{"id": "package:96d09d56-931c-467f-acd6-4635e879fa73", "name": "utils_reviewer.py", "self_uri": "https://services.scicrunch.io/sparc/drs/v1/objects/96d09d56-931c-467f-acd6-4635e879fa73", "size": 21439, "created_time": "2024-08-01T20:13:17,991960Z", "updated_time": "2024-08-01T20:18:48,730580Z", "version": "1", "mime_type": "text/x-python", "checksums": [{"checksum": "813ba02a255185e99748fbae37eb89aa", "type": "sha256"}], "access_methods": [{"type": "s3", "access_url": {"url": "s3://sparc-prod-aod-discover-publish50-use1/537/files/code/utils_reviewer.py"}, "region": "us-east-1"}], "dataset": {"id": "537", "doi": "DOI:10.26275/le2j-j2gf", "title": "Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach", "description": "Biomarkers of gastrointestinal (GI) vagus nerve stimulation (VNS)", "abstract": "This computational dataset contains processed electrogastrography signals, extracted time/frequency-domain features, and machine learning classification results derived from analyzing gastric myoelectric activity in 7 adult male ferrets during vagus nerve stimulation at 10Hz and 30Hz frequencies, demonstrating that distinct feature sets optimally characterize electrophysiological changes induced by VNS and that systematic feature selection enhances identification of gastrointestinal neuromodulation biomarkers."}}