A knowledge-based approach to automatic detection of equipment alarm sounds in a neonatal intensive care unit environment

Research output: Contribution to journalArticlepeer-review

Authors

  • Ganna Raboshchuk
  • Climent Nadeu
  • Alex Peiro Lilja
  • Blanca Munoz Mahamud
  • Ana Riverola de Veciana

Colleges, School and Institutes

External organisations

  • Universitat Politecnica de Catalunya
  • Hospital Sant Joan de Déu, Barcelona

Abstract

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modelling and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%.

Details

Original languageEnglish
JournalIEEE Journal of Translational Engineering in Health and Medicine
Volume6
Early online date22 Dec 2017
Publication statusE-pub ahead of print - 22 Dec 2017