{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:28:27Z","timestamp":1768570107392,"version":"3.49.0"},"posted":{"date-parts":[[2025,3,21]]},"group-title":"Anesthesia","reference-count":59,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,3,21]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool\u2019s ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool\u2019s real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool\u2019s detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.<\/jats:p>","DOI":"10.1101\/2025.03.20.25324325","type":"posted-content","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T23:42:48Z","timestamp":1742773368000},"source":"Crossref","is-referenced-by-count":0,"title":["Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms"],"prefix":"10.64898","author":[{"given":"Ravi","family":"Pal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akos","family":"Rudas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiffany","family":"Williams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey N.","family":"Chiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Barney","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxime","family":"Cannesson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025032508550886000_2025.03.20.25324325v1.1","doi-asserted-by":"crossref","first-page":"138817","DOI":"10.1109\/ACCESS.2021.3117969","article-title":"A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG)","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"2025032508550886000_2025.03.20.25324325v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59121-0"},{"key":"2025032508550886000_2025.03.20.25324325v1.3","doi-asserted-by":"crossref","first-page":"7856","DOI":"10.1038\/s41598-023-35055-1","article-title":"Reproduction of human blood pressure waveform using physiology-based cardiovascular simulator","volume":"13","year":"2023","journal-title":"Sci Rep"},{"key":"2025032508550886000_2025.03.20.25324325v1.4","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.bpa.2014.08.001","article-title":"Measurement of blood pressure","volume":"28","year":"2014","journal-title":"Best practice & research. 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