ArticleNPJ cardiovascular health2025
Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.
Article in NPJ cardiovascular health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Harmonic-Selective Gaussian Filtering for Morphology and Timing Preservation in PPG Signals.Sensors (Basel, Switzerland) · 2026Article
- Comparative Assessment of PPG-Derived HRV Using MAX30102 Sensor and Analog Circuitry with ADS1115 ADC.Sensors (Basel, Switzerland) · 2026Article
- Vascular waveform analysis using Bayesian pulse deconvolution.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning-Based Identification of Patients with Elevated Central Venous Pressure Using Features Extracted from Photoplethysmography Waveforms.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This study presents 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. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated on the perioperative MLORD dataset comprising 17,327 patients and on real-time data collected from a patient monitor (retrospective analysis). When compared with markings by an experienced researcher, the tool demonstrated robust performance across both datasets, waveform types, and all four landmarks, achieving average F1-scores above 97% and error rates below 4%. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.