Motion artefact minimization from photoplethysmography based non-invasive hemoglobin sensor based on an envelope filtering algorithm

Yuan, Hongwei; Memon, Sanober Farheen; Newe, Thomas; Lewis, Elfed; Leen, Gabriel

doi:10.1016/j.measurement.2017.10.060

Cited by 23 publications

(11 citation statements)

References 35 publications

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“…The amplitude, frequency, and baseline of PPG waveform are modulated by respiration ( Pimentel et al, 2015 ). Compared with normal breathing, slow, and deep breathing enhances the amplitude fluctuations of PPG signal ( Yuan et al, 2018 ). The interaction between breathing pattern and measurement site might influence the PPG waveform, but has not been fully investigated.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Comparison of Photoplethysmographic Waveform Characteristics: Effect of Measurement Site

et al. 2019

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Introduction: Photoplethysmography (PPG) has been widely used to assess cardiovascular function. However, few studies have comprehensively investigated the effect of measurement site on PPG waveform characteristics. This study aimed to provide a quantitative comparison on this.Methods: Thirty six healthy subjects participated in this study. For each subject, PPG signals were sequentially recorded for 1 min from six different body sites (finger, wrist under (anatomically volar), wrist upper (dorsal), arm, earlobe, and forehead) under both normal and deep breathing patterns. For each body site under a certain breathing pattern, the mean amplitude was firstly derived from recorded PPG waveform which was then normalized to derive several waveform characteristics including the pulse peak time (Tp), dicrotic notch time (Tn), and the reflection index (RI). The effects of breathing pattern and measurement site on the waveform characteristics were finally investigated by the analysis of variance (ANOVA) with post hoc multiple comparisons.Results: Under both breathing patterns, the PPG measurements from the finger achieved the highest percentage of analyzable waveforms for extracting waveform characteristics. There were significant effects of breathing pattern on Tn and RI (larger Tn and smaller RI with deep breathing on average, both p < 0.03). The effects of measurement site on mean amplitude, Tp, Tn, and RI were significant (all p < 0.001). The key results were that, under both breathing patterns, the mean amplitude from finger PPG was significantly larger and its Tp and RI were significantly smaller than those from the other five sites (all p < 0.001, except p = 0.04 for the Tp of “wrist under”), and Tn was only significantly larger than that from the earlobe (both p < 0.05).Conclusion: This study has quantitatively confirmed the effect of PPG measurement site on PPG waveform characteristics (including mean amplitude, Tp, Tn, and RI), providing scientific evidence for a better understanding of the PPG waveform variations between different body sites.

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Section: Introductionmentioning

confidence: 99%

Quantitative Comparison of Photoplethysmographic Waveform Characteristics: Effect of Measurement Site

et al. 2019

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“…It has been reported that the correlation coefficients between PPG signals derived from finger and wrist were different when the measurements were performed under the normal and deep breathing patterns (Keikhosravi and Zahedi, 2013). The fluctuation of respiration-related PPG signal tends to be larger under deep breathing than normal breathing (Yuan et al., 2018). Therefore, it is important to consider the effect of breathing pattern in comparing the PPG-derived RFs between different body sites.…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

et al. 2019

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Background: It is known that the respiration-modulated photoplethysmographic (PPG) signals could be used to derive respiratory frequency (RF) and that PPG signals could be measured from different body sites. However, the accuracy of RF derived from PPG signals of different body sites has not been comprehensively investigated. Objective: This study aims to investigate the difference in the accuracy of PPG-derived RFs between measurements from different body sites, respectively, for normal and deep breathing conditions. Methods: Under normal and deep breathing patterns, the PPG signals were recorded sequentially in a randomized order from six body sites [finger, wrist under (anatomically volar), wrist upper (dorsal), earlobe, and forehead] of 36 healthy subjects. Simultaneously, the reference respiratory signal was measured by a respiratory belt on the chest. Using the frequency demodulation approach, respiratory signals were extracted from PPG signals for calculating RF by power spectral density. The bias between PPG-derived and reference RFs was then analyzed statistically using analysis of variance and non-parametric tests, Bland-Altman analysis, and linear regression to investigate the difference in RF bias between different sites. Results: The RF bias was significantly influenced by the breathing pattern and measurement site (both p < 0.001). Under normal breathing, the RF bias was insignificant in the arm, forehead, and wrist under (all p > 0.05) and significant in the other sites (all p < 0.05). Significant linear relationship between PPG-derived and reference RFs existed at all the sites ( p < 0.001) except the wrist upper ( p > 0.05). The linearity between PPG-derived and reference RFs was highest at the forehead (slope of best-fit line: 0.90, R 2 : 0.64), followed by the earlobe, finger, arm, and wrist under (slope: 0.71, R 2 : 0.40). Under deep breathing, there was no significant RF bias in all the measurement sites ( p > 0.05) except forehead ( p = 0.048). The effect of measurement site on RF bias was not significant ( p > 0.05). The finger had the smallest RF bias and the narrowest limits of agreement. Conclusion: This study has demonstrated that the accuracy of PPG-derived RF depends on the measurement site and breathing pattern. The best sites are the forehead and finger, respectively, for normal and deep breathing patterns.

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“…Furthermore, the PPG signals are highly susceptible to movement, which makes their use in cardiopulmonary exercise testing or cardiopulmonary resuscitation difficult [ 131 , 132 ]. There are already research works demonstrating that this problem can minimized or even solved, as it is the case in the work of Yuan et al [ 133 ], who produced a method of improvement for minimization of the artifacts due to the movement performed with a PPG sensor.…”

Section: Discussion and Open Issuesmentioning

confidence: 99%

Advances in Photopletysmography Signal Analysis for Biomedical Applications

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Rocha

Vasconcelos

et al. 2018

Sensors

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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.

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Motion artefact minimization from photoplethysmography based non-invasive hemoglobin sensor based on an envelope filtering algorithm

Cited by 23 publications

References 35 publications

Quantitative Comparison of Photoplethysmographic Waveform Characteristics: Effect of Measurement Site

Quantitative Comparison of Photoplethysmographic Waveform Characteristics: Effect of Measurement Site

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

Advances in Photopletysmography Signal Analysis for Biomedical Applications

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