title: Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering and Medical Physics, Centre for Science and Technology in Medicine, University of Keele, Thornburrow Drive, Hartshill, Staffordshire ST4 7QB, Stoke On Trent, UK. s.belal@keele.ac.uk
authors.forename: Suliman Yousef
authors.initials: SY
authors.lastname: Belal
authors.forename: Azzam Fouad George
authors.initials: AF
authors.lastname: Taktak
authors.forename: Andrew John
authors.initials: AJ
authors.lastname: Nevill
authors.forename: Stephen Andrew
authors.initials: SA
authors.lastname: Spencer
authors.forename: David
authors.initials: D
authors.lastname: Roden
authors.forename: Sharon
authors.initials: S
authors.lastname: Bevan
mesh_terms: D000293:Adolescent; D000465:Algorithms; D016477:Artifacts; D001331:Automation; D002648:Child; D002675:Child, Preschool; D005189:False Positive Reactions; D005260:Female; D017143:Fuzzy Logic; D006339:Heart Rate; D006454:Hemoglobins / Q000032:analysis; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008171:Lung Diseases / Q000175:diagnosis*; D008297:Male; D016571:Neural Networks, Computer; D010092:Oximetry / Q000379:methods*; D010100:Oxygen / Q000032:analysis; D010991:Plethysmography / Q000379:methods*; D019095:Point-of-Care Systems; D012680:Sensitivity and Specificity
chemical_list: D006454:Hemoglobins; D010100:Oxygen