Fingertip Photoplethysmography Research Articles

Validation of non-invasive derivation of the central aortic pressure waveform from fingertip photoplethysmography using a novel selective transfer function method

Introduction: Central aortic pressure waveform analyses can provide clinically relevant information beyond conventional brachial blood pressure (BP) assessment. This waveform can be reproduced non-invasively through application of a general transfer function (GTF) on a peripheral waveform, as conventionally performed by applanation tonometry. Photoplethysmography (PPG) is an alternate approach, however, differences in measurement site and modality demand the use of a transfer function (TF) specific for those differences. This study aims to compare central aortic waveform features generated from radial tonometry (reference) using a proprietary GTF with a central aortic waveform and its features generated from a simultaneous fingertip PPG measurement using a selective method where one of three different TFs is chosen based on the input signal harmonic profile. Methods: Brachial BP was measured in triplicate under resting conditions and was used for subsequent calibration. Multiple simultaneous radial tonometry (SphygmoCor CvMS) and fingertip PPG measurements were then performed in individual participants (n=21, 10 females, age: 39±16 years). Measurements were converted into central aortic waveforms with their respective TFs. Twenty central aortic pressure waveform parameters were compared through correlation analysis, Bland-Altman plots, and a repeated measure mixed-effects ANOVA model. Central aortic waveform shape was compared using the root-mean-squared error (RMSE). Results: Correlation (r) of PPG-derived parameters with radially tonometry derived central aortic parameters were high ranging from 0.79 to 0.99. Mean differences of pressure-related parameters were within 1.3 mmHg and differences of time-related parameters ranged from –2.2 to 3.4%. Whilst some parameters were statistically different these differences are not physiologically meaningful. Central aortic waveform shape had an average RMSE of 1.8±0.9%. Conclusion: Fingertip PPG derived central aortic waveform parameters using a novel selective TF were comparable to central aortic waveform features derived from radial tonometry using a previously validated GTF.

Finger pulse plethysmography predicts gestational hypertension, preeclampsia and gestational diabetes.

Pregnancy complications related to hypertension can affect both mother and newborn. Pulse wave attenuation (PWA) captured through fingertip photoplethysmography (PPG) provide valuable insights into maternal acute hemodynamic and autonomic vascular function. Here, we quantify the nocturnal dynamics of PWA during early pregnancy and assess their association with the development of gestational hypertension, preeclampsia and gestational diabetes. PWA dynamics were assessed on overnight polysomnography-derived PPG signals from a cohort of 2714 pregnant women (mean age: 26.8 ± 5.5 years) enrolled in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). We determined the average duration (PWAduration) and depth (PWAdepth) of PWA events in all women. Odds ratio (OR) analysis-adjusted common confounders indicates that an average PWAduration greater than 8.74 s was associated with the increased risk of gestational hypertension [OR = 1.75 (1.27-2.39), P < 0.001]. Similarly, average PWAdepth greater than 1.19 was associated with an increased risk of preeclampsia [OR = 1.53 (1.01-2.33), P = 0.045] and gestational diabetes [OR = 1.66 (1.01-2.73), P = 0.044]. PWA attenuation dynamics during early pregnancy predict the risk of developing gestational hypertension and diabetes condition for women in their later trimesters. Potentially obtainable from smart wearable consumer devices, PWA analysis offers a low-cost, accessible and scalable marker that can enhance the management of pregnancy-induced cardiometabolic issues.

An innovative approach for assessing coronary artery lesions: Fusion of wrist pulse and photoplethysmography using a multi-sensor pulse diagnostic device

Coronary heart disease (CHD) is a leading cause of mortality globally and poses a significant threat to public health. Coronary angiography (CAG) is a gold standard for the clinical diagnosis of CHD, but its invasiveness restricts its widespread application. In this study, we utilized a pulse diagnostic device equipped with pressure and photoelectric sensors to synchronously and non-invasively capture wrist pressure pulse waves and fingertip photoplethysmography (FPPG) of patients undergoing CAG. The extracted features were utilized in constructing random forest-based models to assessing the severity of coronary artery lesions. Notably, Model 3, incorporating both wrist pulse and FPPG features, surpassed Model 1 (solely utilizing wrist pulse features) and Model 2 (solely utilizing FPPG features). Model3 achieved an Accuracy, Precision, Recall, and F1-score of 78.79%, 78.69%, 78.79%, and 78.70%, respectively. Compared to Model1 and Model2, Model 3 exhibited improvements by 4.55%, 5.25%, 4.55%, and 5.12%, and 6.06%, 6.58%, 6.06%, and 6.54% respectively. This fusion of wrist pulse and FPPG features in Model 3 highlights the advantages of multi-source information fusion for model optimization. Additionally, this research provides invaluable insights into the novel development of diagnostic devices imbued with TCM principles and their potential in managing cardiovascular diseases.

Single-arm diagnostic electrocardiography with printed graphene on wearable textiles.

Stimulated by the COVID-19 outbreak, the global healthcare industry better acknowledges the necessity of innovating novel methods for remote healthcare monitoring and treating patients outside clinics. Here we report the development of two different types of graphene textile electrodes differentiated by the employed fabrication techniques (i.e., dip-coating and spray printing) and successful demonstration of ergonomic and truly wearable, single-arm diagnostic electrocardiography (SADE) using only 3 electrodes positioned on only 1 arm. The performance of the printed graphene e-textile wearable systems were benchmarked against the "gold standard" silver/silver chloride (Ag/AgCl) "wet" electrodes; achieving excellent correlation up to ∼ 96% and ∼ 98% in ECG recordings (15s duration) acquired with graphene textiles fabricated by dip-coating and spray printing techniques, respectively. In addition, we successfully implemented automatic detection of heartrate of 8 volunteers (mean value: 74.4 bpm) during 5min of static and dynamic daily activities and benchmarked their recordings with a standard fingertip photoplethysmography (PPG) device. Heart rate variability (HRV) was calculated, and the root means successive square difference (rMMSD) metric was 30ms during 5min of recording. Other cardiac parameters such as R-R interval, QRS complex duration, S-T segment duration, and T-wave duration were also detected and compared to typical chest ECG values.

Evaluating changes in pulse transit time drop index in patients with obstructive sleep apnea before and during CPAP therapy.

Airflow limitation in patients with obstructive sleep apnea (OSA) leads to arousal, increased sympathetic nervous system activity, and elevated blood pressure, which causes a decrease in pulse transit time (PTT). The present study aims to evaluate the effect of CPAP therapy on PTT in patients with moderate to severe OSA. This was a cross‐sectional study. Split‐night polysomnography (PSG) study was performed for each participant with apnea‐hypopnea index (AHI) ≥ 15 before and during CPAP therapy. The PTT was calculated as the time interval between the R wave of the electrocardiogram and the following arrival point in fingertip photoplethysmography. PTT drop was defined as a fall in the PTT curve of ≥15 ms lasting at least for 3 s and at most for 30 s. PTT drop index was defined as the number of drops in PTT that occur per hour of sleep. A total of 30 patients were included. PTT significantly increased, and PTT drop index significantly decreased during CPAP therapy (P < 0.001). PTT was significantly correlated to sleep efficiency (r s = −0.376, P = 0.049) and oxygen desaturation index (ODI) (r s = −0.428, P = 0.018). PTT drop index was strongly correlated to AHI (r s = 0.802, P < 0.001), respiratory disturbance index (RDI) (r s = 0.807, P < 0.001), ODI (r s = 0.693, P < 0.001), arousal index (r s = 0.807, P < 0.001), and periodic leg movement (PLM) index (r s = 0.400, P = 0.035). Overall, the findings from this study indicated that the PTT drop index is a non‐invasive and useful marker for evaluating the severity of OSA and the effectiveness of treatment in patients with moderate to severe OSA.

Assessment of the structural and functional state of blood vessels in patients with hypertrophic cardiomyopathy

Aim To evaluate the structural and functional condition of the vasculature using fingertip photoplethysmography and computerized videocapillaroscopy in patients with hypertrophic cardiomyopathy (HCMP).Material and methods The study included patients with HCMP (n=48; 28 (57 %) men; age, 54.3±13.6 years) and healthy volunteers (control group, n=33, 15 (45 %) men; age, 58.2±8.8 years). Standard laboratory and instrumental examination (blood count and biochemistry, electrocardiography, echocardiography, Holter electrocardiogram monitoring) were performed for all HCMP patients. The condition of vascular wall at various levels of the vasculature was evaluated by fingertip photoplethysmography (apparatus Angioscan-01) and computerized nail-fold videocapillaroscopy (apparatus Capillaroscan-01). The photoplethysmography study analyzed structural parameters, including the arterial wall stiffness index (aSI) of large blood vessels and the resistance index (RI) of small muscular arteries. Endothelial dysfunction was evaluated by the occlusion index (OI) and phase shift (PS). The capillaroscopy study assessed structural parameters, including the resting capillary density (rCD) and the capillary density following venous occlusion (voCD), and functional parameters, including the percentage of perfused capillaries (PPC), the percentage of restored capillaries (PRC), and the capillary density after the reactive hyperemia test (rhCD).Results The study showed increases in aSI (8.8 [6.8; 12.2] and RI (32.5 [17.4; 47.9] in the HCMP group. The OI was significantly lower in the HCMP group (1.3 [1.1; 1.5]) than in the control group (1.8 [1.5; 2.7], р&lt;0.001). Also, PS values were significantly decreased in the HCMP group (4.4 [2.3; 8.6]) compared to the control group (8.4 [5.1; 12.1]. p=0.018). Disorders of structural and functional capillary indexes were observed in HCMP patients compared to the control group; rCD and voCD were decreased in the HCMP group (60 [52.6; 68] and 88 [75; 90], respectively) compared to the control group (75.8 [60; 87] and 90 [73; 101]), however, no intergroup difference reached a statistical significance. The rhCD, PPC, and PRC values were decreased in the HCMP group (66.3 [55; 72], 86.7 [70.9; 104.2] and 1.7 [-6.95; 20.3], respectively) compared to the control group (86 [68.6; 100], 103 [96; 114] and 18.4 [8.1; 27.4], respectively); PPC and PRC values were significantly different (р&lt;0.005 and p&lt;0.004, respectively).Conclusion In patients with HCMP, fingertip photoplethysmography and computerized videocapillaroscopy showed increased wall stiffness in both large blood vessels and microvasculature, pronounced endothelial dysfunction, and decreases in capillary density and percentage of restored capillaries following respective tests.

Non-invasive cuffless blood pressure and heart rate monitoring using impedance cardiography

BackgroundContinuous blood pressure (BP) monitoring provides additional information about how changes in BP may correlate with daily activities and sleep patterns. Recommendations from the American Heart Association and American College of Cardiology strongly suggest confirming a diagnosis of hypertension with continuous BP monitoring. Non-invasive and non-intrusive detection of haemodynamic parameters is emerging as a norm, based on self-monitoring wearable medical devices. Researchers have carried out several studies using non-invasive and continuous BP measurements as an alternative to conventional cuff-based measurements. In this work, we proposed a novel method for cuffless estimation of BP using impedance cardiography (ICG). MethodsWe conducted a single-centre, cross-sectional study of 104 subjects (of whom 30 were categorized as controls and the remaining 74 as the disease group) at the Medical College and Hospital, Kolkata. The disease group consisted of patients with confirmed coronary artery disease, while the individuals in the control group were deemed to be healthy. All subjects underwent electrocardiogram recording by on-duty doctors in order to determine their health status. A custom-made device based on the principle of impedance plethysmography was designed to record impedance changes due to subjects’ peripheral blood flow. The device was used to record ICG signals. In this study, we developed a novel auto-adaptive algorithm based on ICG signals for non-invasive, cuffless, continuous monitoring of BP and heart rate. Separate mathematical models were developed for all the estimated parameters (BP and heart rate) for both the study groups (control and disease). The developed models were auto-adaptive and did not require subject-specific calibration. Performance indicators including, r2, error percentage, standard deviation, and mean difference were used to quantify the performance of the models. ResultsThe ICG signal recorded by the device was used to extract features and compute the augmentation index. The calculated augmentation index values showed strong correlations with systolic BP (r=0.99, P<0.05), diastolic BP (r=0.95, P<0.05), and heart rate (r=0.78, P<0.05). The models were also shown to have a high degree of accuracy for systolic and diastolic BP. Error margins were in the range ±2.33 and ±1.79 mmHg for systolic BP in disease and control subjects, respectively, and ±3.60 and ±1.82 mmHg for diastolic BP. However, the accuracy was lower in the prediction of heart rate in disease subjects, with a reported r2 value of 0.72 and an error margin of ±2.88 beats per min; for healthy subjects, the results were marginally better, with an error margin of ±1.82 beats per min. All statistical analyses were performed using MATLAB (R2017a, MathWorksⓇ, USA). ConclusionIn this study, we developed a non-invasive cuffless approach for estimation of systemic peripheral BP and heart rate using ICG. The proposed methodology eliminated any discomfort to patients caused by inflation of the cuff (in the case of cuff-based BP monitoring) or the need to constantly wear a fingertip photoplethysmography device (in the case of cuffless BP monitoring). The results obtained appeared promising and increased the potential scope of ICG for monitoring other haemodynamic parameters related to heart function.

Thoracic Outlet Syndrome: Fingertip Cannot Replace Forearm Photoplethysmography in the Evaluation of Positional Venous Outflow Impairments.

Objective: Fingertip photoplethysmography (PPG) resulting from high-pass filtered raw PPG signal is often used to record arterial pulse changes in patients with suspected thoracic outlet syndrome (TOS). Results from venous (low-pass filtered raw signal) forearm PPG (V-PPG) during the Candlestick-Prayer (Ca + Pra) maneuver were recently classified into four different patterns in patients with suspected TOS, two of which are suggestive of the presence of outflow impairment. We aimed to test the effect of probe position (fingertip vs. forearm) and of red (R) vs. infrared (IR) light wavelength on V-PPG classification and compared pattern classifications with the results of ultrasound (US).Methods: In patients with suspected TOS, we routinely performed US imaging (US + being the presence of a positional compression) and Ca + Pra tests with forearm V-PPGIR. We recruited patients for a Ca + Pra maneuver with the simultaneous fingertip and forearm V-PPGR. The correlation of each V-PPG recording to each of the published pattern profiles was calculated. Each record was classified according to the patterns for which the coefficient of correlation was the highest. Cohen’s kappa test was used to determine the reliability of classification among forearm V-PPGIR, fingertip V-PPGR, and forearm V-PPGR.Results: We obtained 40 measurements from 20 patients (40.2 ± 11.3 years old, 11 males). We found 13 limbs with US + results, while V-PPG suggested the presence of venous outflow impairment in 27 and 20 limbs with forearm V-PPGIR and forearm V-PPGR, respectively. Fingertip V-PPGR provided no patterns suggesting outflow impairment.Conclusion: We found more V-PPG patterns suggesting venous outflow impairment than US + results. Probe position is essential if aiming to perform upper-limb V-PPG during the Ca + Pra maneuver in patients with suspected TOS. V-PPG during the Ca + Pra maneuver is of low cost and easy and provides reliable, recordable, and objective evidence of forearm swelling. It should be performed on the forearm (close to the elbow) with either PPGR or PPGIR but not at the fingertip level.

Development of a Novel Web Camera-Based Contact-Free Major Depressive Disorder Screening System Using Autonomic Nervous Responses Induced by a Mental Task and Its Clinical Application.

BackgroundTo increase the consultation rate of potential major depressive disorder (MDD) patients, we developed a contact-type fingertip photoplethysmography-based MDD screening system. With the outbreak of SARS-CoV-2, we developed an alternative to contact-type fingertip photoplethysmography: a novel web camera-based contact-free MDD screening system (WCF-MSS) for non-contact measurement of autonomic transient responses induced by a mental task.MethodsThe WCF-MSS measures time-series interbeat intervals (IBI) by monitoring color tone changes in the facial region of interest induced by arterial pulsation using a web camera (1920 × 1080 pixels, 30 frames/s). Artifacts caused by body movements and head shakes are reduced. The WCF-MSS evaluates autonomic nervous activation from time-series IBI by calculating LF (0.04–0.15 Hz) components of heart rate variability (HRV) corresponding to sympathetic and parasympathetic nervous activity and HF (0.15–0.4 Hz) components equivalent to parasympathetic activities. The clinical test procedure comprises a pre-rest period (Pre-R; 140 s), mental task period (MT; 100 s), and post-rest period (Post-R; 120 s). The WCF-MSS uses logistic regression analysis to discriminate MDD patients from healthy volunteers via an optimal combination of four explanatory variables determined by a minimum redundancy maximum relevance algorithm: HF during MT (HFMT), the percentage change of LF from pre-rest to MT (%ΔLF(Pre–R⇒MT)), the percentage change of HF from pre-rest to MT (%ΔHF(Pre–R⇒MT)), and the percentage change of HF from MT to post-rest (%ΔHF(MT⇒Post–R)). To clinically test the WCF-MSS, 26 MDD patients (16 males and 10 females, 20–58 years) were recruited from BESLI Clinic in Tokyo, and 27 healthy volunteers (15 males and 12 females, 18–60 years) were recruited from Tokyo Metropolitan University and RICOH Company, Ltd. Electrocardiography was used to calculate HRV variables as references.ResultThe WCF-MSS achieved 73% sensitivity and 85% specificity on 5-fold cross-validation. IBI correlated significantly with IBI from reference electrocardiography (r = 0.97, p < 0.0001). Logit scores and subjective self-rating depression scale scores correlated significantly (r = 0.43, p < 0.05).ConclusionThe WCF-MSS seems a promising contact-free MDD screening apparatus. This method enables web camera built-in smartphones to be used as MDD screening systems.

Non-invasive cuff-less blood pressure machine learning algorithm using photoplethysmography and prior physiological data.

Conventional blood pressure (BP) measurement methods have a number of drawbacks such as being invasive, cuff-based or requiring manual operation. Many studies are focussed on emerging methods of noninvasive, cuff-less and continuous BP measurement, and using only photoplethysmography to estimate BP has become popular. Although it is well known that physiological characteristics of the subject are important in BP estimation, this has not been widely explored. This article presents a novel method which adopts photoplethysmography and prior knowledge of a subject's physiological features to estimate DBP and SBP. Features extracted from a fingertip photoplethysmography signal and prior knowledge of a subject's physiological characteristics, such as gender, age, height, weight and BMI is used to estimate BP using three different machine learning models: artificial neural networks, support vector machine and least absolute shrinkage and selection operator regression. The accuracy of BP estimation obtained when prior knowledge of the physiological characteristics are incorporated into the model is superior to those which do not take the physiological characteristics into consideration. In this study, the best performing algorithm is an artificial neural network which obtains a mean absolute error and SD of 4.74 ± 5.55 mm Hg for DBP and 9.18 ± 12.57 mm Hg for SBP compared to 6.61 ± 8.04 mm Hg for DBP and 11.12 ± 14.20 mm Hg for SBP without prior knowledge. The inclusion of prior knowledge of the physiological characteristics can improve the accuracy of BP estimation using machine learning methods, and the incorporation of more physiological characteristics enhances the accuracy of the BP estimation.

A Possible, Non-Invasive Method of Measuring Dynamic Lung Compliance in Patients with Interstitial Lung Disease Using Photoplethysmography.

Measuring lung compliance is useful for evaluating progression of interstitial lung disease (ILD), because reduced lung compliance due to fibrosis progression is the main cause of decreased vital capacity. However, because insertion of a balloon into the esophagus is invasive, lung compliance is rarely measured. A recently developed method uses fingertip photoplethysmography to estimate intrathoracic pressure. This method non-invasively measures lung dynamic compliance (Cdyn) by simultaneously measuring tidal volume. We evaluated the efficacy of this method in assessing ILD. This single-center, cross-sectional, observational study evaluated the efficacy of this method in patients with ILD and healthy controls. The primary outcome was estimated Cdyn (eCdyn), as determined with this method. We also evaluated baseline characteristics that are potential confounding factors for eCdyn. Median eCdyn was significantly lower in the ILD group (n = 14) than in the control group (n = 49) (0.122 vs. 0.183; P = 0.011). In univariate regression analysis, eCdyn was significantly correlated with height, weight, forced vital capacity, forced expiratory volume in 1 second, diffusing capacity for carbon monoxide, and usual interstitial pneumonia. In multivariate regression analysis, weight (β = 0.49, P = 0.011) and usual interstitial pneumonia (β = 0.52, P = 0.007) were significantly correlated with eCdyn. Using photoplethysmography, we noted a significant reduction in Cdyn in patients with ILD. This novel non-invasive method is a promising tool for evaluating fibrosis progression in ILD.

Screening of Ischemic Heart Disease based on PPG Signals using Machine Learning Techniques.

The increasing rate of cardiac ailments has led to the rise in the scrutinization of ones cardiac health. The prevalent techniques for detecting heart diseases are costly and require expert supervision as well as modern equipment. Thus there is a need for an alternative low cost and easily available technique. Finger-tip photoplethysmography (PPG) signals can be used for identifying Ischemic Heart Disease (IHD). This technique of screening the disease will be very helpful to the inhabitants of remote, underdeveloped and unprivileged areas. Time-domain analysis of the signal was done for extracting different features. Segregation of diseased and healthy subjects was performed using Decision Trees, Discriminant Analysis, Logistic Regression, Support Vector Machine, KNN, and Boosted trees. Ten different performance metrics was studied using the confusion matrix. After analysis, the accuracy, sensitivity, specificity, and precision of 0.94, 0.95, 0.95 and 0.97 respectively was obtained using Boosted tress classifier. ROC and AUC were calculated to establish the robustness of the classification methods for determining IHD patients.

Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics.

Noninvasive monitoring of cardiac hemodynamics remains challenging in cardiovascular medicine. The possibility of noninvasive optical monitoring of cardiac hemodynamics was theoretically investigated in this study. By utilizing the Monte Carlo simulation method for voxelized media (MCVM) and Visible Chinese Human dataset, we quantified and visualized the photon migration in human thoracic region. The light fluence distribution was showed to reach heart tissue (∼3 cm depth underbody surface) and 12% of the total fluence was absorbed by the myocardium. The proportion of spatial sensitivity distribution (SSD) in cardiac tissue to the total SSD reached 0.0195%. The portion of SSD increased following with cardiac diastole and diffuse reflectance deceased linearly with increasing cardiac volume. The optimal separation between the light source and detector was provided to be 3.5 to 4.0 cm for future development of noninvasive cardiac hemodynamics monitoring. A pilot experimental study was conducted to measure the diffuse reflectance light and fingertip photoplethysmography. These data suggest that the fluctuation period of near-infrared (NIR) diffuse reflectance was consistent with the cardiac cycle, while the fluctuation features of the NIR signal was not consistent with that of photoplethysmography. All results indicate the great potential of noninvasive optical monitoring of myocardial hemodynamics.

HRV Assessment Using Finger-tip Photoplethysmography (PulseRate) as Compared to ECG on Healthy Subjects During Different Postures and Fixed Breathing Pattern

Heart Rate Variability (HRV) is a well-known method to assess the human Autonomic Nervous System (ANS) functions. Currently, in the health care information system, HRV properties are very useful to provide medical and other vital signs of the patients. The HRV assessment was derived using the ECG R-R beat interval series, however, using the ECG probes with the electrode’s gel is not convenient for certain people such as who has an allergic symptom and or probes localization in front of the body part that needs certain knowledge how to put it on the correct location. Photoplethysmography (PPG) was considered to be an alternative way to assess HRV using the Peak-to-Peak interval series (PPI). Since previous studies already reported the availability to use PPG to assess HRV instead ECG, this study investigated how the HRV properties using those two methods during different postures with a fixed breathing pattern. Ten subjects participated in this study and we observed the R-R and the Peak-Peak beat interval time series from ECG and PPG respectively during sitting-supine, and standing-supine postures with 3 seconds breathing pattern. The results showed that using PPG to assess HRV still can be used with caution, especially during supine posture that had a deviation (SD) difference between ECG and PPG from the inter-subject analysis (p < 0.05). We concluded that the use of PPG still needs to be carefully considered to assess HRV properties, especially for wearable device and health care information system.

Resting and Post-exercise Heart Rate Detection from Fingertip and Facial Photoplethysmography Using a Smartphone Camera

Resting and Post-exercise Heart Rate Detection from Fingertip and Facial Photoplethysmography Using a Smartphone Camera

Pulse transit time and heart rate variability in sleep staging.

This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs. The validation of the algorithm was evaluated by Leave-One-Out-Cross-Validation method. The average accuracy of 73.4% with standard deviation of 6.4 was achieved while the algorithm could distinguish stages 2, 3 of non-rapid eye movement sleep by average sensitivity of almost 80%. The lowest mean sensitivity of 53% was for stage 1. These results demonstrate that an algorithm based on PTT and HRV spectral analysis is able to classify and distinguish sleep stages with high accuracy and sensitivity. In addition the proposed algorithm is capable to be improved and implemented as a wearable, comfortable and cheap instrument for sleep screening.

Photoplethysmography derivatives and pulse transit time in overnight blood pressure monitoring.

Overnight continuous blood pressure measurement provides simultaneous monitoring of blood pressure and sleep architecture. By this means, we are able to investigate whether different sleep events are associated to blood pressure fluctuations. In this paper, we used the Pulse Transit Time (PTT) to develop and evaluate functions for measurement of blood pressure. We focused on the first and second derivatives of fingertip Photoplethysmography (PPG) recordings to detect PPG critical points. By applying R wave of ECG and PPG critical points, we created two PTT-based models for estimation of systolic and diastolic blood pressure (SBP and DBP). Seven subjects polysomnography datasets that contained PPG, ECG and blood pressure recordings were utilised to validate and compare developed PTT-BP functions. Results found that if the peak of the first derivative of PPG (VPG) was considered as the pulse pressure arrival point, the resulted PTT (PTTV) would more accurately predict both SBP and DBP. The average R-squared coefficient for SBP and DBP were correspondingly 0.593 and 0.416. The obtained mean error for PTTV based functions in SBP was ±3.96 mmHg with standard deviation of 1.41 mmHg and in DBP was ±6.88 mmHg with standard deviation of 3.03 mmHg. We concluded PTT detected from VPG is a reliable and suitable maker for overnight continuous blood pressure monitoring.

Abstract 15697: Fingertip-Based Monitoring of Vascular Reactivity and Endothelial Function: A Three-Way Comparison of Testing Modalities

Introduction: In recent years fingertip-based measurement of vascular reactivity and endothelial function using a 5-min arm-cuff reactive hyperemia protocol has become a clinical tool for clinicians to evaluate patients in the exam room. Our group previously introduced Digital Thermal Monitoring (DTM) as a simple, easy and operator-independent test that measures vascular reactivity and endothelial function by monitoring fingertip temperature. Recently we developed fingertip photoplethysmography (PPG). In this study, we performed a simultaneous comparison of the three methods to evaluate their similarities and dissimilarities. Hypothesis: We hypothesized that PPG correlates strongly with PAT and could be used as a replace. Methods: Cuff-induced reactive hyperemia protocol (5 min. baseline, 5 min. ischemia, 5 min. post deflation) tests were performed with simultaneous recording of signals from PAT Pressure sensors (on index finger), DTM temperature sensors (3rd finger), and PPG photoelectric sensors (4th finger). A total of 39 tests were performed on 17 individual subjects. Signal data were analyzed, cross correlated, and graphed using Excel software and MATLAB functions. Results: As shown in Figure below, all three modalities showed similar patterns. Analyses of data during reactive hyperemia phase showed the strongest correlation between PPG and PAT (mean crosscorr = 0.79), compared to PAT and DTM (mean crosscorr = 0.64) and PPG and DTM (mean crosscorr = 0.49). Conclusions: DTM, PPG, and PAT signals are well correlated. The strong correlation between PPG and PAT suggests they can be used interchangeably.

Robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals.

The paper presents a fingertip photoplethysmography based technique to assess patient fluid status that is robust to waveform artifacts and health variability in the underlying patient population. The technique is intended for use in intensive care units, where patients are at risk for hypovolemia, and signal artifacts and inter-patient variations in health are common. Input signals are preprocessed to remove artifact, then a parameter-invariant statistic is calculated to remove effects of patient-specific physiology. Patient data from the Physionet MIMICII database was used to evaluate the performance of this technique. The proposed method was able to detect hypovolemia within 24 hours of onset in all hypovolemic patients tested, while producing minimal false alarms over non-hypovolemic patients.

Fuzzy Entropy based Detection of Tachycardia and Estimation of Pulse Rate through Fingertip Photoplethysmography

Fuzzy Entropy based Detection of Tachycardia and Estimation of Pulse Rate through Fingertip Photoplethysmography