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The robust monitoring of heart rate from the video-photoplethysmogram (video-PPG) during challenging conditions requires new analysis techniques. The work reported here extends current research in this area by applying a motion tolerant algorithm to extract high quality video-PPGs from a cohort of subjects undergoing marked heart rate changes during a hypoxic challenge, and exhibiting a full range of skin pigmentation types. High uptimes in reported video-based heart rate (HR vid ) were targeted, while retaining high accuracy in the results. Ten healthy volunteers were studied during a double desaturation hypoxic challenge. Video-PPGs were generated from the acquired video image stream and processed to generate heart rate. HR vid was compared to the pulse rate posted by a reference pulse oximeter device (HR p ). Agreement between video-based heart rate and that provided by the pulse oximeter was as follows: Bias = - 0.21 bpm, RMSD = 2.15 bpm, least squares fit gradient = 1.00 (Pearson R = 0.99, p < 0.0001), with a 98.78% reporting uptime. The difference between the HR vid and HR p exceeded 5 and 10 bpm, for 3.59 and 0.35% of the reporting time respectively, and at no point did these differences exceed 25 bpm. Excellent agreement was found between the HR vid and HR p in a study covering the whole range of skin pigmentation types (Fitzpatrick scales I-VI), using standard room lighting and with moderate subject motion. Although promising, further work should include a larger cohort with multiple subjects per Fitzpatrick class combined with a more rigorous motion and lighting protocol.

Redundancy: it is a word heavy with connotations of lacking usefulness. I often hear that the rationale for not using the continuous wavelet transform (CWT)-even when it appears most appropriate for the problem at hand-is that it is 'redundant'. Sometimes the conversation ends there, as if self-explanatory. However, in the context of the CWT, 'redundant' is not a pejorative term, it simply refers to a less compact form used to represent the information within the signal. The benefit of this new form-the CWT-is that it allows for intricate structural characteristics of the signal information to be made manifest within the transform space, where it can be more amenable to study: resolution over redundancy. Once the signal information is in CWT form, a range of powerful analysis methods can then be employed for its extraction, interpretation and/or manipulation. This theme issue is intended to provide the reader with an overview of the current state of the art of CWT analysis methods from across a wide range of numerate disciplines, including fluid dynamics, structural mechanics, geophysics, medicine, astronomy and finance.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'., (© 2018 The Author(s).)

Background: The physiologic information contained in the video photoplethysmogram is well documented. However, extracting this information during challenging conditions requires new analysis techniques to capture and process the video image streams to extract clinically useful physiologic parameters. We hypothesized that heart rate, respiratory rate, and oxygen saturation trending can be evaluated accurately from video information during acute hypoxia., Methods: Video footage was acquired from multiple desaturation episodes during a porcine model of acute hypoxia using a standard visible light camera. A novel in-house algorithm was used to extract photoplethysmographic cardiac pulse and respiratory information from the video image streams and process it to extract a continuously reported video-based heart rate (HRvid), respiratory rate (RRvid), and oxygen saturation (SvidO2). This information was then compared with HR and oxygen saturation references from commercial pulse oximetry and the known rate of respiration from the ventilator., Results: Eighty-eight minutes of data were acquired during 16 hypoxic episodes in 8 animals. A linear mixed-effects regression showed excellent responses relative to a nonhypoxic reference signal with slopes of 0.976 (95% confidence interval [CI], 0.973-0.979) for HRvid; 1.135 (95% CI, 1.101-1.168) for RRvid, and 0.913 (95% CI, 0.905-0.920) for video-based oxygen saturation. These results were obtained while maintaining continuous uninterrupted vital sign monitoring for the entire study period., Conclusions: Video-based monitoring of HR, RR, and oxygen saturation may be performed with reasonable accuracy during acute hypoxic conditions in an anesthetized porcine hypoxia model using standard visible light camera equipment. However, the study was conducted during relatively low motion. A better understanding of the effect of motion and the effect of ambient light on the video photoplethysmogram may help refine this monitoring technology for use in the clinical environment.

Cerebral blood flow (CBF) is regulated over a range of systemic blood pressures by the cerebral autoregulation (CA) control mechanism. This range lies within the lower and upper limits of autoregulation (LLA, ULA), beyond which blood pressure drives CBF, and CA function is considered impaired. A standard method to determine autoregulation limits noninvasively using NIRS technology is via the COx measure: a moving correlation index between mean arterial pressure and regional oxygen saturation. In the intact region, there should be no correlation between these variables whereas in the impaired region, the correlation index should approximate unity. In practice, however, the data may be noisy and/or the intact region may often exhibit a slightly positive relationship. This positive relationship may render traditional autoregulation limit calculations difficult to perform, resulting in the need for manual interpretation of the data using arbitrary thresholds. Further, the underlying mathematics of the technique are asymmetric in terms of the results produced for impaired and intact regions and are, in fact, not computable for the ideal case within the intact region. In this work, we propose a novel gradient adjustment method (GACOx) to enhance the differences in COx values observed in the intact and impaired regions. Results from a porcine model (N = 8) are used to demonstrate that GACOx is successful in determining LLA values where traditional methods fail. It is shown that the derived GACOx indices exhibit a mean difference between the intact/impaired regions of 1.54 ± 0.26 (mean ± SD), compared to 0.14 ± 0.10 for the traditional COx method. The GACOx effectively polarizes the COx data in order to better differentiate the intact and impaired zones and, in doing so, makes the determination of the LLA and ULA points a simpler and more consistent task. The method lends itself to the automation of the robust determination of autoregulation zone limits.

The extraction of heart rate from a video-based biosignal during motion using a novel wavelet-based ensemble averaging method is described. Running Wavelet Archetyping (RWA) allows for the enhanced extraction of pulse information from the time-frequency representation, from which a video-based heart rate (HRvid) can be derived. This compares favorably to a reference heart rate derived from a pulse oximeter.

Adequate perfusion of blood is fundamental to brain tissue viability, and failure to appropriately regulate cerebral blood flow is related to neurological damage. Cerebral tissue oxygenation is commonly used as a surrogate of cerebral blood flow for non-invasive measures of autoregulation, but may only be valid during periods of constant oxygen delivery. We present a new algorithm to correct for supply oxygen-induced variations in cerebral tissue oxygenation, and we validate it by measuring the improved correlation of the corrected tissue oxygenation with blood flow. The algorithm corrects tissue oxygenation by calculating its linear dependence with arterial oxygen saturation below a baseline level. A porcine model (N=8) of hypoxia is used to test the algorithm and compare the tissue oxygen correction with a blood flow reference signal. The correction provides significant improvement in the correlation between flow and tissue oxygenation (Wilcoxon signed rank, p<;0.01), and for the root mean square distance between the corrected hypoxic periods and the rSO 2 -flow regression line (Wilcoxon signed rank, p<;0.01). This method allows the correction of tissue oxygenation levels used in the non-invasive monitoring of autoregulation.

Background: Intermittent measurement of respiratory rate via observation is routine in many patient care settings. This approach has several inherent limitations that diminish the clinical utility of these measurements because it is intermittent, susceptible to human error, and requires clinical resources. As an alternative, a software application that derives continuous respiratory rate measurement from a standard pulse oximeter has been developed. We sought to determine the performance characteristics of this new technology by comparison with clinician-reviewed capnography waveforms in both healthy subjects and hospitalized patients in a low-acuity care setting., Methods: Two independent observational studies were conducted to validate the performance of the Medtronic Nellcor Respiration Rate Software application. One study enrolled 26 healthy volunteer subjects in a clinical laboratory, and a second multicenter study enrolled 53 hospitalized patients. During a 30-minute study period taking place while participants were breathing spontaneously, pulse oximeter and nasal/oral capnography waveforms were collected. Pulse oximeter waveforms were processed to determine respiratory rate via the Medtronic Nellcor Respiration Rate Software. Capnography waveforms reviewed by a clinician were used to determine the reference respiratory rate., Results: A total of 23,243 paired observations between the pulse oximeter-derived respiratory rate and the capnography reference method were collected and examined. The mean reference-based respiratory rate was 15.3 ± 4.3 breaths per minute with a range of 4 to 34 breaths per minute. The Pearson correlation coefficient between the Medtronic Nellcor Respiration Rate Software values and the capnography reference respiratory rate is reported as a linear correlation, R, as 0.92 ± 0.02 (P < .001), whereas Lin's concordance correlation coefficient indicates an overall agreement of 0.85 ± 0.04 (95% confidence interval [CI] +0.76; +0.93) (healthy volunteers: 0.94 ± 0.02 [95% CI +0.91; +0.97]; hospitalized patients: 0.80 ± 0.06 [95% CI +0.68; +0.92]). The mean bias of the Medtronic Nellcor Respiration Rate Software was 0.18 breaths per minute with a precision (SD) of 1.65 breaths per minute (healthy volunteers: 0.37 ± 0.78 [95% limits of agreement: -1.16; +1.90] breaths per minute; hospitalized patients: 0.07 ± 1.99 [95% limits of agreement: -3.84; +3.97] breaths per minute). The root mean square deviation was 1.35 breaths per minute (healthy volunteers: 0.81; hospitalized patients: 1.60)., Conclusions: These data demonstrate the performance of the Medtronic Nellcor Respiration Rate Software in healthy subjects and patients hospitalized in a low-acuity care setting when compared with clinician-reviewed capnography. The observed performance of this technology suggests that it may be a useful adjunct to continuous pulse oximetry monitoring by providing continuous respiratory rate measurements. The potential patient safety benefit of using combined continuous pulse oximetry and respiratory rate monitoring warrants assessment.

The potential for a simple, non-invasive measure of respiratory effort based on the pulse oximeter signal - the photoplethysmogram or 'pleth' - was investigated in a pilot study. Several parameters were developed based on a variety of manifestations of respiratory effort in the signal, including modulation changes in amplitude, baseline, frequency and pulse transit times, as well as distinct baseline signal shifts. Thirteen candidate parameters were investigated using data from healthy volunteers. Each volunteer underwent a series of controlled respiratory effort maneuvers at various set flow resistances and respiratory rates. Six oximeter probes were tested at various body sites. In all, over three thousand pleth-based effort-airway pressure (EP) curves were generated across the various airway constrictions, respiratory efforts, respiratory rates, subjects, probe sites, and the candidate parameters considered. Regression analysis was performed to determine the existence of positive monotonic relationships between the respiratory effort parameters and resulting airway pressures. Six of the candidate parameters investigated exhibited a distinct positive relationship (p<0.001 across all probes tested) with increasing upper airway pressure repeatable across the range of respiratory rates and flow constrictions studied. These were: the three fundamental modulations in amplitude (AM-Effort), baseline (BM-Effort) and respiratory sinus arrhythmia (RSA-Effort); two pulse transit time modulations - one using a pulse oximeter probe and an ECG (P2E-Effort) and the other using two pulse oximeter probes placed at different peripheral body sites (P2-Effort); and baseline shifts in heart rate, (BL-HR-Effort). In conclusion, a clear monotonic relationship was found between several pleth-based parameters and imposed respiratory loadings at the mouth across a range of respiratory rates and flow constrictions. The results suggest that the pleth may provide a measure of changing upper airway dynamics indicative of the effort to breathe., (Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.)

A novel pulse transit time proxy measurement, slope transit time (STT), is proposed in this letter. STT is based on geometrical considerations of the arriving photoplethysmographic cardiac waveform and its computation requires only the measurement of a single point on each cardiac beat arriving at the peripheral site. This novel transit time is explained conceptually and its implementation illustrated through its application to signals from respiratory effort, Müller maneuver, and obstructive sleep apnea trials.

DPOP is a measure of the strength of respiratory modulations present in the pulse oximetry photoplethysmogram (pleth) waveform. It has been proposed as a non-invasive parameter for the prediction of the response to volume expansion in hypovolemic patients. The effect of resistive breathing on the DPOP parameter was studied to determine whether it may have an adjunct use as a measure of respiratory effort. Healthy volunteers were tasked to breathe at fixed respiratory rates over a range of airway resistances generated by a flow resistor inserted within a mouthpiece. Changes in respiratory efforts, effected by the subjects and measured as airway pressures at the mouth, were compared to DPOP values derived from a finger pulse oximeter probe. It was found that the increased effort to breathe manifests itself as an associated increase in DPOP. Further, a relationship between DPOP and percent modulation of the pleth waveform was observed. A version of the DPOP algorithm that corrects for low perfusion was implemented which resulted in an improved relationship between DPOP and PPV. Although a limited cohort of seven volunteers was used, the results suggest that DPOP may be useful as a respiratory effort parameter, given that the fluid level of the patient is maintained at a constant level over the period of analysis., Competing Interests: Compliance with ethical standards Conflicts of interest Paul S. Addison is an employee of Medtronic who sponsored the research.

Cerebral blood flow is regulated over a range of systemic blood pressures through the cerebral autoregulation (CA) control mechanism. The COx measure based on near infrared spectroscopy (NIRS) has been proposed as a suitable technique for the analysis of CA as it is non-invasive and provides a simpler acquisition methodology than other methods. The COx method relies on data binning and thresholding to determine the change between intact and impaired autoregulation zones. In the work reported here we have developed a novel method of differentiating the intact and impaired CA blood pressure regimes using clustering methods on unbinned data. K-means and Gaussian mixture model algorithms were used to analyse a porcine data set. The determination of the lower limit of autoregulation (LLA) was compared to a traditional binned data approach. Good agreement was found between the methods. The work highlights the potential application of using data clustering tools in the monitoring of CA function., Competing Interests: The authors are all employees of Medtronic. The research was sponsored by Medtronic. Financial disclosure The authors are all employees of Medtronic.

A method for extracting video photoplethysmographic information from an RGB video stream is tested on data acquired during a porcine model of acute hypoxia. Cardiac pulsatile information was extracted from the acquired signals and processed to determine a continuously reported oxygen saturation (SvidO2). A high degree of correlation was found to exist between the video and a reference from a pulse oximeter. The calculated mean bias and accuracy across all eight desaturation episodes were -0.03% (range: -0.21% to 0.24%) and accuracy 4.90% (range: 3.80% to 6.19%) respectively. The results support the hypothesis that oxygen saturation trending can be evaluated accurately from a video system during acute hypoxia.

A novel method of extracting heart rate and oxygen saturation from a video-based biosignal is described. The method comprises a novel modular continuous wavelet transform approach which includes: performing the transform, undertaking running wavelet archetyping to enhance the pulse information, extraction of the pulse ridge time-frequency information [and thus a heart rate (HRvid) signal], creation of a wavelet ratio surface, projection of the pulse ridge onto the ratio surface to determine the ratio of ratios from which a saturation trending signal is derived, and calibrating this signal to provide an absolute saturation signal (SvidO2). The method is illustrated through its application to a video photoplethysmogram acquired during a porcine model of acute desaturation. The modular continuous wavelet transform-based approach is advocated by the author as a powerful methodology to deal with noisy, non-stationary biosignals in general.

DPOP (∆POP or Delta-POP) is a non-invasive parameter which measures the strength of respiratory modulations present in the pulse oximetry photoplethysmogram (pleth) waveform. It has been proposed as a non-invasive surrogate parameter for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. Many groups have reported on the DPOP parameter and its correlation with PPV using various semi-automated algorithmic implementations. The study reported here demonstrates the performance gains made by adding increasingly sophisticated signal processing components to a fully automated DPOP algorithm. A DPOP algorithm was coded and its performance systematically enhanced through a series of code module alterations and additions. Each algorithm iteration was tested on data from 20 mechanically ventilated OR patients. Correlation coefficients and ROC curve statistics were computed at each stage. For the purposes of the analysis we split the data into a manually selected 'stable' region subset of the data containing relatively noise free segments and a 'global' set incorporating the whole data record. Performance gains were measured in terms of correlation against PPV measurements in OR patients undergoing controlled mechanical ventilation. Through increasingly advanced pre-processing and post-processing enhancements to the algorithm, the correlation coefficient between DPOP and PPV improved from a baseline value of R = 0.347 to R = 0.852 for the stable data set, and, correspondingly, R = 0.225 to R = 0.728 for the more challenging global data set. Marked gains in algorithm performance are achievable for manually selected stable regions of the signals using relatively simple algorithm enhancements. Significant additional algorithm enhancements, including a correction for low perfusion values, were required before similar gains were realised for the more challenging global data set.

Respiratory rate is recognized as a clinically important parameter for monitoring respiratory status on the general care floor (GCF). Currently, intermittent manual assessment of respiratory rate is the standard of care on the GCF. This technique has several clinically-relevant shortcomings, including the following: (1) it is not a continuous measurement, (2) it is prone to observer error, and (3) it is inefficient for the clinical staff. We report here on an algorithm designed to meet clinical needs by providing respiratory rate through a standard pulse oximeter. Finger photoplethysmograms were collected from a cohort of 63 GCF patients monitored during free breathing over a 25-min period. These were processed using a novel in-house algorithm based on continuous wavelet-transform technology within an infrastructure incorporating confidence-based averaging and logical decision-making processes. The computed oximeter respiratory rates (RRoxi) were compared to an end-tidal CO2 reference rate (RRETCO2). RRETCO2 ranged from a lowest recorded value of 4.7 breaths per minute (brpm) to a highest value of 32.0 brpm. The mean respiratory rate was 16.3 brpm with standard deviation of 4.7 brpm. Excellent agreement was found between RRoxi and RRETCO2, with a mean difference of -0.48 brpm and standard deviation of 1.77 brpm. These data demonstrate that our novel respiratory rate algorithm is a potentially viable method of monitoring respiratory rate in GCF patients. This technology provides the means to facilitate continuous monitoring of respiratory rate, coupled with arterial oxygen saturation and pulse rate, using a single non-invasive sensor in low acuity settings.

Near-infrared spectroscopy (NIRS) has been proposed as a suitable technique for the analysis of cerebral autoregulation as it provides a simpler acquisition methodology and more artifact-free signal. A number of sophisticated wavelet transform methods have recently emerged to quantify the cerebral autoregulation mechanism using NIRS and blood pressure signals. These provide an enhanced partitioning of signal information via the time-frequency plane, which facilitates improved extraction of the components of interest. This area is reviewed, and enhancements to this form of analysis are suggested.

A novel method of identifying stable phase coupling behavior of two signals within the wavelet transform time-frequency plane is presented. The technique employs the cross-wavelet transform to provide a map of phase coupling followed by synchrosqueezing to collect the stable phase regime information. The resulting synchrosqueezed cross-wavelet transform method (Synchro-CrWT) is illustrated using a synthetic signal and then applied to the analysis of the relationship between biosignals used in the analysis of cerebral autoregulation function.

Objective: The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RR(OXI) algorithm based on this principle and its performance on healthy subject trial data is described herein., Methods: Finger PPGs were collected from a cohort of 139 healthy adult volunteers monitored during free breathing over an 8-min period. These were subsequently processed using a novel in-house algorithm based on continuous wavelet transform technology within an infrastructure incorporating weighted averaging and logical decision making processes. The computed oximeter respiratory rates (RR(oxi)) were then compared to an end-tidal CO2 reference rate RR(ETCO2)., Results: RR(ETCO2) ranged from a lowest recorded value of 2.97 breaths per min (br/min) to a highest value of 28.02 br/min. The mean rate was 14.49 br/min with standard deviation of 4.36 br/min. Excellent agreement was found between RR(oxi) and RR(ETCO2), with a mean difference of -0.23 br/min and standard deviation of 1.14 br/min. The two measures are tightly spread around the line of agreement with a strong correlation observable between them (R2 = 0.93)., Conclusions: These data indicate that RR(oxi) represents a viable technology for the measurement of respiratory rate of healthy individuals.

Objective: We studied the application of our algorithm for the robust extraction of respiratory information from the pulse oximeter signal acquired from a selection of patients attending the chest clinic., Methods: Photoplethysmograms were obtained from 16 individuals: 13 patients with various conditions in the respiratory ward and three healthy subjects. Wavelet transforms were generated from which respiratory information was extracted to obtain a measure of respiratory rate. This measured rate was compared with the respiratory rate determined by one of a variety of other means (a digital end tidal CO(2) signal, the output from a non-invasive ventilation device, or a switch actuated by the patient or observer.), Results: Respiratory rates varied from 6.2 to 35.8 breaths per minute (bpm). The oximeter rate determined through our method matched the marker rate obtained for all patients to within 1 bpm., Conclusion: The technique allows the measurement of respiratory rate directly from the photoplethysmogram of a pulse oximeter, and leads the way for development of a simple non-invasive combined respiration and saturation monitor useful for patients with all forms of breathlessness.

Background: Wavelet-based methods of analyzing ECG signals have been used to identify specific features in cardiac arrhythmias. Since some of these features are rate dependent, it is a requirement that they are examined across a range of physiological heart rates. The wavelet transform is a signal analysis tool that can elucidate spectral and temporal information simultaneously from complex signals, including the ECG. The aim of this study was to identify the local frequency characteristics of the ECG using a real-time wavelet scalogram and to study the rate dependence of these features., Methods: We examined the spectral temporal behavior of the local characteristics of the electrocardiogram (ECG) of 10 patients, in whom precise control of heart rate was achieved using right atrial pacing. Temporary reprogramming was used to adjust the paced atrial rate to predetermined values so that a rate-controlled rhythm was produced that closely resembled sinus rhythm., Results: Rate-dependent features are seen on time-frequency scalograms. As the rate increases, the temporal spacing of features decrease and the frequency bands shift upward on the plot. Two patients with abnormal atrioventricular conduction demonstrate features of Wenckebach conduction and fusion., Conclusions: Characterization of the rate-dependent features of the ECG in a paced atrial rhythm by wavelet transform techniques has revealed some additional information not readily seen on single lead ECG analysis. This model provides a surrogate for changes that might be expected during rate changes in physiological sinus rhythm. It is envisaged that this method will offer advantages in detecting features of clinical significance that may not be readily seen by existing methods.

The mathematical models used to describe the dynamical behavior of a group of road vehicles traveling in a single lane without overtaking are known as car-following models. These models are widely used in many commercially available microscopic traffic simulation software packages. They attempt to mimic the interactions between individual vehicles that are traveling sufficiently close together for the behavior of each vehicle to be dependent upon the motion of the vehicle immediately in front. In this paper we modify the traditional car-following model by adding a new nonlinear term to take account of the driver attempting to achieve a certain desired intervehicle separation distance as well as the traditional aim of matching the velocity of the vehicle ahead. Numerical solution of the resulting coupled system of nonlinear differential equations is used to analyze the stability of the equilibrium solution to a periodic perturbation. For certain parameter values chaotic oscillations are generated, consisting of a broad spectrum of frequency components. Such chaotic motion produces extremely complicated dynamical behavior that has an inherent lack of predictability associated with it. The results of simulating over a range of parameter values are presented and, where it is present, the degree of chaos is estimated. (c) 1998 American Institute of Physics.