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

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%Peer ReviewedPostprint (published version

A neural network approach for automatic detection of acoustic alarms

Acoustic alarms generated by biomedical equipment are relevant sounds in the noisy Neonatal Intensive Care Unit (NICU) environment both because of their high frequency of occurrence and their possible negative effects on the neurodevelopment of preterm newborns. This work addresses the detection of specific alarms in that difficult environment by using neural network structures. Specifically, both generic and class-specific input models are proposed. The first one does not take advantage of any specific knowledge about alarm classes, while the second one exploits the information about the alarm-specific frequency sub-bands. Two types of partially connected layers were designed to deal with the input information in frequency and in time and reduce the network complexity. The time context was also considered by performing experiments with long short-term memory networks. The database used in this work was acquired in a real-world NICU environment. The reported results show an improvemen t of more than 9% in absolute value for the generic input model and more than 12% for the class-specific input model, when both consider time information using the proposed partially connected layer.Peer ReviewedPostprint (published version

A neural network approach for automatic detection of acoustic alarms

Acoustic alarms generated by biomedical equipment are relevant sounds in the noisy Neonatal Intensive Care Unit (NICU) environment both because of their high frequency of occurrence and their possible negative effects on the neurodevelopment of preterm newborns. This work addresses the detection of specific alarms in that difficult environment by using neural network structures. Specifically, both generic and class-specific input models are proposed. The first one does not take advantage of any specific knowledge about alarm classes, while the second one exploits the information about the alarm-specific frequency sub-bands. Two types of partially connected layers were designed to deal with the input information in frequency and in time and reduce the network complexity. The time context was also considered by performing experiments with long short-term memory networks. The database used in this work was acquired in a real-world NICU environment. The reported results show an improvemen t of more than 9% in absolute value for the generic input model and more than 12% for the class-specific input model, when both consider time information using the proposed partially connected layer.Peer Reviewe