Photoplethysmogram second derivative review: Analysis and applications

Qawqzeh, Yousef; Uldis, Rubins; Alharbi, Mafawez

doi:10.5897/sre2015.6322

Cited by 23 publications

(7 citation statements)

References 31 publications

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“…PPG signal is used in this study to extract some morphological indices that may be used for diabetes prediction. PPG waveform can be explained as a volumetric measure for blood volume changes inside arteries [7,8]. Still, there is no unified definition for the origin of PPG signal, but it can be understood as an optical plethysmograph [9].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling

Qawqzeh

Bajahzar

Jemmali

et al. 2020

BioMed Research International

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In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling

Qawqzeh

Bajahzar

Jemmali

et al. 2020

BioMed Research International

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“…As described in previous studies, the AI obtained by dividing the sum of the c- and d-waves by the a-wave is the blood transition capacity of the peripheral vascular system [ 21 ]. The c-wave is high and the d-wave is low when the blood flow is appropriate (which is observed in the young adults), and the c-wave is low when the blood circulation is poor (as observed in the elderly).…”

Section: Discussionmentioning

confidence: 99%

Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram

Seo

Choi

Lee

et al. 2021

Sensors

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Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives of the PPG pulse that can observe the inflection point of the pulse wave measured by a wearable PPG device. The PPG pulse at the earlobe was measured for 3 min in 84 elderly Korean women (age: 71.19 ± 6.97 years old). Based on the PPG-based cardiovascular function, we derived additional variables from TDPPG, in addition to the aging variable to predict the age. The Aging Index (AI) from SDPPG and Sum of TDPPG variables were calculated in the second and third differential forms of PPG. The variables that significantly correlated with age were c/a, Tac, AI of SDPPG, sum of TDPPG, and correlation coefficient ‘r’ of the model. In multiple linear regression analysis, the r value of the model was 0.308, and that using deep learning on the model was 0.839. Moreover, the possibility of improving the accuracy of the model using supervised deep learning techniques, rather than the addition of datasets, was confirmed.

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“…The b/a index is important parameter in the diagnosis of the state of the arterial wall [2]. [15,[22][23][24] demonstrated that the value of b/a index is associated with atherosclerosis and aging.…”

Section: The B/a Indexmentioning

confidence: 99%

“…The evaluation of cutaneous circulation using PPG has been utilized by [14]. The second derivative of PPG signal (SDPTG) has been studied by several researchers seeking valuable information about cardiovascular system activities [15]. The analysis of PPG's derivatives facilitates the process of peaks and valleys detection on the original contour of PPG waveform.…”

Section: Introductionmentioning

confidence: 99%

Digital Volume Pulse Analysis to Differentiate Diabetic From Non-Diabetic Subjects

Qawqzeh

2019

Comm. Math. & Appl.

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This paper aims to examine the validity of digital volume pulse waveform index namely the diastolic pulse peak (Dpp) in the evaluation of type II diabetics. In total, 153 participants (115 healthy participants and 48 diabetics type II patients) are recruited during the study. A customized algorithm for DVP waveform analysis is developed in MATLAB to analyze and calculate Dpp and b/a indices. The b/a index is found to be negatively correlated with both age and HbA1C test (r = −83.8 and r = −66.7, respectively), while Dpp index is found to be positively correlated with age and HbA1C test (r = 65.7 and r = 63.3, respectively). The DVP's Dpp index showed strong association with age and HbA1C test since it remains statistically significant based on the analysis of multi-linear regression. The model revealed that b/a, age, and Dpp contribute by 71.9% of the variance in HbA1C test. The findings showed that age, b/a, and Dpp indices are promising factors in diabetes type II assessment. These findings expand the potential utility of DVP signal in clinical settings.

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Photoplethysmogram second derivative review: Analysis and applications

Cited by 23 publications

References 31 publications

[Retracted] Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling

[Retracted] Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling

Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram

Digital Volume Pulse Analysis to Differentiate Diabetic From Non-Diabetic Subjects

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