A novel scalable electrode array and system for non-invasively assessing gastric function using flexible electronics

BACKGROUND

Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body surface.

METHODS

The system comprises a custom-designed stretchable high-resolution "peel-and-stick" sensor array (8 × 8 pre-gelled Ag/AgCl electrodes at 2 cm spacing; area 225 cm² ), wearable data logger with custom electronics incorporating bioamplifier chips, accelerometer and Bluetooth synchronized in real-time to an App with cloud connectivity. Automated algorithms filter and extract HR biomarkers including propagation (phase) mapping. The system was tested in a cohort of 24 healthy subjects to define reliability and characterize features of normal gastric activity (30 m fasting, standardized meal, and 4 h postprandial).

KEY RESULTS

Gastric mapping was successfully achieved non-invasively in all cases (16 male; 8 female; aged 20-73 years; BMI 24.2 ± 3.5). In all subjects, gastric electrophysiology and meal responses were successfully captured and quantified non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in <4 h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7° ± 73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate).

CONCLUSIONS AND INFERENCES

BSGM is a new diagnostic tool for assessing gastric function that is scalable and ready for clinical applications, offering several biomarkers that are improved or new to gastroenterology practice.

Gastric dysfunction in patients with chronic nausea and vomiting syndromes defined by a noninvasive gastric mapping device

Chronic nausea and vomiting syndromes (NVSs) are prevalent and debilitating disorders. Putative mechanisms include gastric neuromuscular disease and dysregulation of brain-gut interaction, but clinical tests for objectively defining gastric motor function are lacking. A medical device enabling noninvasive body surface gastric mapping (BSGM) was developed and applied to evaluate NVS pathophysiology. BSGM was performed in 43 patients with NVS and 43 matched controls using Gastric Alimetry (Alimetry), a conformable high-resolution array (8 × 8 electrodes; 20-mm interelectrode spacing), wearable reader, and validated symptom-logging app. Continuous measurement encompassed a fasting baseline (30 minutes), 482-kilocalorie meal, and 4-hour postprandial recording, followed by spectral and spatial biomarker analyses. Meal responses were impaired in NVS, with reduced amplitudes compared to controls (median, 23.3 microvolts versus 38.0 microvolts, P < 0.001), impaired fed-fasting power ratios (1.1 versus 1.6, P = 0.02), and disorganized slow waves (spatial frequency stability, 13.6 versus 49.5; P < 0.001). Two distinct NVS subgroups were evident with indistinguishable symptoms (all P > 0.05). Most patients (62%) had normal BSGM studies with increased psychological comorbidities (43.5% versus 7.7%; P = 0.03) and anxiety scores (median, 16.5 versus 13.0; P = 0.035). A smaller subgroup (31%) had markedly abnormal BSGM, with biomarkers correlating with symptoms (nausea, pain, excessive fullness, early satiety, and bloating; all r > 0.35, P < 0.05). Patients with NVS share overlapping symptoms but comprise distinct underlying phenotypes as revealed by a BSGM device. These phenotypes correlate with symptoms, which should inform clinical management and therapeutic trial design.

Effects of anatomical variations of the stomach on body-surface gastric mapping investigated using a large population-based multiscale simulation approach

The contractions of the stomach are governed by bioelectrical slow waves that can be detected non-invasively from the body-surface. Diagnosis of gastric motility disorders remains challenging due to the limited information provided by symptoms and tests, including standard electrogastrography (EGG). Body-surface gastric mapping (BSGM) is a novel technique that measures the resultant body-surface potentials using an array of multiple cutaneous electrodes. However, there is no established protocol to guide the placement of the mapping array and to account for the effects of biodiversity on the interpretation of gastric BSGM data. This study aims to quantify the effect of anatomical variation of the stomach on body surface potentials. To this end, 93 subject specific models of the stomach and torso were developed. Anatomical models were developed based on data obtained from the Cancer Imaging Archive. For each subject a set of points were created to model general anatomy the stomach and the torso, using a finite element mesh. A bidomain model was used to simulate the gastric slow waves in the antegrade wave (AW) direction and formation of colliding waves (CW). The resultant dipole was calculated, and a forward modeling approach was employed to simulate body-surface potentials. Simulated data were sampled from a 55 array of electrodes from the body-surface and compared between AW and CW cases. Anatomical parameters such as the Euclidean distance from the xiphoid process (8.6 2.2 cm), orientation relative to the axial plane (195 20.0) were quantified. Electrophysiological simulations of AW and CW were both correlated to specific metrics derived from BSGM signals. In general, the maximum amplitude () and orientation () of the signals provided consistent separation of AW and CW. The findings of this study will aid gastric BSGM electrode array design and placement protocol in clinical practices.

Body surface mapping of the stomach: New directions for clinically evaluating gastric electrical activity

BACKGROUND

Gastric motility disorders, which include both functional and organic etiologies, are highly prevalent. However, there remains a critical lack of objective biomarkers to guide efficient diagnostics and personalized therapies. Bioelectrical activity plays a fundamental role in coordinating gastric function and has been investigated as a contributing mechanism to gastric dysmotility and sensory dysfunction for a century. However, conventional electrogastrography (EGG) has not achieved common clinical adoption due to its perceived limited diagnostic capability and inability to impact clinical care. The last decade has seen the emergence of novel high-resolution methods for invasively mapping human gastric electrical activity in health and disease, providing important new insights into gastric physiology. The limitations of EGG have also now become clearer, including the finding that slow-wave frequency alone is not a reliable discriminator of gastric dysrhythmia, shifting focus instead toward altered spatial patterns. Recently, advances in bioinstrumentation, signal processing, and computational modeling have aligned to allow non-invasive body surface mapping of the stomach to detect spatiotemporal gastric dysrhythmias. The clinical relevance of this emerging strategy to improve diagnostics now awaits determination.

PURPOSE

This review evaluates these recent advances in clinical gastric electrophysiology, together with promising emerging data suggesting that novel gastric electrical signatures recorded at the body surface (termed "body surface mapping") may correlate with symptoms. Further technological progress and validation data are now awaited to determine whether these advances will deliver on the promise of clinical gastric electrophysiology diagnostics.

Effects of magnetogastrography sensor configurations in tracking slow wave propagation

Magnetogastrography (MGG) is a non-invasive method of assessing gastric slow waves (SWs) by recording the resultant magnetic fields. MGG can capture both SW frequency and propagation, and identify SW dysrhythmias that are associated with motility disorders. However, the impact of the restricted spatial coverage and sensor density on SW propagation tracking performance is unknown. This study simulated MGG using multiple anatomically specific torso geometries and two realistic SW propagation patterns to determine the effect of different sensor configurations on tracking SW propagation. The surface current density mapping and center-of-gravity tracking methods were used to compare four magnetometer array configurations: a reference system currently used in GI research and three hypothetical higher density and coverage arrays. SW propagation patterns identified with two hypothetical arrays (with coverage over at least the anterior of the torso) correlated significantly higher with simulated realistic 3 cycle-per-minute SW activity than the reference array (p = 0.016, p = 0.005). Furthermore, results indicated that most of the magnetic fields that contribute to the performance of SW propagation tracking were located on the anterior of the torso as further increasing the coverage did not significantly increase performance. A 30% decrease in sensor spacing within the same spatial coverage of the reference array also significantly increased correlation values by approximately 0.50 when the signal-to-noise ratio was 5 dB. This study provides evidence that higher density and coverage sensor layouts will improve the utility of MGG. Further work is required to investigate optimum sensor configurations across larger anatomical variations and other SW propagation patterns.