ArticleComputer methods and programs in biomedicine2024
An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.
Article in Computer methods and programs in biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Mechanical pressure on endothelial cells mediates remote ischaemic preconditioning-induced neuroprotection via miR-126.Experimental physiology · 2026Article
- Evaluation of a polarization-sensitive, dual-wavelength wearable photoplethysmography sensor across a range of skin tones.Biophotonics discovery · 2026Article
- Assessing human scalp and brain blood flow sensitivities via superficial temporal artery occlusion using speckle contrast optical spectroscopy.APL bioengineering · 2025Article
- Artifact management methodologies for arterial blood pressure signals: A systematic scoping review of human and animal literature.Physiological reports · 2025Article
- Prescreening depression using wearable electrocardiogram and photoplethysmogram data from a psycholinguistic experiment.Physiological measurement · 2025Article
- Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.NPJ cardiovascular health · 2025Article
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Abstract
BACKGROUND AND
objectiveDetection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms.
methodsThe algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms.
resultsThe correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R
conclusionOur proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.
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