Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring.

Background: Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. Methods: In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine-learning approaches to predict glycemic excursions: a classification model and a regression model. Results: The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. Conclusions: Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

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Photoplethysmography (PPG) sensors, commonly used today in wearable biomedical devices, are an easily deployable low-cost technology to estimate heart rate, heart rate variability, and, when coupled with multiple wavelengths, blood oxygenation. PPG sensors can measure these parameters by measuring volumetric changes in the blood due to light absorption. While the technology is established, its utility is hindered due to several factors, including skin contact pressure. Thus, to advance fitness trackers toward regulated medical devices, confounding factors related to contact pressure and its effect on the PPG wave morphology, intensity, and signal-to-noise ratio (SNR) need to be better understood. Toward that end, this study evaluates the effects of contact pressure between the PPG sensor and the wrist, particularly on the palmar side of the wrist along the radial artery. The amplitude of the AC and DC components of the PPG signal, the perfusion index, and the signal morphology were determined for varying contact pressures utilizing three wavelengths of light, green, red, and infrared (537, 660, and 880 nm, respectively). As pressure was applied and varied, the compression effects on the pulsatile and non-pulsatile components were observed, along with changes in the perfusion index and prominent features of the PPG signal. The results showed significant differences in morphology and intensity, which varied with each PPG wavelength. These varying effects of PPG signal morphology, intensity, and SNR as a function of contact pressure suggest that pressure must be considered with wearable sensing technology and when developing signal analysis methods in order to allow the advancement of fitness trackers toward regulated medical devices.

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Effective staining of peripheral blood smears which enhances the contrast of intracellular components and biomarkers is essential for the accurate characterization, diagnosis, and monitoring of various diseases such as malaria.

Finger-based photoplethysmography (PPG) is traditionally acquired using a finger clip form factor on the fingertip. This form factor is not conducive for long-term measurements during regular everyday activities due to its inability to be easily worn while moving outside a clinical setting. However, advancements in finger-based PPG sensors over the last several decades include ring designs at the proximal phalanx of the finger, which are more conducive to being worn during everyday living conditions. The anatomy of this location is more complex relative to the tip as there is a smaller density of blood vessels. Also, the finger's proximal phalanx is comprised of more opaque tissue and bone, which affects the path of the light. In this study, a ring design that included 8 LEDs/PDs each at 45 degrees that included two wavelengths (940 nm: infrared and 655 nm: red) on the proximal phalanx of the finger was studied in terms of their relative placement to each other for detection. Specifically, the system was used to investigate how the pulsatile and DC component amplitudes of the photodiodes varied around the finger in both transmission and reflectance modes as a function of the 8 LEDs/PDs pairs at 45 degrees on the ring. All 64 combinations of LEDs/PDs located at the proximal phalanx of the finger were investigated. The results showed the locations that yielded the strongest amplitude PPG signals.