71 research outputs found
Intrinsic Frequency Analysis and Fast Algorithms
Intrinsic Frequency (IF) has recently been introduced as an ample signal
processing method for analyzing carotid and aortic pulse pressure tracings. The
IF method has also been introduced as an effective approach for the analysis of
cardiovascular system dynamics. The physiological significance, convergence and
accuracy of the IF algorithm has been established in prior works. In this
paper, we show that the IF method could be derived by appropriate mathematical
approximations from the Navier-Stokes and elasticity equations. We further
introduce a fast algorithm for the IF method based on the mathematical analysis
of this method. In particular, we demonstrate that the IF algorithm can be made
faster, by a factor or more than 100 times, using a proper set of initial
guesses based on the topology of the problem, fast analytical solution at each
point iteration, and substituting the brute force algorithm with a pattern
search method. Statistically, we observe that the algorithm presented in this
article complies well with its brute-force counterpart. Furthermore, we will
show that on a real dataset, the fast IF method can draw correlations between
the extracted intrinsic frequency features and the infusion of certain drugs.
In general, this paper aims at a mathematical analysis of the IF method to show
its possible origins and also to present faster algorithms
Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions
Abstract Photoplethysmogram (PPG) monitoring is not only essential for critically ill patients in hospitals or at home, but also for those undergoing exercise testing. However, processing PPG signals measured after exercise is challenging, especially if the environment is hot and humid. In this paper, we propose a novel algorithm that can detect systolic peaks under challenging conditions, as in the case of emergency responders in tropical conditions. Accurate systolic-peak detection is an important first step for the analysis of heart rate variability. Algorithms based on local maxima-minima, first-derivative, and slope sum are evaluated, and a new algorithm is introduced to improve the detection rate. With 40 healthy subjects, the new algorithm demonstrates the highest overall detection accuracy (99.84% sensitivity, 99.89% positive predictivity). Existing algorithms, such as Billauer's, Li's and Zong's, have comparable although lower accuracy. However, the proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination. For best performance, we show that a combination of two event-related moving averages with an offset threshold has an advantage in detecting systolic peaks, even in heat-stressed PPG signals
Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions
Photoplethysmogram (PPG) monitoring is not only essential for critically ill patients in hospitals or at home, but also for those undergoing exercise testing. However, processing PPG signals measured after exercise is challenging, especially if the environment is hot and humid. In this paper, we propose a novel algorithm that can detect systolic peaks under challenging conditions, as in the case of emergency responders in tropical conditions. Accurate systolic-peak detection is an important first step for the analysis of heart rate variability. Algorithms based on local maxima-minima, first-derivative, and slope sum are evaluated, and a new algorithm is introduced to improve the detection rate. With 40 healthy subjects, the new algorithm demonstrates the highest overall detection accuracy (99.84% sensitivity, 99.89% positive predictivity). Existing algorithms, such as Billauer's, Li's and Zong's, have comparable although lower accuracy. However, the proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination. For best performance, we show that a combination of two event-related moving averages with an offset threshold has an advantage in detecting systolic peaks, even in heat-stressed PPG signals.Mohamed Elgendi, Ian Norton, Matt Brearley, Derek Abbott, Dale Schuurman
Robust Algorithms for Unattended Monitoring of Cardiovascular Health
Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring
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Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography
Objective
Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content.
Methods
PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests.
Results
From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz.
Conclusion
The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application.
Significance
The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal
Hypoxia-induced effects on ECG depolarization by time warping analysis during recurrent obstructive apnea
this work, we evaluated a non-linear approach to estimate morphological variations in ECG depolarization, in the context of intermittent hypoxia (IH). Obstructive apnea sequences were provoked for 15 minutes in anesthetized Sprague-Dawley rats, alternating with equal periods of normal breathing, in a recurrent obstructive sleep apnea (OSA) model. Each apnea episode lasted 15 s, while the frequency used for each sequence was randomly selected. Average heartbeats obtained before the start and at the end of each episode, were delineated to extract only the QRS wave. Then, the segmented QRS waves were non-linearly aligned using the dynamic time warping (DWT) algorithm. Morphological QRS changes in both the amplitude and temporal domains were estimated from this alignment procedure. The hypoxic and basal segments were analyzed using ECG (lead I) recordings acquired during the experiment. To assess the effects of IH over time, the changes relative to the basal QRS wave were determined, in the intervals prior to each successive events until the end of the experiment. The results showed a progressive increase in the amplitude and time-domain morphological markers of the QRS wave along the experiment, which were strongly correlated with the changes in traditional QRS markers (r ˜ 0.9). Significant changes were found between pre-apnea and hypoxic measures only for the time-domain analysis (p<; 0.001), probably due to the short duration of the simulated apnea episodes. Clinical relevance Increased variability in ECG depolarization morphology during recurrent hypoxic episodes would be closely related to the expression of cardiovascular dysfunction in OSA patients.Peer ReviewedPostprint (author's final draft
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Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.
BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied. MATERIALS AND METHODS: The arterial blood pressure of 99 subjects with traumatic brain injury was continuously measured for 5 consecutive days, beginning on the day of admission. The machine-learning deep belief network was constructed to automatically identify and remove false incidences of hypotension, hypertension, bradycardia, tachycardia, and alterations in cerebral perfusion pressure (CPP). RESULTS: The prevalences of hypotension and tachycardia were significantly reduced by 47.5% and 13.1%, respectively, after suppressing false incidents (P=0.01). Hypotension was particularly effective at predicting outcome favorability and mortality after artifact elimination (P=0.015 and 0.027, respectively). In addition, increased CPP was also statistically significant in predicting outcomes (P=0.02). CONCLUSIONS: The prevalence of false incidents due to signal artifacts can be significantly reduced using machine-learning. Some clinical events, such as hypotension and alterations in CPP, gain particularly high predictive capacity for patient outcomes after artifacts are eliminated from physiological signals
pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis
Photoplethysmography is a non-invasive optical technique that measures
changes in blood volume within tissues. It is commonly and increasingly used
for in a variety of research and clinical application 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 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 recorded using a standard finger-based 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. This 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 engineers a set of 74
PPG biomarkers. Studying the 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.orgComment: The manuscript was submitted to "Physiological Measurement" on
September 5, 202
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Comparison of pulse rate variability and morphological features of photoplethysmograms in estimation of blood pressure
Photoplethysmography is an optical technique that produces a wealth of information about cardiovascular health. Therefore, the technology has become an integral part of personal health monitoring devices. Given the importance of blood pressure measurement and control in physical and mental health, in recent years, the estimation of blood pressure from photoplethysmography has been an active area of research with promising results. Most studies on the subject rely on the morphological features of the photoplethysmogram. These features are highly prone to noise, changes in sensor placement, and skin properties; including skin colour. To address these limitations, we investigated the feasibility of using pulse rate variability features which are known to be less prone to the aforementioned limitations. To this end, we collected high quality photoplethysmograms using a bespoke, research-grade device from 18 healthy subjects. Approximately 15 min of photoplethysmograms and continuous blood pressure waveforms were collected from each subject. We trained machine learning models based on different feature sets and compared their performances. The model with morphological features alone outperformed the model with pulse rate variability features, root mean squared error (RMSE) of 6.32 vs 7.23 mmHg. However, the best performance was obtained using the combined set of features (RMSE: 5.71 mmHg). Combined, the evidence shows that the estimation of BP from PRV, alone or in conjunction with morphological features, is feasible. In light of the limitations of morphological features in estimation of blood pressure, our findings lend support to further research on the use of pulse rate variability features
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