Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms
Pal, R.; Rudas, A.; Williams, T.; Chiang, J. N.; Barney, A.; Cannesson, M.
Show abstract
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 tools 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 tools 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 tools 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.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Digitizing ECG image: new fully automated method and open-source software code 98%
- An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method 97%
- BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis 96%
Similar papers in this journal
- Hilbert-Envelope Features for Cardiac Disease Classification from Noisy Phonocardiograms 96%
- Graph connection Laplacian allows for enhanced outcomes of consumer camera based photoplethysmography imaging 95%
- Improved online event detection and differentiation by a simple gradient-based nonlinear transformation: Implications for the biomedical signal and image analysis 94%
Similar papers in this journal
- Comparison of feature-based indices derived from photoplethysmogram recorded from different body locations during lower body negative pressure 97%
- An Open-Access Simultaneous Electrocardiogram and Phonocardiogram Database 96%
- High-Fidelity Measurement of Pulse Arrival Time in Critically Ill Children Using Standard Bedside Monitoring Equipment 96%
Similar papers in this journal
Similar papers in this journal
- Machine Learning-based Clinical Decision Support for Infection Risk Prediction 91%
- Development and External Validation of a Mixed-Effects Deep Learning Model to Diagnose COVID-19 from CT Imaging 90%
- Regional pleural strain measurements during mechanical ventilation using ultrasound elastography: A randomised, crossover, proof of concept physiologic study 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.