Automatic detection of left ventricular ejection time from a finger photoplethysmographic pulse oximetry waveform: comparison with Doppler aortic measurement

Study of Echocardiogram Parameters from PPG Signal Using Self-Organized Operational Map-based Network

Two crucial echocardiography parameters -left ventricular ejection fraction (LVEF) and myocardial performance index (MPI) are referred by clinicians to diagnose heart health. Here, an attempt was made to study the possibility of predicting values of LVEF and MPI from Photoplethysmography (PPG) signals. The classification of patients based on the LVEF and MPI values was also evaluated. After PPG signal feature extraction, the Dual Attention-Self Organised Operational Map-LSTM-Conv Network (DASLCN) was used to find the necessary results. Self-organized operational maps (SOOM) helped map the features before sending them to BiLSTM and 1D CNN layers. The results obtained were regression=0.86 with error% of 5.32±8.9 for MPI and accuracy=0.90 & sensitivity=0.89 for LVEF. This technique might help diagnose heart conditions from PPG signals without routine echocardiography.Clinical relevance- PPG is an easy cost-effective portable technique. Whereas, clinical echocardiography is possible only in specialized hospitals. Thus, exploring PPG signals to predict LVEF and MPI values were tried here. This study has been made on whether the grouping of patients based on the range of LVEF and MPI values was possible or not. Newly designed DASLCN helped to perform regression and classification in the same network.

Wearable Photoplethysmography for Cardiovascular Monitoring

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.

A comparison between left ventricular ejection time measurement methods during physiological changes induced by simulated microgravity

NEW FINDINGS

What is the central question of this study? First, we validated easy-to-use oscillometric left ventricular ejection time (LVET) against echocardiographic LVET. Second, we investigated progression of left ventricular ejection time index (LVETI), pre-ejection period index (PEPI), total electromechanical systole index (QS2I) and PEP/LVET ratio during 60 days of head-down tilt (HDT). What is the main finding and its importance? The LVETosci and LVETecho showed good agreement in effect direction. Hence, LVETosci might be useful to evaluate cardiovascular responses during space flight. Moreover, the approach might be useful for individual follow-up of patients with altered ejection times. Furthermore, significant effects of 60 days of HDT were captured by measurements of LVETI, PEPI, QS2I and PEP/LVET ratio.

ABSTRACT

Systolic time intervals that are easy to detect might be used as parameters reflecting cardiovascular deconditioning. We compared left ventricular ejection time (LVET) measured via ultrasound Doppler on the left ventricular outflow tract with oscillometrically measured LVET, measured at the brachialis. Furthermore, we assessed the progression of the left ventricular ejection time index (LVETI), the pre-ejection period index (PEPI), the Weissler index (PEP/LVET) and the total electromechanical systole index (QS2I) during prolonged strict head-down tilt (HDT) bed rest, including 16 male and eight female subjects. Simultaneous oscillometric and echocardiographic LVET measurements showed significant correlation (r = 0.53 with P = 0.0084 before bed rest and r = 0.73 with P < 0.05 on the last day of bed rest). The shortening of LVET during HDT bed rest measured with both approaches was highly concordant in their effect direction, with a concordance rate of 0.96. Our results also demonstrated a significant decrease of LVETI (P < 0.0001) and QS2I (P = 0.0992) and a prolongation of PEPI (P = 0.0049) and PEP/LVET (P = 0.0003) during HDT bed rest over 60 days. Four days after bed rest, LVETI recovered completely to its baseline value. Owing to the relationship between shortening of LVETI and heart failure progression, the easy-to-use oscillometric method might not only be a useful way to evaluate the cardiovascular system during space flights, but could also be of high value in a clinical setting.

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features

Background: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. Methods: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. Results: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves. Conclusion: The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG.

Effects of oxytocin and anaesthesia on vascular tone in pregnant women: a randomised double-blind placebo-controlled study using non-invasive pulse wave analysis

BACKGROUND

Oxytocin is an uterotonic drug with profound cardiovascular effects, which in compromised patients could lead to serious events. The objective was to investigate whether oxytocin affects cardiac function and vascular tone in large and small arteries. We hypothesized that oxytocin decreases arterial vascular tone and elevates cardiac output.

METHODS

51 pregnant women were randomised to treatment with 8.3 μg (5 U) oxytocin or placebo injection during first trimester surgical evacuation of the gravid uterus under general anaesthesia. Oxytocin or placebo was administered once either early or late in the procedure, in a double-blind fashion. Digital photoplethysmography pulse wave analysis variables, heart rate, mean arterial blood pressure and electrocardiographic ST index were recorded before and after anaesthesia and after each injection. Non-parametric statistics were used with a two-sided P value < 0.05 considered significant.

RESULTS

Anaesthesia induced a significant fall in blood pressure, heart rate and vascular tone in small and peripheral arteries. Oxytocin had a vasodilatory effect on small and peripheral arteries and increased the left cardiac ventricular ejection time. The ST index decreased.

CONCLUSIONS

Pulse wave analysis indicated peripheral vasodilation and increased cardiac output after oxytocin, implying increased myocardial oxygen demand. These effects might have been enhanced by the vasodilating effects of anaesthesia. Previous studies have demonstrated myocardial ischaemia after oxytocin, as reflected by a decrease in ST index in the present study.

TRIAL REGISTRATION

Trial registration number ISRCTN17860978 , 2018/03/14, Retrospectively registered.

A Novel AMARS Technique for Baseline Wander Removal Applied to Photoplethysmogram

A new digital filter, AMARS (aligning minima of alternating random signal) has been derived using trigonometry to regulate signal pulsations inline. The pulses are randomly presented in continuous signals comprising frequency band lower than the signal's mean rate. Frequency selective filters are conventionally employed to reject frequencies undesired by specific applications. However, these conventional filters only reduce the effects of the rejected range producing a signal superimposed by some baseline wander (BW). In this work, filters of different ranges and techniques were independently configured to preprocess a photoplethysmogram, an optical biosignal of blood volume dynamics, producing wave shapes with several BWs. The AMARS application effectively removed the encountered BWs to assemble similarly aligned trends. The removal implementation was found repeatable in both ear and finger photoplethysmograms, emphasizing the importance of BW removal in biosignal processing in retaining its structural, functional and physiological properties. We also believe that AMARS may be relevant to other biological and continuous signals modulated by similar types of baseline volatility.

Ejection time: influence of hemodynamics and site of measurement in the arterial tree

The left ventricular ejection time is routinely measured from a peripheral arterial waveform. However, the arterial waveform undergoes constant transformation as the pulse wave propagates along the arterial tree. Our goal was to determine if the left ventricular ejection time measured peripherally in the arterial tree accurately reflected the ejection time measured through the aortic valve. Moreover, we examined/accessed the modulating influence of hemodynamics on ejection time measurements. Continuous wave Doppler waveform images through the aortic valve and the simultaneously obtained radial artery pressure waveforms were analyzed to determine central and peripheral ejection times, respectively. The peripheral ejection time was significantly longer than the simultaneously measured central ejection time (174.5±25.2 ms vs. 120.7±14.4 ms; P<0.0001; 17.4±8.7% increase). Moreover, the ejection time prolongation was accentuated at lower blood pressures, lower heart rate and lower pulse wave velocity. The time difference between centrally and peripherally measured ejection times likely reflects intrinsic vascular characteristics. Moreover, given that the ejection time also depends on blood pressure, heart rate and pulse wave velocity, peripherally measured ejection times might need to be adjusted to account for changes in these variables.

Multi-channel audio-based estimation of the Pre-Ejection Period

Systolic time intervals (STI) have significant diagnostic and prognostic value to assess the global cardiac function. Presently, STIs are regarded as a promising tool for long-term follow-up of patients with chronic cardiovascular diseases. Heart sound has proven to be a valuable approach for STI estimation, in particular for the Pre-Ejection Period (PEP). However, since the optimal auscultation site varies from individual to individual, as well as with the position of the body, its application in single-channel and fixed auscultation site setups poses practical difficulties. Hence, we extend our previous work on PEP estimation to a multi-channel sound acquisition setup, where signal redundancy is exploited. A channel selection method is proposed and the best channel is selected for PEP estimation. As a preliminary study, the devised algorithms were evaluated with respect to echocardiography reference on a set of 236 heartbeats collected from 8 healthy subjects in two sound auscultation sites. The channel selection approach led to 8.4% estimation error decrease, in comparison to a single-channel approach. Current results support our assumption that a multi-channel audio-based strategy can be applied to assess STI in personal health application scenarios.

Assessment of cardiovascular function from multi-Gaussian fitting of a finger photoplethysmogram

Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates from analysis of the photoplethysmographic (PPG) signal alone.We propose using a multi-Gaussian (MG) model consisting of five Gaussian functions to decompose the PPG pulses into its main physiological components. From the analysis of these components, we aim to extract estimators of the left ventricular ejection time, blood pressure and vascular tone changes. Using a multi-derivative analysis of the components related with the systolic ejection, we investigate which are the characteristic points that best define the left ventricular ejection time (LVET). Six LVET estimates were compared with the echocardiographic LVET in a database comprising 68 healthy and cardiovascular diseased volunteers. The best LVET estimate achieved a low absolute error (15.41   ±   13.66 ms), and a high correlation (ρ = 0.78) with the echocardiographic reference.To assess the potential use of the temporal and morphological characteristics of the proposed MG model components as surrogates for blood pressure and vascular tone, six parameters have been investigated: the stiffness index (SI), the T1_d and T1_2 (defined as the time span between the MG model forward and reflected waves), the reflection index (RI), the R1_d and the R1_2 (defined as their amplitude ratio). Their association to reference values of blood pressure and total peripheral resistance was investigated in 43 volunteers exhibiting hemodynamic instability. A good correlation was found between the majority of the extracted and reference parameters, with an exception to R1_2 (amplitude ratio between the main forward wave and the first reflection wave), which correlated low with all the reference parameters. The highest correlation ([Formula: see text] = 0.45) was found between T1_2 and the total peripheral resistance index (TPRI); while in the patients that experienced syncope, the highest agreement ([Formula: see text] = 0.57) was found between SI and systolic blood pressure (SBP) and mean blood pressure (MBP).In conclusion, the presented method for the extraction of surrogates of cardiovascular performance might improve patient monitoring and warrants further investigation.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Clinical and imaging predictors of 1-year and long-term mortality in light chain (AL) amyloidosis: a 5-year follow-up study

Light chain amyloidosis (AL) involves multiorgan failure induced by amyloidogenic light chain proteins, and is associated with high mortality. We aimed to identify clinical, laboratory, and imaging parameters that would predict 1-year and long-term AL mortality. Forty-four biopsy-proven AL patients (61.5 ± 12 years, 20 females) underwent clinical evaluation including laboratory assays, echocardiography, and contrast cardiac magnetic resonance imaging (CMR, n = 31) prior to chemotherapy. Patients were prospectively followed for median duration of 62.7 months (interquartile range 35.5 months). Clinical and laboratory parameters were compared between 1-year survivors and nonsurvivors. Univariate Kaplan-Meier survival plots were calculated followed by stepwise logistic regression analysis to assess independent predictors of long-term survival. Eighteen (40.9 %) patients died within 1 year and an additional 10 subjects died during long-term follow-up. Patients who expired within 1 year presented with more advanced class of heart failure, higher alkaline phosphatase and uric acid, lower limb lead voltage on electrocardiography, shorter left ventricular ejection time (ET) on echocardiography, and a higher proportion of late gadolinium enhancement on CMR. On multivariable analysis, only ET ≤240 ms on echocardiography (hazard ratio (HR) 5.07, 95 % confidence interval (CI) 1.83-14.1, P = 0.002) and New York Heart Association functional class II-IV presentation (HR 1.0058, 95 % CI 1.0014-1.0103, P = 0.01) were independent predictors of AL mortality. In conclusion, AL amyloidosis is associated with high 1-year and long-term mortality. Among clinical, laboratory, and imaging parameters tested, an echocardiographic finding of ET ≤240 ms has independent and additive prognostic value to clinical heart failure evaluation in determining long-term survival of AL patients. This result may be important in the early identification of patients at risk.

Multi-Gaussian fitting for the assessment of left ventricular ejection time from the photoplethysmogram

The Left ventricular ejection time (LVET) is one of the primary surrogates of the left ventricular contractility and stroke volume. Its continuous monitoring is considered to be a valuable hypovolumia prognostic parameter and an important risk predictor in cardiovascular diseases such as cardiac and light chain amyloidosis. In this paper, we present a novel methodology for the assessment of LVET based the Photoplethysmographic (PPG) waveform. We propose the use of Gaussian functions to model both systolic and diastolic phases of the PPG beat and consequently determine the onset and offset of the systolic ejection from the analysis of the systolic phase 3(rd) derivative. The results achieved by the proposed methodology were compared with the algorithm proposed by Chan et al. [1], revealing better estimation of LVET (15.84 ± 13.56 ms vs 23.01 ± 14.60 ms), and similar correlation with the echocardiographic reference (0.73 vs 0.75).

Comparison of systolic time interval measurement modalities for portable devices

Systolic time intervals (STI) have shown significant diagnostic and prognostic value to assess the global cardiac function. Their value has been largely established in hospital settings. Currently, STI are considered a promising tool for long-term patient follow-up with chronic cardiovascular diseases. Several technologies exist that enable beat-by-beat assessment of STI in personal health application scenarios. A comparative study is presented using the echocardiographic gold standard synchronized with impedance cardiography (ICG), phonocardiography (PCG) and photoplethysmography (PPG). The ability of these competing technologies in assessing the pre ejection period (PEP) and the left ventricle ejection time (LVET) is given a general overview with comparative results.

Adaptive threshold method for the peak detection of photoplethysmographic waveform

Photoplethysmography (PPG)-based temporal analyses have been widely used as a useful analytical method in physiological and cardiovascular diagnosis. Most of temporal approaches of PPG are based on detected peak points, peak and foot of PPG. The aim of presented study is the development of improved peak detection algorithm of PPG waveform. The present study demonstrates a promising approach to overcome respiration effect and to detect PPG peak. More extensive investigation is necessary to adapt for the cardiovascular diseases, whose PPG morphology has different form.

Left ventricular ejection time on echocardiography predicts long-term mortality in light chain amyloidosis

OBJECTIVE

Light chain amyloidosis (AL) is associated with high mortality. The aim was to identify echocardiographic parameters that predict AL long-term mortality.

METHODS

Forty-two subjects with biopsy-proven AL (43% were female; aged 61 +/- 12 years) underwent echocardiography and were followed 29 +/- 16 months (median 29.4 months). Standard echocardiographic and clinical parameters and heart failure (HF) class were tested using univariate/multivariable Cox proportional hazard regression analyses to identify markers of mortality.

RESULTS

Twenty-three subjects died, with a 1-year mortality of 44%. Univariate predictors of mortality were HF class (P < .001), left ventricular systolic ejection time (ET) (P = .002), alkaline phosphatase (P < .001), and aspartate and alanine aminotransferase (P = .003 each). On multivariable analysis, only HF class (hazard ratio [HR] 4.86; 95% confidence interval [CI], 1.58-14.9; P = .006), ET (10 ms increase; HR 0.87; CI, 0.78-0.97; P = .01), and alkaline phosphatase (10 U/L increase; HR 1.04; CI, 1.01-1.06; P = .01) were prognostic. ET <or= 240 ms had a sensitivity of 61% and a specificity of 90% in predicting 1-year mortality and a sensitivity of 73% and a specificity of 90% in predicting 1-year cardiac mortality.

CONCLUSION

AL amyloidosis was associated with high long-term mortality. Among echocardiographic and clinical parameters, only ET and alkaline phosphatase had incremental value to HF class in predicting mortality. This may be useful to identify high-risk patients.

Relationship between ocular pulse amplitude and systemic blood pressure measurements

PURPOSE

This study aimed to determine whether ocular pulse amplitude (OPA) measured with dynamic contour tonometry (DCT) is related to systemic blood pressure (BP) parameters.

METHODS

Blood pressure was measured continuously and simultaneously with OPA in one randomly selected eye in 29 healthy subjects. Systemic parameters of interest were: systolic and diastolic BPs and their difference (BP amplitude), and left ventricle ejection time (LVET; defined as the time between the diastolic trough and the incisural notch in the BP curve). In addition, the axial length (AL) of the eye was measured. Associations between OPA, AL and systemic cardiovascular parameters were analysed in a multivariate regression model.

RESULTS

Measurements of OPA ranged from 1.0 mmHg to 4.9 mmHg (mean 2.3 +/- 0.9 mmHg, median 1.9 mmHg). In a univariate analysis with one predictor at a time, means of intraocular pressure (IOP) (p = 0.008), AL (p = 0.046) and LVET (p = 0.037) were significantly correlated with OPA, whereas systolic and diastolic BPs and their amplitude were not. A multiple linear regression analysis showed that mean IOP (p < 0.005), AL (p = 0.01) and LVET (p = 0.002) all independently contributed to OPA.

CONCLUSIONS

The OPA readings measured with DCT in healthy subjects were not related to BP levels and amplitude. It seems that the OPA strongly depends on the time-course of the cardiac contraction. Regulating mechanisms in the carotid system as well as scleral rigidity may be responsible for dampening the direct effect of BP variations.

Assessing PEP and LVET from heart sounds: algorithms and evaluation

This paper addresses the estimation of systolic time intervals, namely the pre-ejection period (PEP) and the left ventricular ejection time (LVET), using heart sound. PEP is estimated with a Bayesian approach resorting to the signal's instantaneous amplitude and typical time intervals between atrio-ventricular valve closure and aortic valve opening. As for LVET, aortic valve closure is determined through the analysis of a high-frequency signature of S2. Additionally, LVET has also been estimated from a PPG signal at a peripheral site, for the sake of comparison over a subset of data. We evaluated our algorithms on a set of 658 heartbeats and achieved 10.32 msec average absolute PEP estimation error with 7.3 msec standard deviation and for LVET, 15.8 msec average estimation error with 13.6 msec standard deviation. Current results support our assumption that heart sounds can be applied to detect the onset of the aortic valve movement processes.

Automatic physiological waveform processing for FMRI noise correction and analysis

Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.