A knowledge-based approach to automatic detection of equipment alarm sounds in a neonatal intensive care unit environment
| Autor: | Ganna Raboshchuk, Alex Peiró Lilja, Climent Nadeu, Ana Riverola de Veciana, Munevver Kokuer, Blanca Muñoz Mahamud, Peter Jancovic |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Sinusoid detection
lcsh:Medical technology Neonatal intensive care unit Alarm detection Monitoring Computer science Remote patient monitoring Speech recognition Feature extraction Biomedical Engineering 02 engineering and technology Enginyeria acústica lcsh:Computer applications to medicine. Medical informatics Pediatrics Article non-negative matrix factorization Medicina intensiva neonatal 03 medical and health sciences ALARM 0302 clinical medicine 030225 pediatrics alarm detection 0202 electrical engineering electronic engineering information engineering sinusoid detection Class (computer programming) Acoustical engineering Física::Acústica [Àrees temàtiques de la UPC] Artificial neural network Acoustic event detection 020206 networking & telecommunications Detectors Non-negative matrix factorization General Medicine Acoustics neural networks neonatal intensive care unit Time–frequency analysis Time-frequency analysis Biomedical equipment lcsh:R855-855.5 Key (cryptography) lcsh:R858-859.7 Neural networks |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) IEEE Journal of Translational Engineering in Health and Medicine Recercat. Dipósit de la Recerca de Catalunya instname IEEE Journal of Translational Engineering in Health and Medicine, Vol 6, Pp 1-10 (2018) |
| Popis: | 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%. A system for automatic detection of alarm sounds that uses the knowledge about their frequency and time structure. |
| Databáze: | OpenAIRE |
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