Current progress of photoplethysmography and SPO2 for health monitoring

Tamura, Toshiyo

doi:10.1007/s13534-019-00097-w

Cited by 177 publications

(152 citation statements)

References 165 publications

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“…Technologies for accurately monitoring pulse rate (PR) and oxygen saturation (SpO 2 ) are especially sought after for numerous applications, such as the ability to evaluate general health, and changes in physical and cognitive performance [2,3]. One of the most common techniques for evaluating measurements of blood oxygen saturation and cardiovascular performance is pulse oximetry by photoplethysmography (PPG) [4][5][6]. Photoplethysmography is a simple, non-invasive, low cost, and portable optical technique that can measure pulse rate, oxygen saturation, blood pressure, and cardiac output, and it is generally accepted as providing valuable information about the general functioning, as well as acute changes, of the cardiovascular system [2,7].…”

Section: Introductionmentioning

confidence: 99%

“…While PPG has been studied and used at various anatomical locations with varied applications, there are a number of limitations to the accuracy and effectiveness of these techniques, especially in dynamic or hypoxic environments [6,14]. It is known that the location of the sensor, high motion artifact, and issues with implementation of skin contact may compromise the effectiveness of the PPG sensor measurements [4,12,15].…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Photoplethysmography Behind the Ear for Pulse Oximetry in Hypoxic Conditions with a Novel Device (SPYDR)

Bradke

Everman²

2020

Biosensors

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Photoplethysmography (PPG) is a valuable technique for noninvasively evaluating physiological parameters. However, traditional PPG devices have significant limitations in high-motion and low-perfusion environments. To overcome these limitations, we investigated the accuracy of a clinically novel PPG site using SPYDR®, a new PPG sensor suite, against arterial blood gas (ABG) measurements as well as other commercial PPG sensors at the finger and forehead in hypoxic environments. SPYDR utilizes a reflectance PPG sensor applied behind the ear, between the pinna and the hairline, on the mastoid process, above the sternocleidomastoid muscle, near the posterior auricular artery in a self-contained ear cup system. ABG revealed accuracy of SPYDR with a root mean square error of 2.61% at a 70–100% range, meeting FDA requirements for PPG sensor accuracy. Subjects were also instrumented with SPYDR, as well as finger and forehead PPG sensors, and pulse rate (PR) and oxygen saturation (SpO2) were measured and compared at various reduced oxygen profiles with a reduced oxygen breathing device (ROBD). SPYDR was shown to be as accurate as other sensors in reduced oxygen environments with a Pearson’s correlation >93% for PR and SpO2. In addition, SPYDR responded to changes in SpO2 up to 50 s faster than PPG measurements at the finger and forehead.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of Photoplethysmography Behind the Ear for Pulse Oximetry in Hypoxic Conditions with a Novel Device (SPYDR)

Bradke

Everman²

2020

Biosensors

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“…1. The PPG waveform is easily affected by motion artifacts, leading to errors in the measurement [21][22][23][24][25]. Most motion artifacts associate with the sensor motion relative to the skin [26].…”

Section: Introductionmentioning

confidence: 99%

“…Cold temperature triggered by diseases can also reduce the correlation between peripheral pulsation and blood pressure [25]. High blood viscosity reduces blood flow and significantly impacts the PPG waveform [27].…”

mentioning

confidence: 99%

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

Tjahjadi¹,

Ramli²

2020

Information

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Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classification accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG-BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.Information 2020, 11, 93 2 of 18 blood volume changes, which occur between the systolic and diastolic phases of the cardiac cycle. The essential frequency of the AC component depends on the heart rate and is covered onto the DC component [11]. An LED is a light source that can be used to illuminate blood vessels so minor perfusion changes can be supervised on the photodetectors [12]. Perfusion is measured as the degree at which blood is distributed to tissue [13].

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“…PPG [1] is a promising biometric technique based on Lambert-Beer's law [2] and the difference in spectral absorption characteristics of human skin and blood to convert optical signals into blood volume and blood flow information. It can be used for noninvasive detection of microvascular blood flow changes, providing quantities of possibilities in detecting blood volume and blood flow parameters [3][4][5]. Unfortunately, the noninvasiveness of PPG has both advantages and disadvantages: PPG signals are susceptible to disturbances from external environment and thus it causes inaccuracies to the measured results and those disturbances, including respiratory activities (RA), motion artifacts (MA), power line interference, and high-frequency noise generated by electronic components, tend to cause PPG signals to be doped with nonlinear and nonstationary components, which can result in spectral aliasing and distortion when processed with traditional methods.…”

Section: Introductionmentioning

confidence: 99%

SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising

Liu

Wang

et al. 2019

Computational and Mathematical Methods in Medicine

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Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). e EEMD method solves the "mode mixing" problem in EMD effectively, but it can do nothing about the "end effect," another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the "end effect" efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.

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Current progress of photoplethysmography and SPO2 for health monitoring

Cited by 177 publications

References 165 publications

Investigation of Photoplethysmography Behind the Ear for Pulse Oximetry in Hypoxic Conditions with a Novel Device (SPYDR)

Investigation of Photoplethysmography Behind the Ear for Pulse Oximetry in Hypoxic Conditions with a Novel Device (SPYDR)

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising

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