P.C.W. Beatty

Improved quantitative dynamic regional oxygen-enhanced pulmonary imaging using image registration

Oxygen‐enhanced MR imaging has been demonstrated in a number of recent studies as a potential method to visualize regional ventilation in the lung. A quantitative pixel‐by‐pixel analysis is hampered by motion and volume change due to breathing. In this study, image registration via active shape modeling is shown to produce significant improvements in the regional analysis of both static and dynamic oxygen‐enhanced pulmonary MRI for five normal volunteers. The method enables the calculation of regional change in relaxation rate between breathing air and 100% oxygen, which is proportional to the change in regional oxygen concentration, and regional oxygen wash‐in and wash‐out time constants. Registration‐corrected mapping of these parameters is likely to provide improved information in the regional assessment of a range of lung diseases. Magn Reson Med 54:464–469, 2005.

Identifying violation-provoking conditions in a healthcare setting

Procedural violations (intentional deviations from established protocols) are prone to occur in many occupational settings, with a potentially detrimental effect on quality or safety. They are thought to result from organisational practices and the social characteristics of rule-related behaviour. This study makes use of qualitative methods to investigate the nature and causes of violations in anaesthetic practice. Twenty-three consultant anaesthetists took part in the study, which involved naturalistic observations and semi-structured interviews. Several factors influencing anaesthetic violations were identified. These include the nature of the rule, the anaesthetist (both as an individual and as a professional group) and the situation. Implications for the understanding and management of human reliability issues within an organisation are discussed. This study provides an insight into procedural violations, which pose a threat to organisational safety but are distinct from human errors. The study also demonstrates the value of qualitative methods in ergonomics research. It is of relevance to researchers and practitioners interested in human reliability and error, especially in healthcare.

Measuring the performance of audible alarms for anaesthesia

The ergonomic performance of an integrated set of 17 audible alarm sounds, divided into low, medium and high priority classes has been undertaken. The sounds were tested for their ease of learning/recall, and how closely their intrinsic perceived urgency matched to a clinical assessment of urgency. The tests were computer-administered and performed on 21 volunteers aged from 18 to 52, in two sessions a few days apart. Session 1 taught the meanings of the alarm sounds and session 2 measured the performance of the sounds. The mean correct identification rate for the sounds was 48.4% (range 10.3-90.0%) with 97.5% of misidentifications within sound priority class. The urgency correlation was statistically significant (r=0.85, p<0.001) with all priority classes included but within priority class correlations were not statistically significant. Poor within priority class performances were ascribable to a priori aspects of the design of the sound system.

Automatic artifact identification in anaesthesia patient record keeping: a comparison of techniques

The anaesthetic chart is an important medico-legal document, which needs to accurately record a wide range of different types of data for reference purposes. A number of computer systems have been developed to record the data directly from the monitoring equipment to produce the chart automatically. Unfortunately, systems to date record artifactual data as normal, limiting the usefulness of such systems. This paper reports a comparison of possible techniques for automatically identifying artifacts. The study used moving mean, moving median and Kalman filters as well as ARIMA time series models. Results on unseen data showed that the Kalman filter (area under the ROC curve 0.86, false positive prediction rate 0.31, positive predictive value 0.05) was the best single method. Better results were obtained by combining a Kalman filter with a seven point moving mid-centred median filter (area under the ROC curve 0.87, false positive prediction rate 0.14, positive predictive value 0.09) or an ARIMA 0-1-2 model with a seven point moving mid-centred median filter (area under the ROC curve 0.87, false positive prediction rate 0.14, positive predictive value 0.10). Only one method that could be used on real-time data outperformed the single Kalman filter which was a Kalman filter combined with a seven point moving median filter predicting the next point in the data stream (area under the ROC curve 0.86, false positive prediction rate 0.23, positive predictive value 0.06).

FRESH GAS REQUIREMENTS OF AN ENCLOSED AFFERENT RESERVOIR BREATHING SYSTEM IN ANAESTHETIZED, SPONTANEOUSLY VENTILATING CHILDREN

We have determined the minimum fresh gas flow rate (VF) for use with the Ohmeda enclosed afferent reservoir breathing system (EAR) in 10 anaesthetized children breathing spontaneously. First, we determined the VF required to prevent rebreathing as detected by increased total ventilation (VE) and end-tidal carbon dioxide partial pressure. Second, we used a mathematical model to calculate the degree of rebreathing occurring at each VF. A VF equal to the predicted alveolar ventilation was sufficient to prevent clinically detectable rebreathing in all patients. From the model, no rebreathing occurred when VF/VE was 0.78 or more. We have shown previously that the EAR functions efficiently during controlled ventilation with a VF = 0.6 x weight 0.5. As this VF is slightly greater than the predicted alveolar ventilation, we suggest that the EAR may be used with a VF = 0.6 x weight 0.5 regardless of the mode of ventilation.

On-line novelty detection for artefact identification in automatic anaesthesia record keeping

We report the design of a kernel-based on-line novelty detector (ADDaM - Automatic Dynamic Data Mapper) and its use in the detection of artefacts in an automatic anaesthesia record keeper (AARK).ADDaM produces a partitioned history of any ordered data stream and constructs a probability distribution function (PDF) from that history using Gaussian kernels. Two forms of PDF are possible: a static PDF where the prior probability of each kernel is determined by the number of observations it represents and a temporal PDF where more recent observations have a higher prior probability. Testing against the current PDF assesses the novelty of the next point entering the stream. The performance of this method for artefact detection in heart rate data was compared to Kalman, ARIMA and moving mean filters using receiver operator characteristic (ROC) curves. Performance was measured using the area under the curves (AUC), and the false positive rate (FPR) and positive predictive value (PPV) calculated at the optimal cost-point on the curves. The results obtained were: ADDaM (Static PDF) AUC 0.92, FPR 0.12, PPV 0.12 and ADDaM (Temporal PDF) AUC 0.97, FPR 0.12, PPV 0.15. Both ADDaM-based methods out performed all other on-line methods tested.

Shape-only identification of breathing system failure

Breathing system failure accounts for approximately 7% of all critical incidents during anaesthesia. Current smart alarm solutions to this problem tend to have been developed for a specific manufacturers equipment and use relatively expensive sensors. What is needed is an intelligent alarm capable of working in all systems from easily available signals. This paper reports the results of research into a shape-only alarm system aimed at providing such a system. It uses pressure, flow and capnograph waveforms gathered at the patient connector. Single breath segments from these waveforms were extracted and roughly synchronised and normalised to the same dynamic range and time base, i.e. the waves were plotted on a 1/spl times/1 x/y graph regardless of the original amplitude or breathing rate, in a simple similarity transformation. A neural network classifier was then trained to recognise the failure modes from the shape of these segmented waveforms after further pre-processing including a genetic algorithm search for relevant features within the waveforms. The system has been tested using an Enclosed Afferent Reservoir (EAR) and a Bain breathing system during simulated spontaneous and controlled ventilation. Correct classification rates for failures of 97.6% and 94.3% were obtained for the EAR and the Bain studies respectively in the face of over 100 unseen simulated failures for each breathing system. In both studies only one false positive alarm, i.e. an indication of a failure when no failure was present, was indicated by the alarm, and only one false negative instances were observed.

Estimation of physiological body parameters from smart garment data

A wearable smart garment containing embedded sensors produces live data stream like electrocardiograph, respiratory rate, tidal volume and body temperature. Additional vital body parameters are necessary to assess the physiological state of a person especially for athletes, firefighters, combat personnel etc. This work shows the development of a graphical user interface software application for estimation of parameters like metabolic rate, heart rate, heat stress index, core body temperature, sweat rate and heat strain following standard physiological equations. The smart garment and the software application can be used for remotely monitoring the health status of a person and taking necessary actions when required.

Improved Regional Analysis of Oxygen-Enhanced Lung MR Imaging Using Image Registration

Oxygen enhanced MR imaging of the lung is a promising technique for monitoring a range of pulmonary diseases but regional analysis is hampered by lung motion and volume changes due to breathing. We have developed an image registration method to improve the quantitative regional analysis of both static and dynamic oxygen-enhanced pulmonary MRI. Images were acquired using a HASTE sequence at 1.5T for five normal volunteers alternately breathing air and 100% oxygen. Static images were used to calculate regional changes in relaxation rate between breathing air and oxygen which is directly related to the increase in the dissolved oxygen concentration. Dynamic scans were used to calculate regional maps of oxygen wash-in and wash-out time constants. The method provided significant improvements in the both the static and the dynamic analysis. This may provide improved information in the regional assessment of chronic obstructive lung diseases.