Accurate dicrotic notch detection using adaptive shear transforms

Balmer, Joel; Pretty, Christopher G.; Amies, Alexander C.; Desaive, Thomas; Chase, J. Geoffrey

doi:10.1016/j.ifacol.2018.11.664

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…The selected ABP windows contained a total of 87,345 cardiac cycles; the PPG windows contained 86,766 cardiac cycles. To evaluate the performance of the IEM-based algorithm, the systolic phase duration (SPD) was used, which is defined as the time difference between the onset of the systolic phase and the occurrence of DN in the cardiac cycle [6]. The measured SPD, based on the marking, served as a reference.…”

Section: Datasetmentioning

confidence: 99%

“…1), occurring immediately after the systolic peak [4,5]. It is the result of the reflection of a wave at the aortic valve, following valve closure during the cardiac cycle [6,7,8, 9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Due to its crucial role as a reference point in the timing of systolic and diastolic events, the DN has been used in numerous applications, including pulse wave velocity calculations [6,7,11], determining left ventricular ejection time [6,7], and models estimating cardiovascular function [7,12,13,14,15]. The DN also plays a crucial role in feature extraction from ABP and PPG waveforms by segmenting the cardiac cycles into distinct phases: the systolic phase (duration from systolic onset to DN), the diastolic phase (duration from DN to end of diastolic phase), and the systolic decay phase (duration from systolic peak to DN).…”

Section: Introductionmentioning

confidence: 99%

“…Many different algorithms have been developed to detect the DN [6,7,25,26,27,28,29,30]. However, either these methods were not thoroughly tested for noise robustness, or they were not examined for their ability to identify the DN in signals lacking a prominent notch (DN-less signals).…”

Section: Introductionmentioning

confidence: 99%

“…These are referred to as 'DN-less signals'. It is important to note that 'detecting the DN' means determining its temporal location within the cardiac cycle.Many different algorithms have been developed to detect the DN[6,7,25,26,27,28,29,30]. However, either these methods were not thoroughly tested for noise robustness, or they were not examined for their ability to identify the DN in signals lacking a prominent notch (DN-less signals).…”

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confidence: 99%

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An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

Pal,

Rudas,

Kim

et al. 2024

Preprint

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Background and Objective: Detection 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. Methods: The 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 with 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. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87343) =.99, p<.001) and PPG (R2(86764) =.98, p<.001) waveforms. The algorithm had a lower mean error of dicrotic notch detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high accuracy of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our 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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Section: Datasetmentioning

confidence: 99%

“…1), occurring immediately after the systolic peak [4,5]. It is the result of the reflection of a wave at the aortic valve, following valve closure during the cardiac cycle [6,7,8, 9,10].…”

Section: Introductionmentioning

confidence: 99%