Algoritmo para el análisis en conjunto de las señales del ECG y PPG

The main objective of this research is based on finding out some assertive and robust Photoplethysmogram’s PPG & Electrocardiogram’s ECG blood pressure-related parameters by the implementation of a novel method with innovations in signal processing and analysis. The biomedical ECG and PPG signals are recorded using a mobile monitor CardioQVark. To increase the cuffless blood pressure measurement accuracy, a technique that involves not only the ECG and PPG joint parameters extraction but also some individual PPG’s morphology features, is proposed in this work. Firstly, the biomedical ECG and PPG signals are time–frequency filtered.  Secondly, some novel parameters from the morphology of photoplethysmogram signal, which may be correlated with blood pressure, are considered in addition to the pulse transit time. Additionally, a neural network is built to determine the relationship between the estimated and reference blood pressure. Finally, the correlation coefficient and regression line are obtained to evaluate the feasibility.El objetivo de esta investigación consiste en identificar aquellos parámetros provenientes de las señales del electrocardiograma ECG y fotopletismograma PPG que permitan hacer una evaluación de la presión sanguínea utilizando un dispositivo móvil. El método propuesto incluye innovaciones en el procesamiento y análisis de las señales. Con el objetivo de aumentar la precisión de la medición de la presión sanguínea, en este trabajo, se propone la utilización de parámetros provenientes de la señal del PPG en conjunto con el PTT obtenido de las señales del ECG y PPG analizadas en conjunto. Adicionalmente, se propone el diseño e implementación de una red neural para determinar la relación existente entre la presión sanguínea estimada por el método y la de referencia, lo cual permite evaluar la viabilidad del método propuesto

A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation

Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial Blood Pressure (ABP) signals as dependent variables, and then using machine learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR), and Heart Rate (HR) features from Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster. The method was implemented using the MIMICII dataset with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multi-trend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster, and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36)

A NOVEL WAVEFORM MIRRORING TECHNIQUE FOR SYSTOLIC BLOOD PRESSURE ESTIMATION FROM ANACROTIC PHOTOPLETHYSMOGRAM

Continuous cuffless Blood Pressure (BP) measurement is an important tool to monitor the health of individuals at risk. In this study, a new method is proposed for Systolic BP (SBP) estimation utilizing Photoplethysmograms (PPG). To this end, toe and carotid PPG were recorded from seventeen subjects aged 20-28 years, whereas their SBP were measured using a standard BP cuff monitor for validation purpose. The proposed method is based on a novel mirroring technique, which allows for an accurate estimation of the Pulse Transit Time (PTT) from the PPG’s rising part (anacrotic) waveform using an ARX System Identification approach. Based on the modified Moens-Korteweg equation, SBP was then calculated based on the estimated PTT values obtained from the ARX model. The estimated PTT was found to be highly correlated to the measured SBP (R2 = 0.98). Comparison of calculated SBP to the measured SBP obtained using standard BP cuff monitor results in a mean error of 3.4%. Given that 95% of the estimated SBP values are accurate in the +/- 8 mmHg range, this method seems promising for non-invasive, continuous BP monitorin

Quantitative assessment of the impact of blood pulsation on intraocular pressure measurement results in healthy subjects

Background. Blood pulsation affects the results obtained using various medical devices in many different ways. Method. The paper proves the effect of blood pulsation on intraocular pressure measurements. Six measurements for each of the 10 healthy subjects were performed in various phases of blood pulsation. A total of 8400 corneal deformation images were recorded. The results of intraocular pressure measurements were related to the results of heartbeat phases measured with a pulse oximeter placed on the index finger of the subject's left hand. Results. The correlation between the heartbeat phase measured with a pulse oximeter and intraocular pressure is 0.69±0.26 (p<0.05). The phase shift calculated for the maximum correlation is equal to 60±40° (p<0.05). When the moment of measuring intraocular pressure with an air-puff tonometer is not synchronized, the changes in IOP for the analysed group of subjects can vary in the range of ±2.31 mmHg (p<0.3). Conclusions. Blood pulsation has a statistically significant effect on the results of intraocular pressure measurement. For this reason, in modern ophthalmic devices, the measurement should be synchronized with the heartbeat phases

An optimization study of estimating blood pressure models based on pulse arrival time for continuous monitoring

Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker correlation between the estimated BP and the cuff BP than MK-EE, MK-BH, and dMK-BH models. Further, in contrast to L-MK, the dMK-BH model had the strongest correlation and the smallest difference between the estimated BP and the cuff BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) than others. In this study, the simple MK-EE model showed the best similarity to the dMK-BH model. There were no significant changes between MK-EE and dMK-BH models. These findings indicated that the nonlinear MK-EE model with low estimated error and simple mathematical expression was a good choice for application in wearable sensor devices for cuff-less BP monitoring compared to others

Baroreflex sensitivity measured by pulse photoplethysmography

Novel methods for assessing baroreflex sensitivity (BRS) using only pulse photoplethysmography (PPG) signals are presented. Proposed methods were evaluated with a data set containing electrocardiogram (ECG), blood pressure (BP), and PPG signals from 17 healthy subjects during a tilt table test. The methods are based on a surrogate of a index, which is defined as the power ratio of RR interval variability (RRV) and that of systolic arterial pressure series variability (SAPV). The proposed a index surrogates use pulse-to-pulse interval series variability (PPV) as a surrogate of RRV, and different morphological features of the PPG pulse which have been hypothesized to be related to BP, as series surrogates of SAPV. A time-frequency technique was used to assess BRS, taking into account the non-stationarity of the protocol. This technique identifies two time-varying frequency bands where RRV and SAPV (or their surrogates) are expected to be coupled: the low frequency (LF, inside 0.04–0.15 Hz range), and the high frequency (HF, inside 0.15–0.4 Hz range) bands. Furthermore, time-frequency coherence is used to identify the time intervals when the RRV and SAPV (or their surrogates) are coupled. Conventional a index based on RRV and SAPV was used as Gold Standard. Spearman correlation coefficients between conventional a index and its PPG-based surrogates were computed and the paired Wilcoxon statistical test was applied in order to assess whether the indices can find significant differences (p < 0.05) between different stages of the protocol. The highest correlations with the conventional a index were obtained by the a-index-surrogate based on PPV and pulse up-slope (PUS), with 0.74 for LF band, and 0.81 for HF band. Furthermore, this index found significant differences between rest stages and tilt stage in both LF and HF bands according to the paired Wilcoxon test, as the conventional a index also did. These results suggest that BRS changes induced by the tilt test can be assessed with high correlation by only a PPG signal using PPV as RRV surrogate, and PPG morphological features as SAPV surrogates, being PUS the most convenient SAPV surrogate among the studied ones

A study on the effect of contact pressure during physical activity on photoplethysmographic heart rate measurements

Heart rate (HR) as an important physiological indicator could properly describe global subject’s physical status. Photoplethysmographic (PPG) sensors are catching on in field of wearable sensors, combining the advantages in costs, weight and size. Nevertheless, accuracy in HR readings is unreliable specifically during physical activity. Among several identified sources that affect PPG recording, contact pressure (CP) between the PPG sensor and skin greatly influences the signals. Methods: In this study, the accuracy of HR measurements of a PPG sensor at different CP was investigated when compared with a commercial ECG-based chest strap used as a test control, with the aim of determining the optimal CP to produce a reliable signal during physical activity. Seventeen subjects were enrolled for the study to perform a physical activity at three different rates repeated at three different contact pressures of the PPG-based wristband. Results: The results show that the CP of 54 mmHg provides the most accurate outcome with a Pearson correlation coefficient ranging from 0.81 to 0.95 and a mean average percentage error ranging from 3.8% to 2.4%, based on the physical activity rate. Conclusion: Authors found that changes in the CP have greater effects on PPG-HR signal quality than those deriving from the intensity of the physical activity and specifically, the individual best CP for each subject provided reliable HR measurements even for a high intensity of physical exercise with a Bland–Altman plot within ±11 bpm. Although future studies on a larger cohort of subjects are still needed, this study could contribute a profitable indication to enhance accuracy of PPG-based wearable devices

Baroreflex Sensitivity Measured by Pulse Photoplethysmography

Novel methods for assessing baroreflex sensitivity (BRS) using only pulse photoplethysmography (PPG) signals are presented. Proposed methods were evaluated with a data set containing electrocardiogram (ECG), blood pressure (BP), and PPG signals from 17 healthy subjects during a tilt table test. The methods are based on a surrogate of α index, which is defined as the power ratio of RR interval variability (RRV) and that of systolic arterial pressure series variability (SAPV). The proposed α index surrogates use pulse-to-pulse interval series variability (PPV) as a surrogate of RRV, and different morphological features of the PPG pulse which have been hypothesized to be related to BP, as series surrogates of SAPV. A time-frequency technique was used to assess BRS, taking into account the non-stationarity of the protocol. This technique identifies two time-varying frequency bands where RRV and SAPV (or their surrogates) are expected to be coupled: the low frequency (LF, inside 0.04–0.15 Hz range), and the high frequency (HF, inside 0.15–0.4 Hz range) bands. Furthermore, time-frequency coherence is used to identify the time intervals when the RRV and SAPV (or their surrogates) are coupled. Conventional α index based on RRV and SAPV was used as Gold Standard. Spearman correlation coefficients between conventional α index and its PPG-based surrogates were computed and the paired Wilcoxon statistical test was applied in order to assess whether the indices can find significant differences (p &lt; 0.05) between different stages of the protocol. The highest correlations with the conventional α index were obtained by the α-index-surrogate based on PPV and pulse up-slope (PUS), with 0.74 for LF band, and 0.81 for HF band. Furthermore, this index found significant differences between rest stages and tilt stage in both LF and HF bands according to the paired Wilcoxon test, as the conventional α index also did. These results suggest that BRS changes induced by the tilt test can be assessed with high correlation by only a PPG signal using PPV as RRV surrogate, and PPG morphological features as SAPV surrogates, being PUS the most convenient SAPV surrogate among the studied ones