The mountaineer's method for peak detection in photoplethysmographic signals

Argüello-Prada, Erick  Javier

doi:10.17533/udea.redin.n90a06

Cited by 19 publications

(17 citation statements)

References 12 publications

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“…Detection of PPG pulse onset times is carried out using the method proposed by Argüello-Prada ( 2019 ), which is based on the first derivative of x PPG ( n ), denoted

. The maximum of

associated with the i th PPG pulse is denoted n F ( i ).…”

Section: Methodsmentioning

confidence: 99%

Acute Cardiovascular Effects of Hydrotreated Vegetable Oil Exhaust

et al. 2022

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Ambient air pollution is recognized as a key risk factor for cardiovascular morbidity and mortality contributing to the global disease burden. The use of renewable diesel fuels, such as hydrotreated vegetable oil (HVO), have increased in recent years and its impact on human health are not completely known. The present study investigated changes in cardiovascular tone in response to exposure to diluted HVO exhaust. The study participants, 19 healthy volunteers, were exposed in a chamber on four separate occasions for 3 h and in a randomized order to: (1) HVO exhaust from a wheel loader without exhaust aftertreatment, (2) HVO exhaust from a wheel loader with an aftertreatment system, (3) clean air enriched with dry NaCl salt particles, and (4) clean air. Synchronized electrocardiogram (ECG) and photoplethysmogram (PPG) signals were recorded throughout the exposure sessions. Pulse decomposition analysis (PDA) was applied to characterize PPG pulse morphology, and heart rate variability (HRV) indexes as well as pulse transit time (PTT) indexes were computed. Relative changes of PDA features, HRV features and PTT features at 1, 2, and 3 h after onset of the exposure was obtained for each participant and exposure session. The PDA index A13, reflecting vascular compliance, increased significantly in both HVO exposure sessions but not in the clean air or NaCl exposure sessions. However, the individual variation was large and the differences between exposure sessions were not statistically significant.

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“…Detection of PPG pulse onset times is carried out using the method proposed by Argüello-Prada ( 2019 ), which is based on the first derivative of x PPG ( n ), denoted

. The maximum of

associated with the i th PPG pulse is denoted n F ( i ).…”

Section: Methodsmentioning

confidence: 99%

Acute Cardiovascular Effects of Hydrotreated Vegetable Oil Exhaust

et al. 2022

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“…Segmentation is an effective task in the cleaning stage because improperly segmented beats will be misclassified as a bad beat. There are many efforts in the PPG beat segmentation area for providing accurate segmentation in presence of artifacts and distortion [45][46][47]. However, in this work, we follow the simple algorithm described in [48] for determining beats limits as local minima points.…”

Section: ) Ppg/abp Segmentationmentioning

confidence: 99%

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

et al. 2022

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This work is supported in part by the Information Technology Industry Development Agency (ITIDA) under number ARP2019.R27.4.ABSTRACTA growing attention is given to exploiting Photoplethysmography (PPG) signals in noninvasively measuring many physiological vital signs. Many machine deep learning models were trained for predicting the continuous arterial blood pressure (ABP) or just the systolic and diastolic blood pressure (BP) values based on a public database. However, jointly cleaning the PPG-ABP dataset that is the most critical pre-processing step for training quality is still in need for more investigations. There is a considerable amount of anomaly data that has to be excluded before any training stage. This paper introduces a two-level joint PPG-ABP cleaning technique conducted at a signal level and per-beat level. Many quality metrics have been checked successively for excluding improper data. These metrics achieve a coarse cleaning step. Finally, principal component analysis (PCA) is exploited for fine cleaning the remaining data from the former stage. The cleaning efficiency is evaluated by measuring its impact on the deep-learning-based BP estimation models. The trained model based on our cleaned data shows performance enhancement in terms of prediction error and the correlation between the predicted and ground-truth BP. Segmented and cleaned PPG/ABP dataset will be publicly available at both signal level and beat level. Based on the simulation results, the proposed cleaning technique enhances the standard deviation of the prediction error of systolic and diastolic blood pressure by 11.68 % and 10.81 %, respectively. Also, it enhances the mean absolute error of the prediction of systolic and diastolic blood pressure by 14.79 % and 11.70 %, respectively.

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“…Nowadays, with the explosion of smart devices and automatic intelligent control systems along with the ever-increasing volume of data, signal analysis is more difficult because of the increase in noise as well as computational requirements [6], [7]. Many solutions have been proposed for peak detection in signal processing in the literature ranging from the adaptive threshold method (ATM) [8], wavelet transform techniques [9], [10], hidden Markov models [11], k-means clustering [12], and entropy-based techniques [13] to the automatic chromatographic peak detection (ACPD) [14], the peak of Shannon energy envelope (PSEE) [15], mountaineer's method [16], automatic multi-scale peak detection (AMPD) [17], [18] and ATPD [19]. But most of them are designed for a specific type of signals.…”

Section: Introductionmentioning

confidence: 99%

“…Some algorithms are designed only for specific signals. For instance, ACPD is designed for chromatographic signals [20]- [22], and the PSEE [23]- [25] are designed for electrocardiogram (ECG) signals, while the ATM and others [16], [26]- [28] are designed for photoplethysmorgraphy (PPG) signals. Some other algorithms (but fewer) are designed for peak detection in general such as AMPD and ATPD [13], [29], [30].…”

Section: Introductionmentioning

confidence: 99%

An optimized algorithm for peak detection in noisy periodic and quasi-periodic signals

2022

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This paper represents a modified version (called T-AMPD) of automatic multiscale-based peak detection (AMPD) algorithm, which was previously proposed by Scholkmann et al for peak detection of noisy periodic and quasi-periodic signals, in order to optimize the algorithm and reduce the computational cost while still maintaining the accuracy. By introducing a new parameter , the number of expected peaks, to the algorithm and some other adjustments, the T-AMPD outperforms the original algorithm in processing time. Experiments in some real-world signals show that the modified algorithm is greatly better than the original one in runtime performance. Specifically, our T-AMPD was compared to the newest version of AMPD which is available in an R programming package. As a result, our T-AMPD is 2 to 25 times faster than the original AMPD in computational time, while the accuracy remains unchanged.

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The mountaineer's method for peak detection in photoplethysmographic signals

Cited by 19 publications

References 12 publications

Acute Cardiovascular Effects of Hydrotreated Vegetable Oil Exhaust

Acute Cardiovascular Effects of Hydrotreated Vegetable Oil Exhaust

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

An optimized algorithm for peak detection in noisy periodic and quasi-periodic signals

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