A Method for Respiration Rate Detection in Wrist PPG Signal Using Holo-Hilbert Spectrum

Chang, Hsiao-Huang; Hsu, Chuan Chih; Chen, Chia-Yuen; Lee, Wai-Keung; Hsu, Hao-Teng; Shyu, Kuo-Kai; Yeh, Jia-Rong; Lin, Pay Liam; Lee, Po-Lei

doi:10.1109/jsen.2018.2855974

Cited by 31 publications

(12 citation statements)

References 45 publications

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“…DVP signals are generally used to detect time domain properties (e.g., heart rate 1 , respiration rate 2 , etc.) and quantitative parameters (e.g., blood oxygenation 3 , 4 , hypovolemia and hypervolemia 5 , blood glucose level 6 , etc.)…”

Section: Introductionmentioning

confidence: 99%

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Hossain

Gupta

Kwon

et al. 2021

Sci Rep

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Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO2 in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

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Section: Introductionmentioning

confidence: 99%

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Hossain

Gupta

Kwon

et al. 2021

Sci Rep

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“…However, finding the relationship between intra-subject finger PPG and wrist PPG within a given time frame or under specific scenarios is essential to comprehend the applicability of PPG obtained from different measurement sites in daily lives. Motion artifacts and noise often corrupt the signal quality during exercise for subjects wearing a smart watch [ 33 ]. A further study can be carried out in these aspects to gain insight into PPG changes caused by various environmental and physical factors, which should be important for the wider applications of smart watches in daily lives.…”

Section: Discussionmentioning

confidence: 99%

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features

Tsai

Huang

Guo

et al. 2021

Sensors

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Background: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. Methods: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. Results: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves. Conclusion: The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG.

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“…The signal

is parameterized as

and

that correspond to amplitude modulation and frequency modulation, respectively. 21 As previously mentioned, the frequency modulation arises due to the influence of the autonomous nervous system.…”

Section: Methodsmentioning

confidence: 96%

“…16 We propose that the first harmonic signal of pðtÞ be modeled as the following equations: The signal p h1 ðtÞ is parameterized as aðtÞ and fðtÞ that correspond to amplitude modulation and frequency modulation, respectively. 21 As previously mentioned, the frequency modulation arises due to the influence of the autonomous nervous system.…”

Section: Frequency Modulated Ippg Signal Modelmentioning

confidence: 96%

HRVCam: robust camera-based measurement of heart rate variability

2021

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Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camerabased HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts. Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals. Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals. Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion. Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.

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A Method for Respiration Rate Detection in Wrist PPG Signal Using Holo-Hilbert Spectrum

Cited by 31 publications

References 45 publications

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features

HRVCam: robust camera-based measurement of heart rate variability

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