Optimum Wavelengths in the Near Infrared for Imaging Photoplethysmography

Würtenberger, Felicia; Haist, Tobias; Reichert, Carsten; Faulhaber, Andreas; Boettcher, T.; Herkommer, Alois

doi:10.1109/tbme.2019.2897284

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Alternatively, the PPG technique uses an optical method to describe changes in pulse rate over time and is commonly used for measuring HRV data [38]. Apart from the ECG and PPG signals, there are also other types of signals that can be used to derive HRV data, such as the ballistocardiograph (BCG) [39], heart sound signal [40], Doppler radar [41], and camera-based assessment, e.g., imaging-PPG and video-PPG [42]. In the following sections, we describe two major types of signals (i.e., ECG and PPG) for HRV measurement.…”

Section: Acquisition and Measurement Of Hrvmentioning

confidence: 99%

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Lü

Zhu

Morelli

et al. 2024

IEEE Rev. Biomed. Eng.

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Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

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Section: Acquisition and Measurement Of Hrvmentioning

confidence: 99%

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Lü

Zhu

Morelli

et al. 2024

IEEE Rev. Biomed. Eng.

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“…Using a different approach, Shao et al used a monochromatic camera and illumination at 611 and 880 nm 113 . Both Wurtenberger et al 114 and Rapczynski et al 115 used a hyperspectral video system in the NIR range (600–975 nm) to assess HR estimation performance in different NIR bands. These works and those using monochromatic imaging are subsumed in the “Other” category in Fig.…”

Section: Survey Taxonomymentioning

confidence: 99%

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Antink

Lyra²,

Paul

et al. 2019

Yearb Med Inform

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Objectives: Camera-based vital sign estimation allows the contactless assessment of important physiological parameters. Seminal contributions were made in the 1930s, 1980s, and 2000s, and the speed of development seems ever increasing. In this suivey, we aim to overview the most recent works in this area, describe their common features as well as shortcomings, and highlight interesting “outliers”. Methods: We performed a comprehensive literature research and quantitative analysis of papers published between 2016 and 2018. Quantitative information about the number of subjects, studies with healthy volunteers vs. pathological conditions, public datasets, laboratory vs. real-world works, types of camera, usage of machine learning, and spectral properties of data was extracted. Moreover, a qualitative analysis of illumination used and recent advantages in terms of algorithmic developments was also performed. Results: Since 2016, 116 papers were published on camera-based vital sign estimation and 59% of papers presented results on 20 or fewer subjects. While the average number of participants increased from 15.7 in 2016 to 22.9 in 2018, the vast majority of papers (n=100) were on healthy subjects. Four public datasets were used in 10 publications. We found 27 papers whose application scenario could be considered a real-world use case, such as monitoring during exercise or driving. These include 16 papers that dealt with non-healthy subjects. The majority of papers (n=61) presented results based on visual, red-green-blue (RGB) information, followed by RGB combined with other parts of the electromagnetic spectrum (n=18), and thermography only (n=12), while other works (n=25) used other mono- or polychromatic non-RGB data. Surprisingly, a minority of publications (n=39) made use of consumer-grade equipment. Lighting conditions were primarily uncontrolled or ambient. While some works focused on specialized aspects such as the removal of vital sign information from video streams to protect privacy or the influence of video compression, most algorithmic developments were related to three areas: region of interest selection, tracking, or extraction of a one-dimensional signal. Seven papers used deep learning techniques, 17 papers used other machine learning approaches, and 92 made no explicit use of machine learning. Conclusion: Although some general trends and frequent shortcomings are obvious, the spectrum of publications related to camera-based vital sign estimation is broad. While many creative solutions and unique approaches exist, the lack of standardization hinders comparability of these techniques and of their performance. We believe that sharing algorithms and/ or datasets will alleviate this and would allow the application of newer techniques such as deep learning.

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“…Currently, quite a number of remote non-contact heart rate measurement methods have been developed. Examples include methods based on microwave Doppler radar [10], fast hyperspectral imager with 25 spectral channels in the NIR [11], and thermal imaging [12], but they all require using expensive and specialized hardware.…”

Section: Introductionmentioning

confidence: 99%

“…It uses a camera to collect the reflected light from the skin and analyzes it to obtain heart rate. Existing rPPG methods use Independent Component Analysis (ICA) [13], Principal Component Analysis (PCA) [12], Butterworth pass band filter [14], Random Forest Regression (RFR) [15], and Fast Fourier Transform (FFT) [11], [16] for heart rate measurement. These methods cannot always effectively detach the heart rate signals from the noises brought by the complicated environment and subjects.…”

Section: Introductionmentioning

confidence: 99%

Robust and Remote Measurement of Heart Rate Based on A Surveillance Camera

Yang

Lian

Zhang

et al. 2021

Preprint

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Arrhythmia is a marked symptom of many cardiovascular diseases. The accurate and in time detection of heart rate can greatly reduce its harm to people. However, it is still a challenge to automatedly and remotely measure the heart rate in daily life, because the environment factor of the measurement changes variously, such as the changing light intensity, the movement of people, and the uncertain distance from sensor to people. In this study, we accurately measured the heart rate of people at the distance of 4.8 meters under different intensity of light just by using a surveillance camera. After a short color video (20 sec) of a person's hand was captured by this camera, a method based on Fast Fourier Transform Algorithm (FFT) is proposed to extract the blood volume pulse wave to calculate the heart rate. By comparing the real heart rate with the results measured by electrocardiography (ECG), the accuracy of heart rate measurement using the method proposed in this study is 98.65% within 4.0 meters, and the accuracy can reach 90% within 5.6 meters. Our experiments also demonstrated that this method can accurately obtain the heart rate even when the intensity of light is below 32 LUX ( office environment 300-500 LUX). The strong environmental suitability makes this method can be applied to many occasions, such as community clinic, old peoples' home, classroom, and other public space.

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Optimum Wavelengths in the Near Infrared for Imaging Photoplethysmography

Cited by 10 publications

References 24 publications

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Robust and Remote Measurement of Heart Rate Based on A Surveillance Camera

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