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Respiratory signals monitored in the neonatal intensive care units are usually ignored due to the high prevalence of noise and false alarms (FA). Apneic events are generally therefore indicated by a pulse oximeter alarm reacting to the subsequent desaturation. However, the high FA rate in the photoplethysmogram may desensitize staff, reducing the reaction speed. The main reason for the high FA rates of critical care monitors is the unimodal analysis behaviour. In this work, we propose a multimodal analysis framework to reduce the FA rate in neonatal apnoea monitoring. Information about oxygen saturation, heart rate, respiratory rate and signal quality was extracted from electrocardiogram, impedance pneumogram and photoplethysmographic signals for a total of 20 features in the 5 min interval before a desaturation event. 1616 desaturation events from 27 neonatal admissions were annotated by two independent reviewers as true (physiologically relevant) or false (noise-related). Patients were divided into two independent groups for training and validation, and a support vector machine was trained to classify the events as true or false. The best classification performance was achieved on a combination of 13 features with sensitivity, specificity and accuracy of 100% in the training set, and a sensitivity of 86%, a specificity of 91% and an accuracy of 90% in the validation set.

Original publication

DOI

10.1088/0967-3334/33/9/1503

Type

Journal article

Journal

Physiol Meas

Publication Date

09/2012

Volume

33

Pages

1503 - 1516

Keywords

Apnea, Electrocardiography, False Positive Reactions, Heart Rate, Humans, Infant, Newborn, Intensive Care Units, Neonatal, Oxygen, Photoplethysmography, Quality Control, ROC Curve, Respiratory Rate, Signal Processing, Computer-Assisted, Support Vector Machine