Photoplethysmogram Modeling of Extreme Bradycardia and Ventricular Tachycardia

Paliakaitė‬‬‬, Birutė; Petrėnas, Andrius; Sološenko, Andrius; Marozas, Vaidotas

doi:10.1007/978-3-030-31635-8_141

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Life-threatening arrhythmias are a common symptom in patients with CI and MI, and the common life-threatening arrhythmias include EB, ET, VT, and VF (Deaconu et al, 2021), and the definitions of those four life-threatening arrhythmias are given in Table 1 according to the beating rhythm of the heart rate (Alinejad et al, 2019;Paliakaitė et al, 2019). In the initial period of suddenness of lifethreatening arrhythmias, patients sometimes experience sudden heart pain that is slight and rapid and disappears after a short rest such as sitting or lying down, which is called "transient" (Chorin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

et al. 2022

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Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.

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Section: Introductionmentioning

confidence: 99%

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

et al. 2022

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“…PPG signal filtering is one of the most essential parts of preprocessing. The human heart rate ranges between 30 and 200 beats per minute (Paliakaitė et al 2020). Therefore, in PPG signal analysis, it is common to apply bandpass filtering such 0.…”

Section: Preprocessingmentioning

confidence: 99%

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Goda,

Charlton,

Behar

2024

Physiol. Meas.

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Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.com.

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“…Initial development could be performed using data from controlled experiments, including simulated data. Indeed, models have recently been developed to simulate the PPG during bradycardia and ventricular tachycardia [ 266 ] and AF [ 267 ]. The latter has been used for the initial assessment of an AF detector [ 268 ].…”

Section: Research Directionsmentioning

confidence: 99%

Wearable Photoplethysmography for Cardiovascular Monitoring

et al. 2022

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Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

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Photoplethysmogram Modeling of Extreme Bradycardia and Ventricular Tachycardia

Cited by 6 publications

References 23 publications

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Wearable Photoplethysmography for Cardiovascular Monitoring

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