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Purpose: Respiratory rate (RR) is one of the most common vital signs with numerous clinical uses. It is an important indicator of acute illness and a significant change in RR is often an early indication of a potentially serious complication or clinical event such as respiratory tract infection, respiratory failure and cardiac arrest. Early identification of changes in RR allows for prompt intervention, whereas failing to detect a change may result in poor patient outcomes. Here, we report on the performance of a depth-sensing camera system for the continuous non-contact 'touchless' monitoring of Respiratory Rate. Methods: Seven healthy subjects undertook a range of breathing rates from 4 to 40 breaths-per-minute (breaths/min). These were set rates of 4, 5, 6, 8, 10, 15, 20, 25, 30, 35 and 40 breaths/min. In total, 553 separate respiratory rate recordings were captured across a range of conditions including body posture, position within the bed, lighting levels and bed coverings. Depth information was acquired from the scene using an Intel D415 RealSenseTM camera. This data was processed in real-time to extract depth changes within the subject's torso region corresponding to respiratory activity. A respiratory rate RRdepth was calculated using our latest algorithm and output once-per-second from the device and compared to a reference. Results: An overall RMSD accuracy of 0.69 breaths/min with a corresponding bias of -0.034 was achieved across the target RR range of 4–40 breaths/min. Bland-Altman analysis revealed limits of agreement of -1.42 to 1.36 breaths/min. Three separate sub-ranges of low, normal and high rates, corresponding to < 12, 12–20, > 20 breaths/min, were also examined separately and each found to demonstrate RMSD accuracies of less than one breath-per-minute. Conclusions: We have demonstrated high accuracy in performance for respiratory rate based on a depth camera system. We have shown the ability to perform well at both high and low rates which are clinically important. [ABSTRACT FROM AUTHOR]

The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RRdepth) and tidal volume (TVdepth) estimates. The bias and root mean squared difference (RMSD) accuracy between RRdepth and the ventilator reference, RRvent, across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RRdepth = 0.96 × RRvent + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TVdepth and the reference TVvent across the whole data set was found to be − 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TVdepth = 0.79 × TVvent—0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RRdepth is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TVdepth may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting. [ABSTRACT FROM AUTHOR]

Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report here on an algorithm to determine respiratory rate continuously and robustly using a non-contact method based on depth sensing camera technology. The respiratory rate of 14 healthy volunteers was studied during an acute hypoxic challenge where blood oxygen saturation was reduced in steps to a target 70% oxygen saturation and which elicited a wide range of respiratory rates. Depth sensing data streams were acquired and processed to generate a respiratory rate (RRdepth). This was compared to a reference respiratory rate determined from a capnograph (RRcap). The bias and root mean squared difference (RMSD) accuracy between RRdepth and the reference RRcap was found to be 0.04 bpm and 0.66 bpm respectively. The least squares fit regression equation was determined to be: RRdepth = 0.99 × RRcap + 0.13 and the resulting Pearson correlation coefficient, R, was 0.99 (p < 0.001). These results were achieved with a 100% reporting uptime. In conclusion, excellent agreement was found between RRdepth and RRcap. Further work should include a larger cohort combined with a protocol to further test algorithmic performance in the face of motion and interference typical of that experienced in the clinical setting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (HRvid) 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. HRvid was compared to the pulse rate posted by a reference pulse oximeter device (HRp). 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 HRvid and HRp 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 HRvid and HRp 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (RR) were compared to an end-tidal CO reference rate (RR). RR 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 RR and RR, 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. [ABSTRACT FROM AUTHOR]

DPOP quantifies respiratory modulations in the photoplethysmogram. It has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. The correlation between DPOP and PPV may degrade due to low perfusion effects. We implemented an automated DPOP algorithm with an optional correction for lowperfusion. These two algorithmvariants (DPOPa and DPOPb) were tested on data from 20mechanically ventilatedORpatients split into a benign "stable region" subset and awhole record "global set." Strong correlationwas found between DPOP and PPV for both algorithms when applied to the stable data set: R = 0.83/0.85 for DPOPa/DPOPb. However, a marked improvement was found when applying the low perfusion correction to the global data set: R = 0.47/0.73 for DPOPa/DPOPb. Sensitivities, Specificities, and AUCs were 0.86, 0.70, and 0.88 for DPOPa/stable region; 0.89, 0.82, and 0.92 for DPOPb/stable region; 0.81, 0.61, and 0.73 for DPOPa/global region; 0.83, 0.76, and 0.86 for DPOPb/global region. An improvement was found in all results across both data sets when using the DPOPb algorithm. Further,DPOPb showed marked improvements, both in terms of its values, and correlation with PPV, for signals exhibiting low percent modulations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 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) were then compared to an end-tidal CO reference rate ( $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$). Results $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$ 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 and $$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$, 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 ( R = 0.93). Conclusions These data indicate that RR represents a viable technology for the measurement of respiratory rate of healthy individuals. [ABSTRACT FROM AUTHOR]

An obituary for Scottish historian Angus Calder is presented.

The article presents a study on the aspects of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) as well as their application in the biomedical signal processing. It notes that CWT is fitted for high-resolution interrogation while DWT provides powerful methodologies where potent compression strategies are based. It concludes that wavelet transform is a flexible time-frequency decomposition tool which serves as groundwork of useful signal analysis and coding system.

Ventricular tachyarrhythmias are potentially lethal cardiac pathologies and the commonest cause of sudden cardiac death. Efforts to predict the onset of such events are based on feature extraction from the surface ECG. T-wave alternans (TWAs) are considered a marker of abnormal ventricular function that may be associated with ventricular tachycardia (VT) and ventricular fibrillation. A novel TWA detection algorithm utilizing the continuous wavelet transform is described in this paper. Simulated ECGs containing artificial TWA were used to test the algorithm that achieved a sensitivity of 91.40% and a specificity of 94.00%. The algorithm was subsequently used to analyze the ECGs of eight patients prior to the onset of VT. Of these, the algorithm indicated that five patients exhibited TWA prior to the onset of the tachyarrhythmic events, while the remaining three patients did not exhibit identifiable TWA. Healthy individuals were also studied in which one short TWA episode was detected by the algorithm. How- ever, closer visual inspection of the data revealed this to be a likely false positive result. [ABSTRACT FROM AUTHOR]

Background: We have developed an automated algorithm to allow the measurement of respiratory rate directly from the photoplethysmogram (pulse oximeter waveform). Aim: To test the algorithm's ability to determine respiratory rate in children. Methods: A convenience sample of patients attending a paediatric Accident and Emergency Department was monitored using a purpose-built pulse oximeter and the photoplethysmogram (PPG) recorded. Respiration was also recorded by an observer activating a push-button switch in synchronization with the child's breathing. The switch marker signals were processed to derive a manual respiratory rate that was compared with the wavelet-based oximeter respiratory rate derived from the PPG signal. Results: Photoplethysmograms were obtained from 18 children aged 18 mo to 12 y, breathing spontaneously at rates of 17 to 27 breaths per minute. There was close correspondence between the wavelet-based oximeter respiration rate and the manual respiratory rate, with the difference between them being less than one breath per minute in all children. Conclusion: Our automated algorithm allows the accurate determination of respiratory rate from photoplethysmograms of a heterogeneous group of children. We believe that our automated wavelet-based signal-processing techniques could soon be easily incorporated into current pulse oximetry technology. [ABSTRACT FROM AUTHOR]

The parasitoid Microctonus hyperodae Loan (Hymenoptera: Braconidae) was introduced into New Zealand to control the weevil Listronotus bonariensis (Kuschel) (Coleoptera: Curculionidae), a major pest of graminaceous plants. Four experiments were conducted to investigate the effects of various pesticides that are commonly used in the pastoral environments of L. bonariensis and M. hyperodae. Topical applications of aqueous solutions prepared from commercial formulations of five herbicides were not toxic, but the surfactant Silwett L-77 increased M. hyperodae mortality relative to the water-treated controls. Laboratory assays showed that M. hyperodae adults were susceptible to chlorpyrifos residues on pasture foliage following application of the insecticide to field plots at ≥5 g a.i. ha−1. Maintenance of L. bonariensis on ryegrass in the laboratory showed that treatment of the food plants with chlorpyrifos at ≥96 g a.i. ha−1 reduced L. bonariensis survivorship and ultimately reduced M. hyperodae prepupal emergence from those hosts. Initially, mortalities of non-parasitized L. bonariensis were significantly greater than for parasitized L. bonariensis. Maintenance of parasitized L. bonariensis on diflubenzuron-treated ryegrass plants arrested M. hyperodae larval development in the host and ultimately reduced prepupal emergence of M. hyperodae from those hosts. Despite the arrested development of M. hyperodae, the mortality of L. bonariensis hosts was increased. Adult M. hyperodae successfully reared from hosts maintained on diflubenzuron (12.5 g a.i. ha−1) treated food plants had reduced reproductive potential. The consequences of pasture management strategies that employ pesticides are discussed in relation to biocontrol of L. bonariensis by M. hyperodae. [ABSTRACT FROM AUTHOR]

The South American curculionid Listronotus bonariensis (Kuschel) is an important pest of pastures in New Zealand. Population census data were gathered for L. bonariensis in northern New Zealand pastures during 1980–1983 in the absence of parasitism and again in 1991–1996 after the introduction and establishment of the braconid parasitoid Microctonus hyperodae Loan as a biological control agent. M. hyperodae achieved high rates of parasitism, with 75–90% of overwintering L. bonariensis parasitized within 3 yr of the parasitoid establishing at a site. Multistratum analysis of variance (ANOVA), with allowance for variation in host plant resource (numbers of Neotyphodium-free grass tillers), indicated reduction in the abundance of L. bonariensis life stages in the early part of life cycle. Although providing evidence for suppression of L. bonariensis, these analyses indicated the regulatory role of M. hyperodae was weak because L. bonariensis populations continued to exhibit marked intergenerational variability in abundance. Analyses of life tables indicated larval + pupal survival contributed most to intergenerational changes in abundance, irrespective of presence or absence of M. hyperodae. However, the density dependence of the stage survivals was modified in the presence of the parasitoid, with loss of density-dependent mortality in overwintering adults and increased density dependence in population natality. Regression analyses indicated dual contribution of parasitism and host plant resource to regulation of population natality and population trend in L. bonariensis. We conclude that M. hyperodae is a useful adjunct to host plant resistance in reducing the economic status of L. bonariensis populations in northern New Zealand pastures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a method for the detection of pertinent signal features masked by other features with similar spectral content but which perturb (but not necessarily periodically) other constituent parts of the signal. This modulation may be a modulation in frequency and/or amplitude of a locus of chosen selected points on a transform surface. A secondary transform is then performed on this derived signal. We apply the method, which we have termed secondary wavelet feature decoupling (SWFD), to the specific problem of decomposition and analysis of signals used in pulse oximetry where perturbations of the ridges in wavelet space associated with the pulse band may be used to detect respiration features. The method has wide application to problematic signals in many other disciplines. [ABSTRACT FROM AUTHOR]

Focuses on finding coordinated atrial activity during ventricular fibrillation using wavelet decomposition. Analysis of surface electrocardiogram with a novel signal analysis technique; Unreported structure within the ECG tracing; Evidence linking the structure to unexpected phenomena occurring in the heart.

Presents a study which concentrated on wavelet transform analysis applied to velocity signals from a variety of developing wake flows in an open channel. Discussion on the wavelet transform theory; Details of the experimental setup; Wavelet analysis of experimental data; Conclusions.

A stochastic model that can simulate non-Fickian (scale-dependent) subsurface dispersion by imposing long-range correlation structure within a particle-based transport scheme is detailed here. The model uses increments from self-similar random fractal processes. These processes [fractional Brownian motions (FBMs)] are characterized by an index H, which is related to the correlation properties of the function. Temporally correlated FBMs that mimic the faster-than-Fickian growth observed in contaminant plumes in heterogeneous subsurface porous media are chosen. It is shown how FBMs can be incorporated within traditional Langevin-based formulations of the random-walk particle tracking models to describe mass displacements. A Fokker-Planck diffusion equation with a time-dependent tensor and its solution are presented to describe FBM anomalous dispersion. A relationship of the model's parameters to measurable porous media parameters is given. [ABSTRACT FROM AUTHOR]

An integrated method for the prediction of the spatial pollution distribution within a street canyon directly from a microscopic traffic simulation model is outlined. The traffic simulation package Paramics is used to model the flow of vehicles in realistic traffic conditions on a real road network. This produces details of the amount of pollutant produced by each vehicle at any given time. The authors calculate the dispersion of the pollutant using a particle tracking diffusion method which is superimposed on a known velocity and turbulence field. This paper shows how these individual components may be integrated to provide a practical street canyon pollution model. The resulting street canyon pollution model provides isoconcentrations of pollutant within the road topography. [ABSTRACT FROM AUTHOR]

Natural fractal objects, unlike their mathematical counterparts, tend to exhibit power law scaling over a limited range of scales. In this paper a prefractal theoretical framework is constructed for natural fractal objects and illustrated through the application to natural crack surfaces. The prefractal model is linked with both traditional and current fractal methods for determining the energy of fracture. It is shown how the prefractal model gives the same size effect law as incorporated by existing recent fractal theories, but with the advantage of retaining the correct physical dimensions. The prefractal theory developed within the paper has general application to a range of other scientific and engineering problems. [ABSTRACT FROM AUTHOR]

Describes mathematical models used to illustrate the dynamical behavior of a group of road vehicles traveling in a single lane without overtaking. Application of the model in commercially available microscopic traffic simulation software packages; Attempts to mimic interactions between individual vehicles that are travelling sufficiently close together.

There is considerable interest in the noncontact monitoring of patients as it allows for reduced restriction of patients, the avoidance of single-use consumables and less patient–clinician contact and hence the reduction of the spread of disease. A technology that has come to the fore for noncontact respiratory monitoring is that based on depth sensing camera systems. This has great potential for the monitoring of a range of respiratory information including the provision of a respiratory waveform, the calculation of respiratory rate and tidal volume (and hence minute volume). Respiratory patterns and apneas can also be observed in the signal. Here we review the ability of this method to provide accurate and clinically useful respiratory information. [ABSTRACT FROM AUTHOR]