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"Addison, Paul"
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No turning back : the peacetime revolutions of post-war Britain
\"Since the Second World War, Britain has been transformed by a series of peaceful revolutions---the rise of multiculturalism, the permissive society, and the service-based consumer economy, among many others. These, Paul Addison argues, have been more powerful agents of change than the Battle of the Somme or the Blitz ever were.\" \"No Turning Back looks at the changing face of Britain in this period of rapid transformation, highlighting just how much has been gained---but not forgetting that much, too, has been lost.\" \"Historian Paul Addison was born in the 1940s. In No Turning Back, he surveys the vast changes in the character of British society that he has observed in the period since. A series of peaceful revolutions has transformed the country; the comparative peace and growing prosperity of the second half of the twentieth century, he contends, have been more powerful agents of change than the Battle of the Somme or the Blitz.\" \"The Second World War led to the welfare state but in some ways reinforced a conservative way of life. The changes unleashed by the Sixties and Seventies were more radical. Much of the sexual morality preached, if not practised, for centuries has been dismantled with the creation of a لpermissive society'. The employment and career chances of women have radically improved. A white nation has been transformed into a multiracial one. An economy founded upon manufacturing under the watchful eye of the لgentlemen in Whitehall' has morphed into a free market system, heavily dependent on finance, services, and housing; a predominantly working class society has evolved into a predominantly middle class one. And the United Kingdom, which once looked as solid as the rock of Gibraltar, now looks increasingly fragile, as Wales and especially Scotland have started to go their separate ways.\".
Book
Introduction to redundancy rules
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 continuouswavelet transform comes of age’.
Journal Article
Noncontact Respiratory Monitoring Using Depth Sensing Cameras: A Review of Current Literature
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.
Journal Article
Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera
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 (RR
depth
) and tidal volume (TV
depth
) estimates. The bias and root mean squared difference (RMSD) accuracy between RR
depth
and the ventilator reference, RR
vent
, 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: RR
depth
= 0.96 × RR
vent
+ 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 TV
depth
and the reference TV
vent
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: TV
depth
= 0.79 × TV
vent
—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 RR
depth
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 TV
depth
may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting.
Journal Article
Robust Non-Contact Monitoring of Respiratory Rate using a Depth Camera
PurposeRespiratory 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.MethodsSeven 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.ResultsAn 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.ConclusionsWe 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.
Journal Article
Continuous non-contact monitoring of neonatal activity
Purpose
Neonatal activity is an important physiological parameter in the neonatal intensive care unit (NICU). The degree of neonatal activity is associated with under and over-sedation and may also indicate the onset of disease. Activity may also cause motion noise on physiological signals leading to false readings of important parameters such as heart rate, respiratory rate or oxygen saturation or, in extreme cases, a failure to calculate the parameter at all. Here we report on a novel neonatal activity monitoring technology we have developed using a Random Forest machine learning algorithm trained on features extracted from a depth video stream from a commercially available depth sensing camera.
Methods
A cohort of twenty neonates took part in the study where depth information was acquired from various camera locations above and to the side of each neonate. Depth data were processed to provide features indicating changes corresponding to the activity of the neonate and then input into a Random Forest model which was trained and tested using a leave-one-out cross validation paradigm.
Results
Applying the thresholds found in training the Random Forest model during testing with leave-one-out cross validation, the mean (standard deviation) of the sensitivity and specificity of the optimal points and the corresponding area under the receiver operator curve (ROC-AUC) were 92.0% (8.8%), 93.2% (11.1%) and 97.7% (2.5%) respectively. The activity identified by the model also appeared to match well with noisy segments on the corresponding respiratory flow signal.
Conclusions
The results reported here indicate the viability of continuous non-contact monitoring of neonatal activity using a depth sensing camera system.
Journal Article
Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera
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.
Journal Article
Respiratory modulations in the photoplethysmogram (DPOP) as a measure of respiratory effort
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.
Journal Article
Developing an algorithm for pulse oximetry derived respiratory rate (RRoxi): a healthy volunteer study
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 CO
2
reference rate (
).
Results
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
, 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
2
= 0.93).
Conclusions
These data indicate that RR
oxi
represents a viable technology for the measurement of respiratory rate of healthy individuals.
Journal Article
Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge
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.
Journal Article