Key result
PPG signal processing pipeline extracts ~84,000 high-quality segments and pulse wave features.
Why the study?
Recognizing the inherent variability in PPG signal quality, a comprehensive processing pipeline was needed to collect and analyze high-quality PPG data for biomarker extraction and physiological analysis.
The proposed pipeline provides a standardized approach for extracting high-quality PPG segments and biomarkers from large datasets like MIMIC-III, facilitating AI applications in healthcare.
Provides standardized PPG feature extraction from large ICU datasets; leaves open prospective validation before clinical AI use.
Photoplethysmography (PPG) is a non-invasive optical measurement method widely used for monitoring cardiovascular health. PPG signals provide a rich source of continuoustime data, valuable for various applications including circuit and system design, biomarker extraction, and physiological analysis. The availability of large datasets like the Medical Information Mart for Intensive Care III(MIMIC-III) dataset provides opportunities to develop advanced signal-processing algorithms for PPG data and offers great potential for artificial intelligence (AI) applications in healthcare. Recognizing the inherent variability in PPG signal quality, we present a comprehensive PPG signal processing pipeline designed to provide researchers with a tool for collecting and analyzing high-quality PPG data. The pipeline utilizes the MIMIC-III matched waveform dataset and the pyPPG Python toolbox. We demonstrate a workflow for PPG signal extraction, signal quality assessment using dynamic time wrapping-based (DTW) signal quality indexing (SQI), and fiducial point and biomarker extraction. The proposed pipeline provides researchers and developers with high-quality, labeled PPG segments and extracted features. We processed a total of 1.83 million (2.4 TB) PPG segments and successfully extracted 83,597 high-quality 60 -second segments (16 GB). For each signal segment, we extracted its fiducial points and a total of 102 pulse wave features using the pyPPG toolbox. We analyze the distributions of key biomarkers and discuss the pipeline's effectiveness. This work offers a standardized approach to signal processing that can enhance the reliability and efficiency of PPGbased health assessment tools and lay the foundation for future efforts in developing advanced algorithms for wearable health monitoring systems.
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Kong et al. (2025) studied this question. PPG signal processing pipeline was evaluated on Extraction of high-quality PPG segments and biomarkers. A comprehensive PPG signal processing pipeline successfully extracted 83,597 high-quality 60-second segments and 102 pulse wave features from 1.83 million MIMIC-III PPG segments.
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