A fuzzy system for detecting distorted plethysmogram pulses in neonates and paediatric patients.

Pulse oximetry is a useful, quick, non-invasive and widely used technology for monitoring oxygen saturation (SaO2) for neonates and paediatric patients. However, pulse oximetry is fraught with artefacts, causing false alarms resulting from patient or probe movement. The shape of the plethysmogram is a useful visual indicator for determining the reliability of SaO2 numerical readings. If certain features could be defined that tag valid plethysmogram pulses, then automatic recognition of valid SaO2 values can be attained. We observed that the systolic upstroke time (t1), the diastolic time (t2) and heart rate (HR) extracted from the plethysmogram pulse constitute features which can be used for detecting normal and distorted plethysmogram pulses. Therefore, we developed a knowledge-based system using fuzzy logic for classifying plethysmogram pulses into two categories: valid and artefact. A total of 22,497 pulse waveforms were used to define the system parameters. These were obtained from 13 patients with heart rates ranging between 62 and 209 beats min-1. A further 1420 waveforms obtained from another four patients were used for testing the system, and visually classified into 833 (59%) valid and 587 (41%) distorted segments. The system was able to classify 679 (82%) valid segments and 543 (93%) distorted segments correctly. The calculations of the system's performance showed 82% sensitivity, 86% accuracy and 93% specificity. We, therefore, conclude that the algorithm used in this system can be implemented in its present from for real-time SaO2 monitoring in intensive care for detecting valid and distorted plethysmogram pulses.