Photoplethysmogram second derivative review: Analysis and applications

Automated myocardial infarction and angina detection using second derivative of photoplethysmography

Photoplethysmography (PPG) based healthcare devices have gained enormous interest in the detection of cardiac abnormalities. Limited research has been implemented for myocardial infarction (MI) detection. Moreover, PPG-based detection of angina is still a research gap. PPG signals are not always informative. Therefore, this research work presents the use of PPG signals and their second derivative to evaluate myocardial infarction and angina using a novel set of morphological features. The obtained morphological features are fed onto the feed-forward artificial neural network for the identification of the type of MI and unstable angina (UA). The initial experiments have been carried out on non-ambulatory (public) subjects for feature extraction and later evaluated on ambulatory (self-generated) databases. The intended method attains accuracy, sensitivity, and specificity of 98%, 97%, 98% on the public database and 94%, 94%, 94% on the self-generated database. The result shows that the proposed set of features can detect MI and UA with significant accuracy.

Harmonic components of photoplethysmography and pathological patterns: A cross-sectional study

This study aimed to examine whether the 3 harmonic components (HCs) of photoplethysmography (PTG) - total harmonic distortion (THD), harmonic power (HP), and normalized harmonic amplitude (HA) - have aging effects and may serve as an arterial stiffness marker and examine the relationship between HCs and clinical severity of pathological patterns. This study had a retrospective chart review design, and electronic medical records of 173 female patients (age: 38.57 ± 11.64 years) were reviewed. Patients were asked to complete the phlegm, blood stasis (BS), and food retention (FR) pattern questionnaires and underwent PTG and the second derivative of PTG measurements. THD, HP, and HA data were extracted till the 12th HCs from the raw PTG data. THD and HA had an aging effect (β: -0.179 to -0.278) and were related to b/a (r: -02.76 to -0.455) and d/a (r: 0.265-0.360) of the second derivative of PTG. In the younger group (≤33 years), HP and HA were positively correlated with phlegm, BS, and FR patterns (r: 0.257-0.370), while HP was positively correlated with the FR pattern (r: 0.278-0.315) in the middle age group (34-45 years). In the older group (≥46 years), HP and HA were positively or negatively correlated with the phlegm pattern (r: ±0.263 to ±0.440). HCs may serve as an arterial stiffness marker, and may be partially related to phlegm, BS, and FR patterns. Aging effect needs to be considered when utilizing HCs as an indicator of phlegm, BS, and FR patterns.

Exploiting moving slope features of PPG derivatives for estimation of mean arterial pressure

Monitoring Mean Arterial Pressure (MAP) helps calculate the arteries' flow, resistance, and pressure. It allows doctors to check how well the blood flows through our body and reaches all major organs. Photoplethysmogram technology is gaining momentum and popularity in smart wearable devices to monitor cuff-less blood pressure (BP). However, the performance reliability of the existing PPG-based BP estimation devices is still poor. Inaccuracy in estimating systolic and diastolic blood pressure leads to an overall imprecision in resultant MAP values. Hence, there is a need for robust and reliable MAP estimation algorithms. This work exploits the moving slope features of PPG contour in its first and second derivatives that directly correlate with MAP and does not require estimating systolic and diastolic blood pressure values. The proposed approach is evaluated using two different data sets (i.e., MIMIC-I and MIMIC-II) to demonstrate the robustness and reliability of the work for personalized non-invasive BP monitoring devices to estimate MAP directly. A mean absolute error of 1.28 mmHg and a standard deviation of 2.50 mmHg is obtained with MIMIC-II data-set using GridSearchCV random forest regressor that outperformed most of the existing related works.

Variations in the Second Derivative of a Photoplethysmogram with Age in Healthy Korean Adults

Second derivative of photoplethysmogram (SDPTG) indices correlate with aging and vascular health. The trend of SDPTG indices with age has not yet been studied in the Korean population. Various SDPTG indices were measured in 300 healthy Koreans (150 men and 150 women), aged 19−69 years, stratified into five age groups consisting of 60 people (30 men and 30 women) in each age group from their 20s to 60s. The values of the SDPTG indices clearly showed distinct variations with age in healthy Korean groups (p < 0.001 for all indices). b/a increased linearly with age (y = 0.0045x − 0.803), as did SDPTG aging index (SDPTG-AI) (y = 0.0162x − 1.1389). c/a decreased linearly with age (y = −0.0044x + 0.1017), as did d/a (y = −0.0062x + 0.034) and e/a (y = −0.001x + 0.2002). A significant sex difference was shown in b/a and e/a ratios and SDPTG-AI (p < 0.001 for all indices); women had a higher b/a ratio (−0.55 ± 0.14 versus −0.65 ± 0.13) and SDPTG-AI (−0.33 ± 0.3 versus −0.52 ± 0.33) and a lower e/a ratio (0.13 ± 0.06 versus 0.18 ± 0.07) than men. A linear regression model of diverse SDPTG indices was provided according to the age of healthy Koreans, which may be valuable in preventing diseases related to vascular conditions by estimating the degradation of arterial function.

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

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

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.

Filtering-induced time shifts in photoplethysmography pulse features measured at different body sites: the importance of filter definition and standardization

Objective.The waveform of a photoplethysmography (PPG) signal depends on the measurement site and individual physiological conditions. Filtering can distort the morphology of the original PPG signal waveform and change the timing of pulse feature points on PPG signals. We aim to quantitatively investigate the effect of PPG signal morphology (related to measurement site) and type of pulse feature on the filtering-induced time shift (TS).Approach.60 s PPG signals were measured from six body sites (finger, wrist under (volar), wrist upper (dorsal), earlobe, and forehead) of 36 healthy adults. Using infinite impulse response digital filters which are common in PPG signal processing, PPG signals were prefiltered (band-pass, pass and stop bands: >0.5 Hz and <0.2 Hz for high-pass filter, <20 Hz and >30 Hz for low-pass filter) and then filtered (low-pass, pass and stop bands: <3 Hz and >5 Hz). Four pulse feature points were defined and extracted (peak, valley, maximal first derivative, and maximal second derivative). For each subject, overall TS and intra-subject TS variability in feature points were calculated as the mean and standard deviation of TS between prefiltered and filtered PPG signals in 50 cardiac cycles. Statistical testing was performed to investigate the effect of measurement site and type of pulse feature on overall TS and intra-subject TS variability.Main results.Measurement site, type of pulse feature, and their interaction had significant impacts on the overall TS and intra-subject TS variability (p < 0.001 for all). Valley and maximal second derivative showed higher overall TS than peak and maximal first derivative. Finger had higher overall TS and lower intra-subject TS variability than other measurement sites.Significance. Measurement site and type of pulse feature can significantly influence the timing of feature points on filtered PPG signals. Filtering parameters should be quoted to support the reproducibility of PPG-related studies.

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

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.

Pulse arrival time (PAT) measurement based on arm ECG and finger PPG signals - comparison of PPG feature detection methods for PAT calculation

In this study, pulse arrival time (PAT) was measured using a simple measurement setup consisting of arm electrocardiogram (ECG) and finger photoplethysmogram (PPG). Four methods to calculate PAT from the measured signals were evaluated. PAT was calculated as the time delay between ECG R peak and one of the following points in the PPG waveform: peak (maximum value of PPG waveform), foot (minimum value of PPG waveform), dpeak (maximum value of the first derivative of PPG waveform) and ddpeak (maximum value of the second derivative of PPG waveform). In addition to PAT calculation, pulse period (PP) intervals based on the detected features were determined and compared to RR intervals derived from ECG signal. Based on the results obtained here, the most promising method to be used in PAT or PP calculation seems to be the dpeak detection method.