A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment

IEEE Journal of Translational Engineering in Health and Medicine

Munevver Kokuer¹  Ganna Raboshchuk²  Peter Jancovic²  Blanca Munoz Mahamud²  Ana Riverola De Veciana³  Alex Peiro Lilja³  Climent Nadeu³ 

[1] Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, U.K.;Department of Signal Theory and Communications, TALP Research Center, Universitat Polit&x00E8;cnica de Catalunya, Barcelona, Spain;

关键词: Acoustic event detection;    alarm detection;    neonatal intensive care unit;    sinusoid detection;    non-negative matrix factorization;    neural networks;   

DOI  :  10.1109/JTEHM.2017.2781224

来源: DOAJ

【 摘 要 】

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, modeling, 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%.

【 授权许可】

Unknown