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LIBPhys-EGG: An open-access multichannel electrogastrography and accelerometry dataset for reproducible research in
Rodrigo Braga1, Sara Sousa1, Nianfei Ao1
1Department of Physics, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, 2892-516, Caparica, Portugal.
Background And Objective:
Surface electrogastrography records gastrointestinal myoelectric activity non-invasively from the abdominal surface. Despite decades of investigation, clinical adoption remains limited by low signal-to-noise ratio, motion artifacts, and the lack of standardized public datasets. This work presents a multichannel electrogastrography dataset acquired using a wearable sensor belt, synchronized with tri-axial accelerometry, designed to support reproducible research in gastrointestinal signal processing and machine learning.
Methods:
The dataset (LIBPhys-EGG) includes 30 recording sessions from 15 healthy adults, totaling 111.8 h of multichannel physiological data. Primary session duration averaged 5.16 ± 1.22 h, covering fasting, meal ingestion, and post-prandial periods. Four bipolar electrogastrography channels and a tri-axial accelerometer were sampled at 1000 Hz. We release raw data with synchronized annotations, a recommended pipeline, feature selection methodology, and a benchmark classification task distinguishing pre- versus post-prandial states.
Results:
LIBPhys-EGG captured strong physiological changes post-meal. Features measuring signal power and amplitude variability increased substantially across 13 high-quality recordings from 7 unique participants, with large effect sizes (Cohen's dz up to 2.06, p<0.002). Random Forest achieved 0.93 accuracy and 0.966 AUC-ROC using 14 features on recording-level split data; for the same model, leave-one-subject-out validation yielded 0.690 ± 0.198 accuracy.
Conclusions:
This open dataset offers high-resolution, multichannel sEGG with synchronized motion signals and event annotations, enabling development and benchmarking of noise-robust signal processing and machine-learning methods. It also facilitates analysis of spatial signal distribution and electrode placement. We anticipate this resource will accelerate non-invasive GI research and contribute to standardized protocols for future studies.
