Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

https://iopscience.iop.org/article/10.1088/0967-3334/28/3/R01/pdf

Photoplethysmography and its application in clinical physiological measurement.

Analysis of the contour of the peripheral pulse to assess arterial properties was first described in the nineteenth century. With the recognition of the importance of arterial stiffness there has been a resurgence of interest in pulse wave analysis, particularly the analysis of the radial pressure pulse acquired using a tonometer. An alternative technique utilizes a volume pulse. This may conveniently be acquired optically from a finger (digital volume pulse). Although less widely used, this technique deserves further consideration because of its simplicity and ease of use. As with the pressure pulse, the contour of the digital volume pulse is sensitive to changes in arterial tone induced by vasoactive drugs and is influenced by ageing and large artery stiffness. Measurements taken directly from the digital volume pulse or from its second derivative can be used to assess these properties. This review describes the background to digital volume pulse contour analysis, how the technique relates to contour analysis of the pressure pulse, and current and future

Contour analysis of the photoplethysmographic pulse measured at the finger.

We present HealthGear, a real-time wearable system for monitoring, visualizing and analyzing physiological signals. HealthGear consists of a set of noninvasive physiological sensors wirelessly connected via Bluetooth to a cell phone which stores, transmits and analyzes the physiological data, and presents it to the user in an intelligible way. In this paper, we focus on an implementation of HealthGear using a blood oximeter to monitor the user's blood oxygen level and pulse while sleeping. We also describe two different algorithms for automatically detecting sleep apnea events, and illustrate the performance of the overall system in a sleep study with 20 volunteers.

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HealthGear: a real-time wearable system for monitoring and analyzing physiological signals

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

/pdf/advances-in-photopletysmography-signal-analysis-for-3f8fj4337q.pdf

Advances in Photopletysmography Signal Analysis for Biomedical Applications

Cardiovascular disease (CVD) is currently the biggest single cause of mortality in the developed world, hence, the early detection of its onset is vital for effective prevention therapies. Aortic stiffness as measured by aortic pulse wave velocity (PWV) has been shown to be an independent predictor of CVD, however, the measurement of PWV is complex and time consuming. Recent studies have shown that pulse contour characteristics depend on arterial properties such as arterial stiffness. This paper presents a method for estimating PWV from the digital volume pulse (DVP), a waveform that can be rapidly and simply acquired by measuring the transmission of infra-red light through the finger pulp. PWV and DVP were measured on 461 subjects attending a clinic in South East London. Techniques for extracting features from the DVP contour based on physiology and information theory were compared. Low and high stiffness were defined according to a threshold level of PWV chosen to be 10 m/s. Using a support vector machine-based classifier, it is possible to achieve high overall classification rates on unseen data. Further, the use of support vector regression techniques lead to a direct real-valued estimate of PWV which outperforms previous methods based on multilinear regression. We, therefore, conclude that support vector machine-based classification and regression techniques provide effective prediction of arterial stiffness from the simple measurement of the digital volume pulse. This technique could be usefully employed as a cheap and effective CVD screening technique for use in general practice clinics.

Predicting Arterial Stiffness From the Digital Volume Pulse Waveform

Qualitative assessment of the overall clinical status of the subject and characterization of complex cardiovascular dynamics from digital blood volume pulsations measured noninvasively using a photo-plethysmographic device is addressed. A novel concept is employed to detect the dominant nonsinusoidal periodicity embedded in the data series and to extract the associated periodic component. The detection and the extraction of periodic component is performed with moving window to accommodate the variations of the physiological oscillations. The covariance matrix formed by the gradually varying pattern is used as a simple measure of qualitative assessment. Further, the characterization of the underlying system in the light of nonlinear dynamical analysis is also presented. The stable subjects are shown to behave as a low-dimensional system whereas the diseased subjects exhibit comparatively high dimensional activity.

Analysis and characterization of photo-plethysmographic signal

Scalar and vector data arising from the numerical simulation of flows and transport equations encode a wealth of information that needs to be extracted and displayed so as to gain insight into the underlying patterns. In this respect, it is an increasingly important task to process, represent, visualize, and analyze flow data. This paradigm maps an array of discrete data values either to geometric elements followed by graphics rendering or directly to pixels. Coupled with this pipeline of numerical flow visualization is an emerging preprocessing stage of great promise that unleashes the power of artificial intelligence, particularly machine learning, to perform feature detection and pattern recognition. In light of the importance of computer graphics and machine learning to data analysis, the editors conceived the idea of a special issue, with the core purpose of extracting and exploiting information by means of numerical flow visualization to help with scientific discovery. 

V. Muralidhar

Papers

Analysis and characterization of photo-plethysmographic signal

Preface: numerical flow visualization: from the perspectives of computer graphics and machine learning