Pre-processing techniques for improved detection of vocalization sounds in a neonatal intensive care unit

The sounds occurring in the noisy acoustical environment of a Neonatal Intensive Care Unit (NICU) are thought to affect the growth and neurodevelopment of preterm infants. Automatic sound detection in a NICU is a novel and challenging problem, and it is an essential step in the investigation of how preterm infants react to auditory stimuli of the NICU environment. In this paper, we present our work on an automatic system for detection of vocalization sounds, which are extensively present in NICUs. The proposed system reduces the presence of irrelevant sounds prior to detection. Several pre-processing techniques are compared, which are based on either spectral subtraction or non-negative matrix factorization, or a combination of both. The vocalization sounds are detected from the enhanced audio signal using either generative or discriminative classification models. An audio database acquired in a real-world NICU environment is used to assess the performance of the detection system in terms of frame-level missing and false alarm rates. The inclusion of the enhancement pre-processing step leads to up to 17.54% relative improvement over the baseline.Peer ReviewedPostprint (published version

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

Acoustic study of a neonatal intensive care unit: preliminary results

The acoustic environment of a typical neonatal intensive care unit is very rich and may contain a large number of di erent sounds, which reflect the activities taking place in it. There exists a medical concern about the e ect of that acoustical environment on a preterm, since loud or particular sounds may be harmful for the preterm's further neurological development. In the reported work, an audio database of ten acoustic scenarios was recorded using two microphones, one inside and the other outside the incubator. Preliminary results of the acoustical analysis of sounds are presented along with the experiments on automatic detection of the presence of vocalizations.Peer Reviewe

Acoustic study of a neonatal intensive care unit: preliminary results

The acoustic environment of a typical neonatal intensive care unit is very rich and may contain a large number of di erent sounds, which reflect the activities taking place in it. There exists a medical concern about the e ect of that acoustical environment on a preterm, since loud or particular sounds may be harmful for the preterm's further neurological development. In the reported work, an audio database of ten acoustic scenarios was recorded using two microphones, one inside and the other outside the incubator. Preliminary results of the acoustical analysis of sounds are presented along with the experiments on automatic detection of the presence of vocalizations.Peer ReviewedPostprint (published version

Implementing palliative care, based on family-centered care, in a highly complex neonatal unit

Objective: To describe the causes and circumstances of neonatal mortality and determine whether the implementation of a palliative care protocol has improved the quality of end-of-life care. Methods: A retrospective observational study including all patient mortalities between January 2009 and December 2019. Cause of death and characteristics of support during the dying process were collected. Two periods, before and after the implementation of a palliative care protocol, were compared. Results: There were 344 deaths. Congenital malformations were the most frequent cause of death (45.6 %). Most patients died after the transition to palliative care (74.4 %). The most frequently cited criteria for initiating transition of care was poor neurocognitive prognosis (47.2 %). Parents accompanied their children in the dying process in 72 % of cases. Twenty-three percent of patients died outside the Neonatal Intensive Care Unit after being transferred to a private room to enhance family intimacy. After the addition of the palliative care protocol, statistically significant differences were observed in the support and patient experience during the dying process. Conclusions: The most frequent causes of death were severe congenital malformations. Most patients died accompanied by their parents after the transition to palliative care. The implementation of a palliative care protocol helped to improve the family-centered end-of-life care

Acoustic study of a neonatal intensive care unit: preliminary results

The acoustic environment of a typical neonatal intensive care unit is very rich and may contain a large number of di erent sounds, which reflect the activities taking place in it. There exists a medical concern about the e ect of that acoustical environment on a preterm, since loud or particular sounds may be harmful for the preterm's further neurological development. In the reported work, an audio database of ten acoustic scenarios was recorded using two microphones, one inside and the other outside the incubator. Preliminary results of the acoustical analysis of sounds are presented along with the experiments on automatic detection of the presence of vocalizations.Peer Reviewe

Comparación ambiental en dos salas de cuidados intensivos neonatales de tercer nivel

Les Unitats de Cures Intensives Neonatals (UCIN) de tercer nivell solen estar afectades d'elevada contaminació acústica. L'objectiu era quantificar mitjançant un estudi observacional la intensitat de soroll en dues sales UCIN, sales A i B, destinades a nounats a terme i a prematurs, respectivament. Les mesures de soroll van efectuar-se simultàniament a les sales mitjançant sensors sense fils durant 24 hores en 4 dies. Les fluctuacions de soroll continu van ser inferiors a 5dB produint-se uns màxims de soroll transitori a la nit i aconseguint-se valors més elevats en sala A. El soroll a les sales sobrepassa els límits internacionalment recomanats.Las Unidades de Cuidados Intensivos Neonatales (UCIN) de tercer nivel suelen estar afectadas de elevada contaminación acústica. El objetivo fue cuantificar mediante un estudio observacional, la intensidad de ruido en dos salas UCIN, salas A y B, destinadas a neonatos a término y a prematuros, respectivamente. Las medidas de ruido se efectuaron simultáneamente en ambas salas mediante sensores inalámbricos durante 24h en 4 días. Las fluctuaciones de ruido continuo fueron inferiores a 5dB produciéndose máximos de ruido transitorio por la noche y alcanzándose valores más elevados en sala A. El ruido en ambas salas sobrepasó los límites internacionalmente recomendados

Comparación ambiental en dos salas de cuidados intensivos neonatales de tercer nivel

Les Unitats de Cures Intensives Neonatals (UCIN) de tercer nivell solen estar afectades d'elevada contaminació acústica. L'objectiu era quantificar mitjançant un estudi observacional la intensitat de soroll en dues sales UCIN, sales A i B, destinades a nounats a terme i a prematurs, respectivament. Les mesures de soroll van efectuar-se simultàniament a les sales mitjançant sensors sense fils durant 24 hores en 4 dies. Les fluctuacions de soroll continu van ser inferiors a 5dB produint-se uns màxims de soroll transitori a la nit i aconseguint-se valors més elevats en sala A. El soroll a les sales sobrepassa els límits internacionalment recomanats.Las Unidades de Cuidados Intensivos Neonatales (UCIN) de tercer nivel suelen estar afectadas de elevada contaminación acústica. El objetivo fue cuantificar mediante un estudio observacional, la intensidad de ruido en dos salas UCIN, salas A y B, destinadas a neonatos a término y a prematuros, respectivamente. Las medidas de ruido se efectuaron simultáneamente en ambas salas mediante sensores inalámbricos durante 24h en 4 días. Las fluctuaciones de ruido continuo fueron inferiores a 5dB produciéndose máximos de ruido transitorio por la noche y alcanzándose valores más elevados en sala A. El ruido en ambas salas sobrepasó los límites internacionalmente recomendados

Pre-processing techniques for improved detection of vocalization sounds in a neonatal intensive care unit

The sounds occurring in the noisy acoustical environment of a Neonatal Intensive Care Unit (NICU) are thought to affect the growth and neurodevelopment of preterm infants. Automatic sound detection in a NICU is a novel and challenging problem, and it is an essential step in the investigation of how preterm infants react to auditory stimuli of the NICU environment. In this paper, we present our work on an automatic system for detection of vocalization sounds, which are extensively present in NICUs. The proposed system reduces the presence of irrelevant sounds prior to detection. Several pre-processing techniques are compared, which are based on either spectral subtraction or non-negative matrix factorization, or a combination of both. The vocalization sounds are detected from the enhanced audio signal using either generative or discriminative classification models. An audio database acquired in a real-world NICU environment is used to assess the performance of the detection system in terms of frame-level missing and false alarm rates. The inclusion of the enhancement pre-processing step leads to up to 17.54% relative improvement over the baseline.Peer Reviewe

On the acoustic environment of a neonatal intensive care unit: initial description, and detection of equipment alarms

The acoustic environment of a typical neonatal intensive care unit (NICU) is very rich and may contain a large number of different sounds, which come either from the equipment or from the human activities taking place in it. There exists a medical concern about the effect of that acoustical environment on preterm infants, since loud sounds or particular sounds may be harmful for their further neurological development. In this work, first of all, an initial description of the acoustic characteristics of the NICU has been carried out using a set of diverse recordings produced with microphones placed both inside and outside an incubator. Then, the work has focused on detection of the most relevant types of sounds. In this paper, after describing the recorded database and the acoustic environment, preliminary experiments for detection of the acoustic alarms of devices are reported. The proposed detection system is based on Deep Belief Networks (DBN). The experimental results show that the DBN-based system is able to achieve better results than a baseline GMM-based system.Peer Reviewe