Using Time-Frequency Analysis of the Photoplethysmographic Waveform to Detect the Withdrawal of 900 mL of Blood

Validity of resting heart rate derived from contact-based smartphone photoplethysmography compared with electrocardiography: a scoping review and checklist for optimal acquisition and reporting

Background

With the rise of smartphone ownership and increasing evidence to support the suitability of smartphone usage in healthcare, the light source and smartphone camera could be utilized to perform photoplethysmography (PPG) for the assessment of vital signs, such as heart rate (HR). However, until rigorous validity assessment has been conducted, PPG will have limited use in clinical settings.

Objective

We aimed to conduct a scoping review assessing the validity of resting heart rate (RHR) acquisition from PPG utilizing contact-based smartphone devices. Our four specific objectives of this scoping review were to (1) conduct a systematic search of the published literature concerning contact-based smartphone device-derived PPG, (2) map study characteristics and methodologies, (3) identify if methodological and technological advancements have been made, and (4) provide recommendations for the advancement of the investigative area.

Methods

ScienceDirect, PubMed and SPORTDiscus were searched for relevant studies between January 1st, 2007, and November 6th, 2022. Filters were applied to ensure only literature written in English were included. Reference lists of included studies were manually searched for additional eligible studies.

Results

In total 10 articles were included. Articles varied in terms of methodology including study characteristics, index measurement characteristics, criterion measurement characteristics, and experimental procedure. Additionally, there were variations in reporting details including primary outcome measure and measure of validity. However, all studies reached the same conclusion, with agreement ranging between good to very strong and correlations ranging from r = .98 to 1.

Conclusions

Smartphone applications measuring RHR derived from contact-based smartphone PPG appear to agree with gold standard electrocardiography (ECG) in healthy subjects. However, agreement was established under highly controlled conditions. Future research could investigate their validity and consider effective approaches that transfer these methods from laboratory conditions into the "real-world", in both healthy and clinical populations.

Blood pressure assessment with in-ear photoplethysmography

Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.

Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.

Development of hemorrhage identification model using non-invasive vital signs

OBJECTIVE

Early detection and timely management of bleeding is critical as failure to recognize physiologically significant bleeding is associated with significant morbidity and mortality. Many such instances are detected late, even in highly monitored environments, contributing to delay in recognition and intervention. We propose a non-invasive early identification model to detect bleeding events using continuously collected photoplethysmography (PPG) and electrocardiography (ECG) waveforms.

APPROACH

Fifty-nine York pigs undergoing fixed-rate, controlled hemorrhage were involved in this study and a least absolute shrinkage and selection operator regression-based early detection model was developed and tested using PPG and ECG derived features. The output of the early detection model was a risk trajectory indicating the future probability of bleeding.

MAIN RESULTS

Our proposed models were generally accurate in predicting bleeding with an area under the curve of 0.89 (95% CI 0.87-0.92) and achieved an average time of 16.1 mins to detect 16.8% blood loss when a false alert rate of 1% was tolerated. Models developed on non-invasive data performed with similar discrimination and lead time to hemorrhage compared to models using invasive arterial blood pressure as monitoring data.

SIGNIFICANCE

A bleed detection model using only non-invasive monitoring performs as well as those using invasive arterial pressure monitoring.

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r²), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.

Can the descending aortic stroke volume be estimated by transesophageal descending aortic photoplethysmography?

PURPOSE

The aim of this study was to investigate the ability of transesophageal photoplethysmography detected from the descending aorta (dPPG) for predicting low descending aortic stroke volume (dSV) level in cardiac surgical patients.

METHODS

Fifteen patients scheduled for elective cardiac surgery were enrolled in our study. A transesophageal echocardiography (TEE) probe with an attached oximetry sensor was placed into the esophagus for paired dPPG signal and descending aortic Doppler blood flow signal acquisition. Metrics, including alternating current (AC), direct current (DC), area under the curve (AUC) and width (W), were extracted from the dPPG signals. The TEE-measured dSV, which was defined as the blood flow through the descending aorta during a cardiac cycle, was chosen as the standard reference. A receiver operating characteristic (ROC) curve was built to evaluate the performance of dPPG metrics in predicting low dSV level, and dSV measuring agreement between TEE and dPPG was analyzed by the Bland-Altman method.

RESULTS

A total of 644 paired dPPG and Doppler signals of the descending aorta were acquired. Significant correlations were found between the dPPG metrics and TEE-measured dSV, and the correlation coefficients between TEE-measured dSV and AUC or AC were 0.64 and 0.66, respectively. AUC and AC values obviously decreased with the reduction of dSV level among the three groups (<20 mL, from 20-40 mL, and >40 mL). The areas under the ROC curve for AUC and AC in predicting low dSV level (<20 mL) were 0.85 and 0.88, respectively. Bland-Altman plot showed a small bias (0.02 mL) but a wide limit of agreement (-18.62 to 18.66 mL) in dSV measurement between dPPG and Doppler technology.

CONCLUSIONS

The AC and AUC extracted from the dPPG signal provided a sensitive and qualitative prediction for dSV level. The dSV value could not be accurately measured by dPPG metrics.

TRIAL REGISTRATION

Chinese Clinical Trials Register Identifier: ChiCTR-OCS-12002789.

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach recently developed in our laboratory. The proposed TifMA algorithm consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data. Moreover, our algorithm was able to pinpoint the start and end times of the MNA with an error of less than 1 s in duration, whereas the next-best algorithm had a detection error of more than 2.2 s. The final, most challenging, dataset was collected to verify the performance of the algorithm in discriminating between corrupted data that were usable for accurate HR estimations and data that were nonusable. It was found that on average 48% of the data segments were found to have MNA, and of these, 38% could be used to provide reliable HR estimation.

A mucoadhesive endoluminal wearable sensory system

Bio- or muco-adhesive anchoring is a challenge for the development of advanced gastrointestinal (GI) surgical instruments, endoluminal monitoring devices and drug delivery systems. In this paper, we present a polymeric bio-adhesive film embedded with an optical sensor that can potentially be used to detect gastrointestinal bleeding. Four different formulas of mucoadhesive polymers were synthesized based on various chemical components and concentration combinations, and they were further layered with miniature photoplethymographic (PPG) sensors. The adhesive ability of the proposed mucoadhesive-sensor module was tested by attaching it to the lumen of a porcine stomach and compared amongst the four formulas. pH testing was also implemented to simulate the performance of the film in gastric cavity. To demonstrate the signal quality of this module, we also tested on the skin of five healthy subjects for hours. The observed shear detachment force between mucoadhesive film and porcine stomach tissue of all four formulations ranged from 0.09 to 1.38 N, and the performance of mucoadhesive film in pH 7 and pH 2 were similar. The module can attach firmly onto the skin for 3-10 hours with comparable PPG signal quality to traditional clip-based setup. With the advent of mucosal tissue anchoring by means of bioadhensive film, a wider extent of endoluminal procedures may become feasible. This emerging technology can also help shape the future of in-body wearable devices in the GI tract or other endoluminal cavities.

Impact of lower body negative pressure induced hypovolemia on peripheral venous pressure waveform parameters in healthy volunteers

Lower body negative pressure (LBNP) creates a reversible hypovolemia by sequestrating blood volume in the lower extremities. This study sought to examine the impact of central hypovolemia on peripheral venous pressure (PVP) waveforms in spontaneously breathing subjects. With IRB approval, 11 healthy subjects underwent progressive LBNP (baseline, -30, -75, and -90 mmHg or until the subject became symptomatic). Each was monitored for heart rate (HR), finger arterial blood pressure (BP), a chest respiratory band and PVP waveforms which are generated from a transduced upper extremity intravenous site. The first subject was excluded from PVP analysis because of technical errors in collecting the venous pressure waveform. PVP waveforms were analyzed to determine venous pulse pressure, mean venous pressure, pulse width, maximum and minimum slope (time domain analysis) together with cardiac and respiratory modulations (frequency domain analysis). No changes of significance were found in the arterial BP values at -30 mmHg LBNP, while there were significant reductions in the PVP waveforms time domain parameters (except for 50% width of the respiration induced modulations) together with modulation of the PVP waveform at the cardiac frequency but not at the respiratory frequency. As the LBNP progressed, arterial systolic BP, mean BP and pulse pressure, PVP parameters and PVP cardiac modulation decreased significantly, while diastolic BP and HR increased significantly. Changes in hemodynamic and PVP waveform parameters reached a maximum during the symptomatic phase. During the recovery phase, there was a significant reduction in HR together with a significant increase in HR variability, mean PVP and PVP cardiac modulation. Thus, in response to mild hypovolemia induced by LBNP, changes in cardiac modulation and other PVP waveform parameters identified hypovolemia before detectable hemodynamic changes.

Assessment of postoperative pain intensity by using photoplethysmography

PURPOSE

Timely assessment of acute postoperative pain is very important for pain management. No objective and reliable method to assess postoperative pain intensity exists till now. The aim of the study was to investigate the feasibility of photoplethysmography (PPG) signals in postoperative pain assessment.

METHODS

Thirty patients scheduled for elective abdominal surgery under general anesthesia were examined. Finger PPG signals and visual analogue scale (VAS) score were acquired before and 5, 10, 20, and 30 min after sufentanil administration when the patients were awake and transferred to the post-anesthesia care unit (PACU). During each pain rating, the patient's blood pressure, heart rate, and pulse oxygen saturation were recorded. The amplitude of alternating current (AC) and direct current (DC) extracted from finger PPG signals were analyzed, and the ratio of AC and DC (AC/DC) was calculated. Receiver operating characteristic (ROC) curves were built to assess the performance of AC and AC/DC to detect patients with VAS >4 in the PACU.

RESULTS

After administration of sufentanil, VAS scores decreased significantly (p < 0.05), as did blood pressure and heart rate. Simultaneously, both values of AC and AC/DC increased significantly. The VAS score had significant correlations with AC (r = -0.477; p < 0.01), AC/DC (r = -0.738; p < 0.01) and heart rate (r = 0.280; p < 0.01). In contrast, no statistical correlations between VAS score and blood pressure were found. Further analysis found significant differences in both AC and AC/DC among different pain levels, but no obvious differences in blood pressures and heart rate. The area under the ROC curves were 0.754 for AC and 0.795 for AC/DC, respectively.

CONCLUSION

The finger PPG signal can be used in acute postoperative pain assessment. Both AC/DC and AC had significant correlations with the pain rating levels, while blood pressure and heart rate were unreliable in pain assessment.

Critically ill patients in emergency department may be characterized by low amplitude and high variability of amplitude of pulse photoplethysmography

Background

The aim of the present pilot study was to determine if pulse photoplethysmography amplitude (PPGA) could be used as an indicator of critical illness and as a predictor of higher need of care in emergency department patients.

Methods

This was a prospective observational study. We collected vital signs and one minute of pulse photoplethysmograph signal from 251 consecutive patients admitted to a university hospital emergency department. The patients were divided in two groups regarding to the modified Early Warning Score (mEWS): > 3 (critically ill) and ≤ 3 (non-critically ill). Photoplethysmography characteristics were compared between the groups.

Results

Sufficient data for analysis was acquired from 212 patients (84.5%). Patients in critically ill group more frequently required intubation and invasive hemodynamic monitoring in the ED and received more intravenous fluids. Mean pulse photoplethysmography amplitude (PPGA) was significantly lower in critically ill patients (median 1.105 [95% CI of mean 0.9946-2.302] vs. 2.476 [95% CI of mean 2.239-2.714], P = 0.0257). Higher variability of PPGA significantly correlated with higher amount of fluids received in the ED (r = 0.1501, p = 0.0296).

Conclusions

This pilot study revealed differences in PPGA characteristics between critically ill and non-critically ill patients. Further studies are needed to determine if these easily available parameters could help increase accuracy in triage when used in addition to routine monitoring of vital signs.