs. current-controlled) and shapes (amplitude, frequency, duration) to parameters that are most effective for evoking locomotion along a desired path in the Madagascar hissing cockroach (G. portentosa) in response to antennal and cercal stimulation. We identified bipolar, 2 V, 50 Hz, 0.5 s voltage controlled pulses as being optimal for evoking forward motion and turns in the expected contraversive direction without habituation in ≈50% of test subjects, a substantial increase over ≈10% success rates previously reported. Larger amplitudes for voltage (1–4 V) and current (50–150 μA) pulses generally evoked larger forward walking (15.6–25.6 cm; 3.9–5.6 cm/s) but smaller concomitant turning responses (149 to 80.0 deg; 62.8 to 41.2 deg/s). Thus, the radius of curvature of the initial turn-then-run locomotor response (≈10–25 cm) could be controlled in a graded manner by varying the stimulus amplitude. These findings could be used to help optimize stimulus protocols for swarms of cockroach biobots navigating unknown terrain.

Generation of complex motor patterns in american grasshopper via current-controlled thoracic electrical interfacing

Micro-air vehicles (MAVs) have attracted attention for their potential application to military applications, environmental sensing, and search and rescue missions. While progress is being made toward fabrication of a completely human-engineered MAV, another promising approach seeks to interface to, and take control of, an insects nervous system. Cyborg insects take advantage of their innate exquisite loco-motor, navigation, and sensing abilities. Recently, several groups have demonstrated the feasibility of radio-controlled flight in the hawkmoth and beetle via electrical neural interfaces. Here, we report a method for eliciting the “jump” response in the American grasshopper (S. Americana). We found that stimulating the metathoracic T3 ganglion with constant-current square wave pulses with amplitude 186 ± 40 μA and frequency 190 ± 13 Hz reproducibly evoked (≥95% success rate) the desired motor activity in N=3 test subjects. To the best of our knowledge, this is the first report of an insect cyborg with a synchronous neuromuscular system.

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.