Photoplethysmogram Pulse Research Articles

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data.

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters.

N°77 – Analysis of variations in the pulse photo-plethysmogram and distribution of the reactivity index on polysomnographic recordings

N°77 – Analysis of variations in the pulse photo-plethysmogram and distribution of the reactivity index on polysomnographic recordings

Mathematical model of the photoplethysmogram for testing methods of biological signals analysis

The purpose of this study was to develop a mathematical model of the photoplethysmogram, which can be used to test methods that introduce the instantaneous phases of the modulating signals. The model must reproduce statistical and spectral characteristics of the real photoplethysmogram, and explicitly incorporate the instantaneous phases of the modulating signals, so they can be used as a reference during testing. Methods. Anacrotic and catacrotic phases of the photoplethysmogram pulse wave were modeled as a sum of two density distributions for the skew normal distribution. The modulating signals were introduced as harmonic functions taken from the experimental instantaneous phases of the VLF (0.015...0.04 Hz), LF (0.04...0.15 Hz) and HF (0.15...0.4 Hz) oscillations in the real photoplethysmogram. The spectral power in the VLF, LF, and HF frequency ranges was calculated to compare the model and experimental data. Results. The model qualitatively reproduces the shape of the experimental photoplethysmogram pulse wave and shows less than 1% error when simulating the spectral properties of the signal. Conclusion. The proposed mathematical model can be used to test the methods for introduction of the instantaneous phases of the modulating signals in photoplethysmogram time-series.

Photoplethysmographic Waveform and Pulse Rate Variability Analysis in Hyperbaric Environments.

The main aim of this work is to identify alterations in the morphology of the pulse photoplethysmogram (PPG) signal, due to the exposure of the subjects to a hyperbaric environment. Additionally, their Pulse Rate Variability (PRV) is analysed to characterise the response of their Autonomic Nervous System (ANS). To do that, 28volunteers are introduced into a hyperbaric chamber and five sequential stages with different atmospheric pressures from 1 atm to 5 atm are performed. In this work, nineteen morphological parameters of the PPG signal are analysed: the pulse amplitude; eight parameters related to pulse width; eight parameters related to pulse area; and the two two pulse slopes. Also, classical time and frequency parameters of PRV are computed. Notable widening of the pulses width is observed in the stages analysed. The PPG area increases with pressure, with no significant changes when the initial pressure is recovered. These changes in PPG waveform may be caused by an increase in the systemic vascular resistance as a consequence of of vasoconstriction in the extremities, suggesting a sympathetic activation. However, the PRV results show an augmented parasympathetic activity and a reduction in the parameters that characterise the sympathetic response. So, only a sympathetic activation is detected in the peripheral region, as reflected by PPG morphology. The information regarding the ANS and the cardiovascular response that can be extracted from the PPG signal, as well as its compatibility with wet conditions make this signal the most suitable for studying the physiological response in hyperbaric environments.

Detrended fluctuation analysis of photoplethysmogram pulse intervals during sleep disordered breathing

Detrended fluctuation analysis of photoplethysmogram pulse intervals during sleep disordered breathing

Comparison of MLP and REN classifiers for detection of hemodynamic stress using photoplethysmograph

The aim of this study is to show a comparison of the Multi-Layered Perceptron (MLP) neural network and Recurrent Elman Network (REN) in determining false positives for Pulse Photoplethysmogram (PPG) recorded during rest and recovery phase after exercise. Several time domain features, depicting the signal morphology and time indices were identified for classification and the robustness of the neural networks were examined using Classification Accuracy (CA) and Receiver Operating Characteristics (ROC). The obtained CA’s were 91% (MLP) and 96.75% (REN) for Reflection Index (RI) and 90% (MLP) and 94% (REN) for Stiffness Index (SI). Besides, the REN is slightly better with a ROC index of 0.94485 and 0.96742 for RI and SI. From the obtained results, it can be concluded that the REN was found to detect lower false positives when compared to the MLP network.

Finger photoplethysmogram pulse amplitude changes induced by flow-mediated dilation

This study was conducted to investigate the utility and efficacy of finger photoplethysmogram pulse amplitude (PPG-AC) in comparison with the standard Doppler ultrasound in assessing an endothelial function via flow-mediated dilation (FMD). High-resolution B-mode scanning of the right brachial artery (BA) of 31 healthy subjects aged 39.7 ± 11.3 (range 22–64) years and 52 risk subjects aged 47.7 ± 10.8 (range 30–65) years were performed before and after 4 min of upper arm occlusion. Concurrent with the ultrasound measurement (where color Doppler imaging was used to enhance arterial boundary detection), PPG signals were recorded from both index fingers for cross evaluation and comparison. Our results show that the finger PPG-AC exhibits a similar response to that of the well-known BA dilation: following the release of pressure (cuff around the BA), the PPG-AC increases abruptly before slowly decreasing toward the baseline. The peak PPG-AC is reached significantly faster than the peak FMD measured by ultrasound among healthy and risk groups (P < 0.001). The proposed technique using a finger photoplethysmogram can be applied in a rapid and non-invasive assessment of peripheral vascular functions as an alternative low-cost and less operator-dependent tool compared to ultrasound.

Noninvasive cardiovascular markers of acoustically induced arousal from non-rapid-eye-movement sleep.

Changes in cardiovascular measures such as heart rate (HR) and pulse transit time (PTT) have been advocated as sensitive markers of autonomic arousal from sleep. In animal studies, alerting stimuli produce particularly marked skin vascular responses. We hypothesized that changes in skin vascular conductance would provide more sensitive markers of autonomic arousal during sleep compared to central cardiovascular response measures such as HR and PTT. Cardiovascular responses to auditory-induced arousals were recorded during overnight sleep studies. Sleep disorders unit in a 270-bed teaching hospital. Eleven young healthy male subjects. Throughout ovemight sleep studies, auditory tones (5-second duration, 54-90 decibels, 22-56 per subject) were presented during non-rapid-eye-movement sleep. Beat-by-beat HR, PTT, laser-Doppler fingertip skin blood flow (SBF) and finger and ear photoplethysmogram pulse wave amplitudes (PWA) were measured in the 20 seconds preceding and 30 seconds following each tone and compared to control measurements obtained during 50-second periods of recording with no stimulus (no tone, 6-22 per subject). Electroencephalographic (EEG) arousals were scored according to standard criteria (American Sleep Disorders Association) into no discemible, 3- to 10-second duration, or 10- to 15-second duration arousals. Poststimulus cardiovascular measurements were expressed as a percentage of the prestimulus mean and response magnitudes quantified from peak responses and the area under the poststimulus response curve. The ability of each cardiovascular response measure to discriminate EEG arousals (EEG changes lasting more than 3 seconds) was assessed from the area under the receiver operating characteristic (ROC) curve. There were no significant changes in any cardiovascular parameter during control recordings. In contrast to all other parameters, finger PWA and SBF decreased following tones that produced no discernible EEG arousal (P < 0.05). A significant HR rise and decreases in all cardiovascular measures occurred with greater than 3-second arousals, with longer duration arousals generally exhibiting larger responses. Conventional EEG arousals (greater than 3 seconds) were relatively poorly detected from HR responses (ROC area HR rise 0.80 +/- 0.04) compared to changes in SBF (0.85 +/- 0.02), PTT (0.85 +/- 0.03) and finger PWA (0.90 +/- 0.01). Decreases in skin vascular conductance (finger PWA and SBF) provide sensitive markers of autonomic arousal during sleep. They are at least as sensitive as PTT for detecting conventionally scored EEG arousals and may be more sensitive in detecting "subcortical" arousals.