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- | ====== | + | ====== Photoplethysmography (PPG)====== |
- | The Photoplethysmography (PPG) also known as Blood Volume Pulse (BVP) is a non-invasive, | + | ====== Overview ====== |
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+ | The Photoplethysmography (PPG) also known as Blood Volume Pulse (BVP) is a non-invasive, | ||
====== Background ====== | ====== Background ====== | ||
- | Background | + | |
The term photoplethysmography was first introduced by Alrick Herzman and colleagues from the Department of Physiology at St. Louis University School of Medicine in the 1930s as “photoelectric plethysmography” based on the principle of light absorption by transilluminated tissue which varies based on the blood content. In 1938, Hertzman performed a validation of his PPG method comparing blood volume changes with measurements taken simultaneously by mechanical plethysmography. He then contended that a good skin contact for this method is required but without a pressure that can cause blanching of the site of measurement. He further concluded that sensor displacement can affect the measurement accuracy. But it was not until the development of pulse oximeter in the 1970s to measure patients’ blood oxygen saturation that a major advancement in clinical use of PPG was introduced which led to developments in computer-based digital signal processing and analysis. | The term photoplethysmography was first introduced by Alrick Herzman and colleagues from the Department of Physiology at St. Louis University School of Medicine in the 1930s as “photoelectric plethysmography” based on the principle of light absorption by transilluminated tissue which varies based on the blood content. In 1938, Hertzman performed a validation of his PPG method comparing blood volume changes with measurements taken simultaneously by mechanical plethysmography. He then contended that a good skin contact for this method is required but without a pressure that can cause blanching of the site of measurement. He further concluded that sensor displacement can affect the measurement accuracy. But it was not until the development of pulse oximeter in the 1970s to measure patients’ blood oxygen saturation that a major advancement in clinical use of PPG was introduced which led to developments in computer-based digital signal processing and analysis. | ||
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There are several factors that can alter the PPG recordings that can be classified as sensing architecture, | There are several factors that can alter the PPG recordings that can be classified as sensing architecture, | ||
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+ | //Factors affecting the PPG recordings [[https:// | ||
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+ | ====== Signal Components ====== | ||
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+ | PPG Signal is composed of AC (pulsatile) and DC (superimposed) components. The former arises from heartbeats provided by the cardiac synchronous variation in blood volume while the latter is formed by respiration, | ||
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+ | //AC component of the PPG signal &the corresponding ECG[[https:// | ||
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+ | ====== Analysis ====== | ||
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+ | PPG signals are susceptible to motion artifacts that are typically observed in hand movements as well as environmental noise which affect the signal acquisition and estimation accuracy of the heart rate that is measured by the inter-beat interval of the signal. One of the pathways to remedy this limitation involves a 2-stage process where initially the corrupt signal is detected using Short Term Fourier Transform (STFT) while a subsequent step applies Lomb-Scargle Periodogram (LSP) to approximate the Power Spectral Density (PSD) of the signal. This solution is proved to be effective to remove short-term disturbances to the signal. While a clean ECG signal could have been analysed for frequency-based features in HRV, through the process described, the algorithm provides the same possibility for PPG signal analysis. | ||
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+ | ====== Remote PPG ====== | ||
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+ | There have been several methods developed for contactless PPG known as remote PPG (rPPG) over the years whose principle of operation relies on a digital camera to capture video footage from an isolated body part, typically the fingertip that can then estimate the heart rate by tracking the skin color changes due to cardiovascular activity unnoticeable to the human eye. A recent technique relies on Convolutional Neural Networks (CNN) to read and process digital images based on the color intensity of pixels to obtain PPG data and extract signals. | ||
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+ | ====== Available Sensors on the Market ====== | ||
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+ | For medical/ | ||
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+ | BITalino BVP finger clip is a wired transmission-mode sensor that works in conjunction with other BITalino sensors that are sold separately or as part of a sensor kit. It offers a preconditioned analog output with a high signal-to-noise ratio currently retailed at €240 as standalone or paired with an all-in-one board (MCU, power, and Bluetooth) for €65. | ||
+ | For biofeedback training, FDA-certified NeXus EXG is a wired sensor kit that measures BVP in conjunction with other biosignals such as EDA, respiration, | ||
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+ | Valencell, a company producing high-performance PPGs, offers their sensor package (hardware, optomechanical design, firmware and algorithms) to be integrated into wearable (wrist/arm bands) and hearable (earbuds) designs. Their PPG is equipped with active signal characterization to remove noise from optical signals during heavy activity and challenging environments coupled with a low-power accelerometer. | ||
+ | [[https:// | ||
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+ | ===== References ===== | ||
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+ | * Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. // Physiological Measurement, | ||
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+ | * Ayesha, A. H., Qiao, D., & Zulkernine, F. (2021). Heart Rate Monitoring Using PPG With Smartphone Camera. //2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)//, 2985–2991. [[https:// | ||
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+ | * BIOPAC Systems Inc. (2022). //Blood Volume//. [[https:// | ||
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+ | * Ghamari, M. (2018). | ||
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+ | * Luo, S., Zhou, J., Duh, H. B.-L., & Chen, F. (2017). BVP Feature Signal Analysis for Intelligent User Interface. // | ||
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+ | * Picard, R. W. (1997). //Affective computing// | ||
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+ | * Zhan, Q., Wang, W., & de Haan, G. (2020). Analysis of CNN-based remote-PPG to understand limitations and sensitivities. // | ||
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+ | ----- | ||
+ | ===== Notes===== | ||
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+ | - Fan, Y., & Sciotto, F.M. (2013). BioSync: An Informed Participatory Interface for Audience Dynamics and Audiovisual Content Co-creation using Mobile PPG and EEG. //NIME//; Fan, Y., & Sciotto, F.M. (2013). Time Giver: An Installation of Collective Expression using Mobile PPG and EEG in the AlloSphere. | ||
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