Automatic detection of apnoea of prematurity.

The detection of the incidents of apnoea of prematurity (AP) in preterm infants is important in the intensive care unit, but this detection is often based on simple threshold techniques, which suffer from poor specificity. Three methods for the automatic detection of AP were designed, tested and evaluated using approximately 2426 h of continuous recording from 54 neonates (µ = 44 h and σ = 7 h). The first method was based on the cumulative sum of the time series of heart rate (HR), respiratory rate (RR) and oxygen saturation (SpO(2)) along with the sum of their Shannon entropy. The performance of this method gave 94.53% sensitivity, 74.72% specificity and 77.84% accuracy. The second method was based on the correlation between the time series of HR, RR and SpO(2), which were used as inputs to an artificial neural network. This gave 81.85% sensitivity, 75.83% specificity and 76.78% accuracy. The third method utilized the derivative of the three time series and yielded a performance of 100% sensitivity, 96.19% specificity and 96.79% accuracy. Although not optimized to work in real time, the latter method has the potential for forming the basis of a real time system for the detection of incidents of AP.

An intelligent ventilation and oxygenation management system in neonatal intensive care using fuzzy trend template fitting.

This paper describes an open loop feedback intelligent system for neonatal intensive care management. The system provides a tool enabling the user to make the final decision to accept or reject the advice given. The system collects 18 parameters from the bedside monitor and ventilator using a Medical Information Bus (MIB) system. Comparison between the system's recommendations and seven clinical users (three doctors and four nurses) actions was made during monitoring of seven neonates with gestation age of 27-31 weeks for 124.13 h (mu=17.7329, sigma=5.3843 h, range=10.40-23.85 h). The validation process compared the recommendations triggered by the system with the user feedback (agree, disagree, wait). The system made 191 recommendations in total, 33 of which (17%) were for ventilation and 158 (83%) for oxygenation. The clinician agreed with the system ventilation decisions in 30 occasions (91%) and in 148 occasions for the system oxygenation decisions (94%). The overall percentage of the agreement between the system and the clinician was 93%.

Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system.

Despite the fact that pulse oximetry has become an essential technology in respiratory monitoring of neonates and paediatric patients, it is still fraught with artefacts causing false alarms resulting from patient or probe movement. As the shape of the plethysmogram has always been considered as a useful visual indicator for determining the reliability of SaO(2) numerical readings, automation of this observation might benefit health care providers at the bedside. We observed that the systolic upstroke time (t(1)), the diastolic time (t(2)) and heart rate (HR) extracted from the plethysmogram pulse constitute features, which can be used for detecting normal and distorted plethysmogram pulses. We developed a technique for classifying plethysmogram pulses into two categories: valid and artefact via implementations of fuzzy inference systems (FIS), which were tuned using an adaptive-network-based fuzzy inference system (ANFIS) and receiver operating characteristics (ROC) curves analysis. Features extracted from a total of 22,497 pulse waveforms obtained from 13 patients were used to systematically optimise the FIS. A further 2843 waveforms obtained from another eight patients were used for testing the system, and visually classified into 1635 (58%) valid and 1208 (42%) distorted segments. For the optimum system, the area under the ROC curve was 0.92. The system was able to classify 1418 (87%) valid segments and 897 (74%) distorted segments correctly. The calculations of the system's performance showed 87% sensitivity, 81% accuracy and 74% specificity. In comparison with the 95% confidence interval (CI) thresholding method, the fuzzy system showed higher specificity (P=0.008,P<0.01), and no significant difference was found between the two methods in terms of sensitivity (P=0.720,P>0.05) and accuracy (P=0.053,P>0.05). We therefore conclude that the algorithm used in this system has some potential in detecting valid and distorted plethysmogram pulse. However, further evaluation is needed using larger patient groups.