The photoplethysmographic (PPG) waveform, also known as the pulse oximeter waveform, is one of the most commonly displayed clinical waveforms. First described in the 1930s, the technology behind the waveform is simple. The waveform, as displayed on the modern pulse oximeter, is an amplified and highly filtered measurement of light absorption by the local tissue over time. It is optimized by medical device manufacturers to accentuate its pulsatile components. Physiologically, it is the result of a complex, and not well understood, interaction between the cardiovascular, respiratory, and autonomic systems. All modern pulse oximeters extract and display the heart rate and oxygen saturation derived from the PPG measurements at multiple wavelengths. "As is," the PPG is an excellent monitor for cardiac arrhythmia, particularly when used in conjunction with the electrocardiogram (ECG). With slight modifications in the display of the PPG (either to a strip chart recorder or slowed down on the monitor screen), the PPG can be used to measure the ventilator-induced modulations which have been associated with hypovolemia. Research efforts are under way to analyze the PPG using improved digital signal processing methods to develop new physiologic parameters. It is hoped that when these new physiologic parameters are combined with a more modern understanding of cardiovascular physiology (functional hemodynamics) the potential utility of the PPG will be expanded. The clinical researcher's objective is the use of the PPG to guide early goal-directed therapeutic interventions (fluid, vasopressors, and inotropes), in effect to extract from the simple PPG the information and therapeutic guidance that was previously only obtainable from an arterial pressure line and the pulmonary artery catheter.

Lower body negative pressure (LBNP) creates a reversible hypovolemia by sequestrating blood volume in the lower extremities. This study sought to examine the impact of central hypovolemia on peripheral venous pressure (PVP) waveforms in spontaneously breathing subjects. With IRB approval, 11 healthy subjects underwent progressive LBNP (baseline, -30, -75, and -90 mmHg or until the subject became symptomatic). Each was monitored for heart rate (HR), finger arterial blood pressure (BP), a chest respiratory band and PVP waveforms which are generated from a transduced upper extremity intravenous site. The first subject was excluded from PVP analysis because of technical errors in collecting the venous pressure waveform. PVP waveforms were analyzed to determine venous pulse pressure, mean venous pressure, pulse width, maximum and minimum slope (time domain analysis) together with cardiac and respiratory modulations (frequency domain analysis). No changes of significance were found in the arterial BP values at -30 mmHg LBNP, while there were significant reductions in the PVP waveforms time domain parameters (except for 50% width of the respiration induced modulations) together with modulation of the PVP waveform at the cardiac frequency but not at the respiratory frequency. As the LBNP progressed, arterial systolic BP, mean BP and pulse pressure, PVP parameters and PVP cardiac modulation decreased significantly, while diastolic BP and HR increased significantly. Changes in hemodynamic and PVP waveform parameters reached a maximum during the symptomatic phase. During the recovery phase, there was a significant reduction in HR together with a significant increase in HR variability, mean PVP and PVP cardiac modulation. Thus, in response to mild hypovolemia induced by LBNP, changes in cardiac modulation and other PVP waveform parameters identified hypovolemia before detectable hemodynamic changes.

<p style='text-indent:20px;'>Phase is the most fundamental physical quantity when we study an oscillatory time series. There have been many tools aiming to estimate phase, and most of them are developed based on the analytic function model. Unfortunately, these analytic function model based tools might be limited in handling modern signals with <i>intrinsic nonstartionary</i> structure, for example, biomedical signals composed of multiple oscillatory components, each with time-varying frequency, amplitude, and non-sinusoidal oscillation. There are several consequences of such limitation, and we specifically focus on the one that phases estimated from signals simultaneously recorded from different sensors for the same physiological system from the same subject might be different. This fact might challenge reproducibility, communication, and scientific interpretation. Thus, we need a standardized approach with theoretical support over a unified model. In this paper, after summarizing existing models for phase and discussing the main challenge caused by the above-mentioned intrinsic nonstartionary structure, we introduce the <i>adaptive non-harmonic model (ANHM)</i>, provide a definition of phase called fundamental phase, which is a vector-valued function describing the dynamics of all oscillatory components in the signal, and suggest a time-varying bandpass filter (tvBPF) scheme based on time-frequency analysis tools to estimate the fundamental phase. The proposed approach is validated with a simulated database and a real-world database with experts' labels, and it is applied to two real-world databases, each of which has biomedical signals recorded from different sensors, to show how to standardize the definition of phase in the real-world experimental environment. We report that the phase describing a physiological system, if properly modeled and extracted, is immune to the selected sensor for that system, while other approaches might fail. In conclusion, the proposed approach resolves the above-mentioned scientific challenge. We expect its scientific impact on a broad range of applications.</p>

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Photoplethysmography (PPG) is increasingly used in digital health, exceptionally in smartwatches. The PPG signal contains valuable information about heart activity, and there is lots of research interest in its means and analysis for cardiovascular diseases. Unfortunately, to our knowledge, there is no arrhythmic PPG dataset publicly available-this paper attempt to provide a toolbox that can generate synthesized arrhythmic PPG signals. The model of a single PPG pulse in this toolbox utilizes two combined Gaussian functions. This toolbox supports synthesizing PPG waveform with regular heartbeats and three irregular heartbeats: compensation, interpolation, and reset. The user can generate a large amount of PPG data with a certain irregularity, with different sampling frequency, time length, and a range of noise types (Gaussian noise and multi-frequency noise) can be added to the synthesized PPG which can all be modified from the interface, and different types of arrhythmic PPGs (as calculated by the model) generated. The generation for large PPG datasets that simulate PPG collected from real humans could be used for testing the robustness of developed algorithms that are targeting arrhythmic PPG signals. Our PPG synthesis tool is publicly available.