Peripheral photoplethysmography variability analysis of sepsis patients

Wearable sensors for prediction of intraamniotic infection in women with preterm premature rupture of membranes: a prospective proof of principle study

PURPOSE

To evaluate the use of wearable sensors for prediction of intraamniotic infection in pregnant women with PPROM.

MATERIALS AND METHODS

In a prospective proof of principle study, we included 50 patients diagnosed with PPROM at the University Hospital Zurich between November 2017 and May 2020. Patients were instructed to wear a bracelet during the night, which measures physiological parameters including wrist skin temperature, heart rate, heart rate variability, and breathing rate. A two-way repeated measures ANOVA was performed to evaluate the difference over time of both the wearable device measured parameters and standard clinical monitoring values, such as body temperature, pulse, leucocytes, and C-reactive protein, between women with and without intraamniotic infection.

RESULTS

Altogether, 23 patients (46%) were diagnosed with intraamniotic infection. Regarding the physiological parameters measured with the bracelet, we observed a significant difference in breathing rate (19 vs 16 per min, P < .01) and heart rate (72 vs 67 beats per min, P = .03) in women with intraamniotic infection compared to those without during the 3 days prior to birth. In parallel to these changes standard clinical monitoring values were significantly different in the intraamniotic infection group compared to women without infection in the 3 days preceding birth.

CONCLUSION

Our results suggest that wearable sensors are a promising, noninvasive, patient friendly approach to support the early detection of intraamniotic infection in women with PPROM. However, confirmation of our findings in larger studies is required before implementing this technique in standard clinical management.

Classifying sepsis from photoplethysmography

Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring.

Spectral analysis of signals of autonomic regulation of blood circulation in patients with COVID-19 and arterial hypertension

The work aims to carry out a comparative analysis of the spectral indices of the photoplethysmogram signals and RR-intervals of healthy volunteers, patients with COVID-19, as well as patients with COVID-19 and chronic arterial hypertension of 1-2 degrees. The study included 45 records of pairs of electrocardiogram and photoplethysmogram signals. Each sample included 15 pairs of 20-minute experimental signals. The study revealed an increase in the integrated power of the low-frequency (LF) band over the integrated power of the high-frequency (HF) band in the photoplethysmogram signals in patients with COVID-19, burdened with hypertension. In the signals of the RR-intervals, the largest values of the ratio of the LF to HF indices are associated with patients without cardiovascular diseases. In turn, this may be an indicator of the different effects of viral infection and arterial hypertension on the loops of autonomic control of heart rate and vascular tone.

Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure

The question of how much information the photoplethysmogram (PPG) signal contains on the autonomic regulation of blood pressure (BP) remains unsolved. This study aims to compare the low-frequency (LF) and high-frequency components of PPG and BP and assess their correlation with oscillations in interbeat (RR) intervals at similar frequencies. The PPG signal from the distal phalanx of the right index finger recorded using a reflective PPG sensor at green light, the BP signal from the left hand recorded using a Finometer, and RR intervals were analyzed. These signals were simultaneously recorded within 15 min in a supine resting condition in 17 healthy subjects (12 males and 5 females) aged 33 ± 9 years (mean ± SD). The study revealed the high coherence of LF components of PPG and BP with the LF component of RR intervals. The high-frequency components of these signals had low coherence. The analysis of the signal instantaneous phases revealed the presence of high-phase coherence between the LF components of PPG and BP. It is shown that the LF component of PPG is determined not only by local myogenic activity but also reflects the processes of autonomic control of BP.

Low-frequency variability in photoplethysmographic waveform and heart rate during on-pump cardiac surgery with or without cardioplegia

We studied the properties of low-frequency (LF) heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV) and their interaction under conditions where the hemodynamic connection between them is obviously absent, as well as the LF regulation of PPGV in the absence of heart function. The parameters of HRV and finger PPGV were evaluated in 10 patients during cardiac surgery under cardiopulmonary bypass (on-pump cardiac surgery) with or without cardioplegia. The following spectral indices of PPGV and HRV were ertimated: the total spectral power (TP), the high-frequency (HF) and the LF ranges of TP in percents (HF% and LF%), and the LF/HF ratio. We assessed also the index S of synchronization between the LF oscillations in finger photoplethysmogram (PPG) and heart rate (HR) signals. The analysis of directional couplings was carried out using the methods of phase dynamics modeling. It is shown that the mechanisms leading to the occurrence of oscillations in the LF range of PPGV are independent of the mechanisms causing oscillations in the LF range of HRV. At the same time, the both above-mentioned LF oscillations retain their activity under conditions of artificial blood circulation and cardioplegia (the latter case applies only to LF oscillations in PPG). In artificial blood circulation, there was a coupling from the LF oscillations in PPG to those in HR, whereas the coupling in the opposite direction was absent. The coupling from the LF oscillations in PPG to those in HR has probably a neurogenic nature, whereas the opposite coupling has a hemodynamic nature (due to cardiac output).

Autonomic control of cardiorespiratory coupling in healthy subjects under moderate physical exercises

Aim of the study is to apply the analysis of phase dynamics to investigation of the coupling patterns between heart rate variability, respiration and peripheral circulation in healthy subjects at rest and after moderate physical exercises. Material and Methods ― 30-minutes electrocardiogram (ECG), photoplethysmogram (PPG) and respiration records were obtained from healthy subjects aged 22±2 (mean ± standard deviation) before and after active Martine Kushelevsky test (20 squats in 30 seconds). The coherence function was estimated between all the signals from each subject, and the phase dynamics modeling was used to detect the directional coupling in high-frequency (HF; 0.14-0.40 Hz) and low-frequency ranges (LF; 0.04-0.14 Hz). Results ― At rest (before the physical activity) no statistically significant couplings were detected between the HF rhythms of respiration and heart rate (RR intervals). For the HF rhythms in respiration and PPG, the influence respirationPPG was detected. No couplings were detected between the LF rhythms. After the moderate physical exercise, predominant direction of the influence was RR intervals  PPG in the LF range and both RR intervals  respiration and PPG  respiration in the HF range. The influence PPG  respiration was delayed by several seconds. Conclusion ― adaptation to the moderate physical activity has led to the decrease in the overall coherence and changes in patterns of directional coupling between the LF and HF rhythms in respiration, heart rate and peripheral circulation. The obtained results confirm nonlinearity and complexity of the coupling patterns in cardiovascular system.

Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study

OBJECTIVE

Hypoxaemia is a strong predictor of mortality in children. Early detection of deteriorating condition is vital to timely intervention. We hypothesise that measures of pulse oximetry dynamics may identify children requiring hospitalisation. Our aim was to develop a predictive tool using only objective data derived from pulse oximetry and observed respiratory rate to identify children at increased risk of hospital admission.

SETTING

Tertiary-level hospital emergency department in Bangladesh.

PARTICIPANTS

Children under 5 years (n=3374) presenting at the facility (October 2012-April 2013) without documented chronic diseases were recruited. 1-minute segments of pulse oximetry (photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate (HR)) and respiratory rate were collected with a mobile app.

PRIMARY OUTCOME

The need for hospitalisation based on expert physician review and follow-up.

METHODS

Pulse rate variability (PRV) using pulse peak intervals of the PPG signal and features extracted from the SpO2 signal, all derived from pulse oximetry recordings, were studied. A univariate age-adjusted logistic regression was applied to evaluate differences between admitted and non-admitted children. A multivariate logistic regression model was developed using a stepwise selection of predictors and was internally validated using bootstrapping.

RESULTS

Children admitted to hospital showed significantly (p<0.01) decreased PRV and higher SpO2 variability compared to non-admitted children. The strongest predictors of hospitalisation were reduced PRV-power in the low frequency band (OR associated with a 0.01 unit increase, 0.93; 95% CI 0.89 to 0.98), greater time spent below an SpO2 of 98% and 94% (OR associated with 10 s increase, 1.4; 95% CI 1.3 to 1.4 and 1.5; 95% CI 1.4 to 1.6, respectively), high respiratory rate, high HR, low SpO2, young age and male sex. These variables provided a bootstrap-corrected AUC of the receiver operating characteristic of 0.76.

CONCLUSIONS

Objective measurements, easily obtained using a mobile device in low-resource settings, can predict the need for hospitalisation. External validation will be required before clinical adoption.

Using demographic and time series physiological features to classify sepsis in the intensive care unit

Sepsis, a systemic inflammatory response to infection, is a major health care problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by (i) using static scores derived from bed-side measurements individually, and (ii) deriving these scores at a much slower rate than the rate for which patient data is collected. In this study, we construct a generalized linear model (GLM) for the probability that an ICU patient has sepsis as a function of demographics and bedside measurements. Specifically, models were trained on 29 patient recordings from the MIMIC II database and evaluated on a different test set including 8 patient recordings. A classification accuracy of 62.5% was achieved using demographic measures as features. Adding physiological time series features to the model increased the classification accuracy to 75%. Although very preliminary, these results suggest that using generalized linear models incorporating real time physiological signals may be useful for an early detection of sepsis, thereby improving the chances of a successful treatment.

A comprehensive assessment of cardiovascular autonomic control using photoplethysmograms recorded from the earlobe and fingers

We compare the spectral indices of photoplethysmogram variability (PPGV) estimated using photoplethysmograms recorded from the earlobe and the middle fingers of the right and left hand and analyze their correlation with similar indices of heart rate variability (HRV) in 30 healthy subjects (26 men) aged 27 (25, 29) years (median with inter-quartile ranges) at rest and under the head-up tilt test. The following spectral indices of PPGV and HRV were compared: mean heart rate (HR), total spectral power (TP), high-frequency (HF) and low-frequency (LF) ranges of TP in percents (HF% and LF%), LF/HF ratio, and spectral coherence. We assess also the index S of synchronization between the LF oscillations in HRV and PPGV. The constancy of blood pressure (BP) and moderate increase of HR under the tilt test indicate the presence of fast processes of cardiovascular adaptation with the increase of the sympathetic activity in studied healthy subjects. The impact of respiration on the PPGV spectrum (accessed by HF%) is less than on the HRV spectrum. It is shown that the proportion of sympathetic vascular activity (accessed by LF%) is constant in the PPGV of three analyzed PPGs during the tilt test. The PPGV for the ear PPG was less vulnerable to breathing influence accessed by HF% (independently from body position) than for PPGs from fingers. We reveal the increase of index S under the tilt test indicating the activation of interaction between the heart and distal vessels. The PPGV spectra for finger PPGs from different hands are highly coherent, but differ substantially from the PPGV spectrum for the ear PPG. We conclude that joint analysis of frequency components of PPGV (for the earlobe and finger PPGs of both hands) and HRV and assessment of their synchronization provide additional information about cardiovascular autonomic control.

Discrimination ability and reproducibility of a new index reflecting autonomic nervous function based on pulsatile amplitude of photoplethysmography

A new physiological index (μ(PA)) is proposed to represent the autonomic nervous system (ANS) function. The index μ(PA) is defined as the natural logarithm of the ratio between two different frequency components of the pulsatile amplitude of the photoplethysmogram (PPG) signal. The discrimination ability and the reproducibility of μ(PA) have been compared with other traditional ANS indices. In the experiment, the electrocardiogram, the PPG and continuous blood pressure were measured in 59 healthy young subjects (age 25.7 ± 6.3) and 86 healthy elderly subjects (age 70.2 ± 4.1) at rest. The discrimination ability and the reproducibility were evaluated by Cohen's d between young and elderly groups and by the interclass correlation coefficient, respectively. The results showed that the elderly subjects were significantly (p<;0.001) lower than young subjects in μ(PA) and a few traditional indices introduced to be compared with μ(PA). Therefore, it suggests that μ(PA) is associated with the decrease in the ANS function accompanied by aging. Moreover, it showed that the discrimination ability and the reproducibility of the proposed index are comparable or larger than those of traditional indices. The proposed index based on the PPG signal will be applied to tele-healthcare systems for monitoring people's health in daily life in combination with the ratio of the standard deviation of the R-R intervals to their average value (CVRR).

Photoplethysmographic signals to predict the success of lumbar sympathetic blockade for lower extremity pain

OBJECTIVE

A prospective, observational study to investigate how photoplethysmography (PPG) signals change during lumbar sympathetic blockade (LSB), and whether these changes can predict sympathetically mediated pain (SMP).

METHODS

Patients with unilateral lower extremity pain and self-reported cold hyperalgesia underwent LSB. Bilateral temperature and PPG signals (AC and DC) were recorded. Power spectrum analysis (PSA) was performed.

RESULTS

Of the total patient cohort (n = 38), eight patients (22.1%) had excellent pain-relief after LSB and were determined to have SMP. In all patients, the PPG AC signal changed immediately after drug administration, before any temperature change. DC signals decreased slowly in a linear fashion. PSA of DC signals showed significantly lower low-frequency/high-frequency (LF/HF) ratios in the SMP group than the sympathetically independent pain group, both before and after LSB. A cut-off value of 2.92 for LF/HF resulted in sensitivity, specificity and positive predictive values for SMP of 75.0%, 76.7% and 3.21 [1.5, 6.9], respectively.

CONCLUSIONS

PPG may be used as an early indicator of a successful LSB and could also be helpful in diagnosing SMP.

Applications of supervised learning to biological signals: ECG signal quality and systemic vascular resistance

Discovering information encoded in non-invasively recorded biosignals which belies an individual's well-being can help facilitate the development of low-cost unobtrusive medical device technologies, or enable the unsupervised performance of physiological assessments without excessive oversight from trained clinical personnel. Although the unobtrusive or unsupervised nature of such technologies often results in less accurate measures than their invasive or supervised counterparts, this disadvantage is typically outweighed by the ability to monitor larger populations than ever before. The expected consequential benefit will be an improvement in healthcare provision and health outcomes for all. The process of discovering indicators of health in unsupervised or unobtrusive biosignal recordings, or automatically ensuring the validity and quality of such signals, is best realized when following a proven systematic methodology. This paper provides a brief tutorial review of supervised learning, which is a sub-discipline of machine learning, and discusses its application in the development of algorithms to interpret biosignals acquired in unsupervised or semi-supervised environments, with the aim of estimating well-being. Some specific examples in the disparate application areas of telehealth electrocardiogram recording and calculating post-operative systemic vascular resistance are discussed in the context of this systematic approach for information discovery.

A clip-free eyeglasses-based wearable monitoring device for measuring photoplethysmograhic signals

An eyeglasses-based device has been developed in this work to acquire photoplethysmogram (PPG) from the nose bridge. This device is aimed to provide wearable physiological monitoring without uncomfortable clips frequently used in PPG measurement from finger and ear. Switching control is applied on the LED and photo detector for power saving. An experiment involving postural change and treadmill jogging among 10 healthy young subjects was carried out to evaluate the performance of the device. Electrocardiogram (ECG) and PPG from finger, ear and nose were simultaneously recorded, from which heart rate (HR) and pulse transit time (PTT) were calculated. The results show that PPG measured from nose and ear are more resistant to motion than signal from finger during exercise. In addition, the difference between PTT measured from ear and nose indicates that local vasomotor activities may exist on ear and/or nose channel, and suggests that PPG from different sites should be used for cuff-less PTT-based BP estimation. We conclude that this wearable device has great potential to be used in the healthcare management in the future.

Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements

Background

Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48).

Methods

Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis.

Results

The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min^-1 when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm^-5 when only one PPG variability feature was used.

Conclusions

These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings.

Implementation of artifact detection in critical care: a methodological review

Artifact detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.

Photoplethysmographic variability analysis in critical care--current progress and future challenges

The concept of early goal-directed therapy emphasizes the need for early diagnosis and intervention to achieve better therapeutic outcomes in critical care. There has been rapidly growing interest in the use of the photoplethysmogram (PPG), also known as the "pulse oximetry waveform", as a noninvasive diagnostic tool in this clinical setting. The peripheral PPG exhibits beat-to-beat variability driven by physiological mechanisms such as respiration and sympathetic vascular activity. This paper provides an overview of the current progress towards the application of PPG waveform variability (PPGV) in emergency and intensive care. Studies to date have demonstrated the potential value of PPGV for assessing a range of pathophysiological conditions including blood loss, sepsis and low systemic vascular resistance. Translation of research findings into clinical practice poses several future challenges, including the need for large scale validation studies with appropriate measurement systems, more robust solutions to signal quality issues (such as motion artifacts), and better physiological understanding of the information-rich PPGV.

Investigation of photoplethysmographic signals and blood oxygen saturation values on healthy volunteers during cuff-induced hypoperfusion using a multimode PPG/SpO₂ sensor

Photoplethysmography (PPG) is a technique widely used to monitor volumetric blood changes induced by cardiac pulsations. Pulse oximetry uses the technique of PPG to estimate arterial oxygen saturation values (SpO₂). In poorly perfused tissues, SpO₂ readings may be compromised due to the poor quality of the PPG signals. A multimode finger PPG probe that operates simultaneously in reflectance, transmittance and a combined mode called "transreflectance" was developed, in an effort to improve the quality of the PPG signals in states of hypoperfusion. Experiments on 20 volunteers were conducted to evaluate the performance of the multimode PPG sensor and compare the results with a commercial transmittance pulse oximeter. A brachial blood pressure cuff was used to induce artificial hypoperfusion. Results showed that the amplitude of the transreflectance AC PPG signals were significantly different (p < 0.05) than the AC PPG signals obtained from the other two conventional PPG sensors (reflectance and transmittance). At induced brachial pressures between 90 and 135 mmHg, the reflectance finger pulse oximeter failed 25 times (failure rate 42.2 %) to estimate SpO₂ values, whereas the transmittance pulse oximeter failed 8 times (failure rate 15.5 %). The transreflectance pulse oximeter failed only 3 times (failure rate 6.8 %) and the commercial pulse oximeter failed 17 times (failure rate 29.4 %).

Spontaneous fluctuations in the peripheral photoplethysmographic waveform: roles of arterial pressure and muscle sympathetic nerve activity

Assessment of spontaneous slow waves in the peripheral blood volume using the photoplethysmogram (PPG) has shown potential clinical value, but the physiological correlates of these fluctuations have not been fully elucidated. This study addressed the contribution of arterial pressure and muscle sympathetic nerve activity (MSNA) in beat-to-beat PPG variability in resting humans under spontaneous breathing conditions. Peripheral PPG waveforms were measured from the fingertip, earlobe, and toe in young and healthy individuals (n = 13), together with the arterial pressure waveform, electrocardiogram, respiration, and direct measurement of MSNA by microneurography. Cross-spectral coherence analysis revealed that among the PPG waveforms, low-frequency fluctuations (0.04-0.15 Hz) in the ear PPG had the highest coherence with arterial pressure (0.71 ± 0.15) and MSNA (0.44 ± 0.18, with a peak of 0.71 ± 0.16 at 0.10 ± 0.03 Hz). The normalized midfrequency powers (0.08-0.15 Hz), with an emphasis on the 0.1-Hz region, were positively correlated between MSNA and the ear PPG (r = 0.77, P = 0.002). Finger and toe PPGs had lower coherence with arterial pressure (0.35 ± 0.10 and 0.30 ± 0.11, respectively) and MSNA (0.33 ± 0.10 and 0.26 ± 0.10, respectively) in the LF band but displayed higher coherence between themselves (0.54 ± 0.09) compared with the ear (P < 0.001), which may suggest the dominance of regional vasomotor activities and a common sympathetic influence in the glabrous skin. These findings highlight the differential mechanisms governing PPG waveform fluctuations across different body sites. Spontaneous PPG variability in the ear includes a major contribution from arterial pressure and MSNA, which may provide a rationale for its clinical utility.

Noninvasive hemodynamic monitoring in the emergency department

PURPOSE OF REVIEW

Emergency department patients are frequently undifferentiated, need accurate risk assessment and stratification, and are time-critical in their need for diagnosis and resuscitation. Valid, noninvasive hemodynamic monitoring modalities are essential to differentiate high from low risk patients, and to perform goal-directed management. This review analyses recent literature, which describes innovation in the range of noninvasive monitoring tools and places them in the emergency medicine context.

RELEVANT FINDINGS

A range of noninvasive measures of hemodynamic status are both commonly used, or are in the research and development phase. Pulse oximetry waveforms, electrocardiogram-based heart rate variability, Doppler and B-mode ultrasound, echocardiography, transthoracic bioimpedance, pressure pulse waveform analysis and near-infrared spectroscopy all have potential value in diagnosis and monitoring of hemodynamics, particularly used to explore autonomic nervous system control of cardiovascular function and by extension the early phases of compensation for illness and injury. Noninvasive techniques coupled with advances in data visualization and pattern recognition bring the potential for revolution to emergency department hemodynamic monitoring.

SUMMARY

Noninvasive measures of hemodynamic status and function are increasingly being used, although much still remains in the research domain. Noninvasive measures may not only offer similar variables to traditional vital signs, but add a new dimension of hemodynamic descriptors.

Identification of high-risk acute coronary syndromes by spectral analysis of ear photoplethysmographic waveform variability

There is a need for robust techniques for early and accurate diagnosis of acute coronary syndromes (ACSs), to avoid inappropriate discharge of patients. This study examined the use of frequency spectrum analysis of heart rate variability (HRV) and photoplethysmogram (PPG) waveform variability for the identification of high-risk ACS patients defined by an elevated cardiac troponin level. The study cohort comprised a convenience sample of adult patients presenting to the emergency department of the Prince of Wales Hospital over a 4 month period complaining of non-traumatic chest pain. Valid electrocardiogram (ECG) and earlobe PPG waveforms together with troponin I test results were obtained from 52 patients at presentation, 4 of which were troponin I positive (Trop 0+). Frequency spectrum analysis was performed on the beat-to-beat HRV and PPG waveform variability (PPGV). The Trop 0+ were found to have significantly higher normalized mid-frequency power (MF(nu)) in HRV (P = 0.017), PPG amplitude variability (P = 0.009) and the cross-spectrum of HRV and PPGV (P = 0.001), which were attributed to reflex sympathetic response to myocardial ischemia. MF(nu) of PPG amplitude had the best overall performance in detecting Trop 0+, with ROC area under the curve of 0.93. The results demonstrate the potential use of ear PPG waveform to identify high-risk heart disease patients, and further highlight the utility of frequency spectrum analysis of PPGV in critical care.

Multivariate classification of systemic vascular resistance using photoplethysmography

Systemic vascular resistance (SVR) classification is useful for the diagnosis and prognosis of critical pathophysiological conditions, with the ability to identify patients with abnormally high or low SVR of immense clinical value. In this study, a supervised classifier, based on Bayes' rule, is employed to classify a heterogeneous group of intensive care unit patients (N = 48) as being below (SVR < 900 dyn s cm(-5)), within (900 ⩽ SVR ⩽ 1200 dyn s cm(-5)) or above (SVR > 1200 dyn s cm(-5)) the clinically accepted range for normal SVR. Features derived from the finger photoplethysmogram (PPG) waveform and other routine cardiovascular measurements (heart rate and mean arterial pressure) were used as inputs to the classifier. In the construction of the classifier model, two techniques were used to approximate the class conditional probability densities--a single Gaussian distribution model (also known as discriminant analysis) and a non-parametric model using the Parzen window kernel density estimation method. An exhaustive feature search was performed to select a feature subset that maximized the performance indicator, Cohen's kappa coefficient (κ). The Gaussian model with multiple features achieved the best overall kappa coefficient (κ = 0.57), although the results from the non-parametric model were comparable (κ = 0.51). The optimum subset in the Gaussian model consisted of PPG waveform variability features, including the low-frequency to high-frequency ratio (LF/HF) and the normalized mid-frequency power (MF(NU)), in addition to the PPG pulse wave features, such as pulse width, peak-to-notch time, reflection index, and notch time ratio. The classifier performed particularly well in discriminating low SVR, with a sensitivity of 85%, specificity of 86%, positive predictive value of 88% and a negative predictive value of 82%. The results highlight the feasibility of deploying a multivariate statistical approach of SVR classification in the clinical setting, simply using a non-invasive and easy-to-measure PPG waveform signal.

Fingertip photoplethysmographic waveform variability and systemic vascular resistance in intensive care unit patients

Low frequency variability in the fingertip photoplethysmogram (PPG) waveform has been utilized for inferring sympathetic vascular control, but its relationship with a quantitative measure of vascular tone has not been established. In this study, we examined the association between fingertip PPG waveform variability (PPGV) and systemic vascular resistance (SVR) obtained from thermodilution cardiac output (CO) and intra-arterial pressure measurements in 48 post cardiac surgery intensive care unit patients. Among the hemodynamic measurements, both CO (P < 0.05) and SVR (P < 0.0001) had statistically significant relationships with the normalized low frequency power (LF(nu)) of PPGV. The LF(nu) of baseline PPGV had moderate but significant positive correlation with SVR (r = 0.54, P < 0.0001), and a value below 52.5 nu was able to identify SVR < 900 dyn s cm⁻⁵ with sensitivity of 59% and specificity of 95%. The results have provided quantitative evidence to confirm the link between fingertip PPGV and sympathetic vascular control. Suppression of LF vasomotor waves leading to dominance of respiration-related HF fluctuations in the fingertip circulation was a specific (though not sensitive) marker of systemic vasodilatation, which could be potentially utilized for the assessment of critical care patients.