{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Analyzes finger photoplethysmogram (PPG) signals to detect pulse waves, identify fiducial points, and extract biomarkers from continuous PPG recordings.","skillfed_tags":["signal-processing","biomedical","cardiovascular"],"use_cases":["Extract heart rate and pulse wave morphology features from finger PPG recordings for cardiovascular research or clinical studies.","Preprocess and segment PPG signals from wearable devices or clinical monitors to identify individual heartbeats and their fiducial points.","Batch analyze multiple PPG files in different formats (MAT, CSV, EDF) to compute standardized biomarkers for population-level health assessment.","Develop custom cardiac analysis pipelines by using pyPPG's API to load, filter, and extract biomarkers within your own Python code.","Assess PPG signal quality and detect artifacts in continuous recordings before downstream analysis or machine learning model input."],"what_it_does":"pyPPG is a Python toolbox for analyzing finger photoplethysmogram (PPG) signals\u2014the optical signals captured from fingertip blood flow. It automates the full pipeline of PPG analysis: loading raw signals from multiple file formats (MAT, TXT, CSV, EDF), preprocessing to remove noise and compute signal derivatives, detecting individual pulse waves by identifying systolic peaks and pulse onsets/offsets, and extracting a comprehensive set of 74 biomarkers (pulse wave features) that characterize cardiac and vascular properties. The toolbox is built on validated algorithms tested against public PPG databases and provides a signal quality index.\n\nThe package is intended for researchers and clinicians who need to extract standardized cardiac biomarkers from continuous PPG recordings. It handles real-time analysis of long-term recordings and abstracts away the complexity of beat detection and fiducial point identification, allowing users to focus on downstream analysis or clinical interpretation. The toolbox depends heavily on scipy, numpy, and pandas for numerical computation, plus wfdb and mne for signal I/O, and includes a web-based GUI via Eel for interactive use.","worth_installing":"Yes, with conditions. Install if you work with finger PPG signals and need validated, standardized biomarker extraction\u2014the toolbox is well-documented, has no known vulnerabilities, and is actively maintained at the repository level. However, be aware that the last PyPI release was over a year ago (aging status), the AGPL v3 license requires open-source compliance or internal-only use, and the 66 runtime dependencies create a large installation footprint. Verify that the aging release cycle does not conflict with your stability or support expectations."},"id":"pyppg","links":{"html":"https://skillfed.io/packages/pyppg","md":"https://skillfed.io/packages/pyppg.md","pypi":"https://pypi.org/project/pyppg/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-07-21","license_spdx":null,"license_treatment":"agpl","name":"pyPPG","python_support":"supports_current","summary":"pyPPG: a python toolbox for PPG morphological analysis."},"popularity":{"monthly_downloads":95143,"position":13290,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.73"}