Selvaganesan P, Dauterman M, Mahajan A, Krishna J (2019) Signal Processing Technique for Identifying Pacifier Artifacts in Pediatric Sleep Lab Airflow Data. Int J Pediatr Res 5:055. doi. org/10.23937/2469-5769/1510055


© 2019 Selvaganesan P, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

RESEARCH ARTICLE | OPEN ACCESSDOI: 10.23937/2469-5769/1510055

Signal Processing Technique for Identifying Pacifier Artifacts in

Padmini Selvaganesan1*, Michala Dauterman1, Ajay Mahajan2 and Jyoti Krishna3

1Department of Biomedical Engineering, The University of Akron, Ohio, USA

2Department of Mechanical and Biomedical Engineering, The University of Akron, Ohio, USA

3Pediatric Sleep Medicine, Akron Children's Hospital, Ohio, USA


For diagnosing sleep apnea, patients are required to stay overnight in a sleep lab, and various physiological signals are recorded using different sensors. The data collected during the study is often prone to artifacts due to various reasons and one such artifact in younger patients is due to the use of pacifiers which corrupts the signal from the sensors. One of the sensor signals which is corrupted frequently is the airflow signal. This airflow signal is obtained using a thermistor that is placed just below the nostrils. Thermistor readings are used to determine the airflow and breathing pattern in the patients based on the difference in temperature readings of the air that is drawn in and then breathed out. The objective of this study is to develop a wavelet based signal processing technique to identify and remove such artifacts from the thermistor data. Wavelet technique is first developed and tested on a simulated waveform to remove artifacts, and then validated on the actual waveform obtained from a patient. The technique shows satisfactory output in removal of artifacts and in reconstruction of the actual signal. It must be noted here that the removal of the artifact may not provide information on the occurrence of a sleep apnea episode by that sensor, but directs more attention to other sensors to see if there was an episode at that time. In addition, the identification and removal of the artifact is the first stage towards an automatic software-based scoring system in the future.