Methods for Measuring and Identifying Sounds in the Intensive Care Unit

Sources of Sound Exposure in Pediatric Critical Care

BACKGROUND

Sound levels in the pediatric intensive care unit (PICU) are often above recommended levels, but few researchers have identified the sound sources contributing to high levels.

OBJECTIVES

To identify sources of PICU sound exposure.

METHODS

This was a secondary analysis of continuous bedside video and dosimeter data (n = 220.7 hours). A reliable coding scheme developed to identify sound sources in the adult ICU was modified for pediatrics. Proportions of sound sources were compared between times of high (≥45 dB) and low (<45 dB) sound, during day (7 AM to 6:59 PM) and night (7 PM to 6:59 AM) shifts, and during sound peaks (≥70 dB).

RESULTS

Overall, family vocalizations (38% of observation time, n = 83.9 hours), clinician vocalizations (32%, n = 70.6 hours), and child nonverbal vocalizations (29.4%, n = 64.9 hours) were the main human sound sources. Media sounds (57.7%, n = 127.3 hours), general activity (40.7%, n = 89.8 hours), and medical equipment (31.3%, n = 69.1 hours) were the main environmental sound sources. Media sounds occurred in more than half of video hours. Child nonverbal (71.6%, n = 10.2 hours) and family vocalizations (63.2%, n = 9 hours) were highly prevalent during sound peaks. General activity (32.1%, n = 33.2 hours), clinician vocalizations (22.5%, n = 23.3 hours), and medical equipment sounds (20.6, n = 21.3 hours) were prevalent during night shifts.

CONCLUSIONS

Clinicians should partner with families to limit nighttime PICU noise pollution. Large-scale studies using this reliable coding scheme are needed to understand the PICU sound environment.

FPGA architecture based on OpenCL for studying the acoustic backscattering by an immersed tube

At a time when the semiconductor industry is facing major difficulties in maintaining sluggish growth, new high-level synthesis tools are repositioning FPGAs as a leading technology for hardware-based algorithm acceleration, in the face of CPUs based clusters. As they stand, however, these tools do not guarantee that a software engineer can use these technologies to their full potential without expertise in the underlying hardware. This particularity can be an obstacle to their democratization. When it comes to acoustic scattering (AS) and its various applications, there is a growing need for autonomous and integrated systems that can operate in real time with high accuracy. This is why we propose our methodology for accelerating algorithms on FPGAs. After presenting a high-level architecture model of this target, we detail various possible optimizations in OpenCL, to finally define a relevant exploration strategy for algorithm acceleration on FPGAs. Applied to various case studies, to characterize and identify an immersed metal tube in the frequency range between 0 and 46.8 kHz. We evaluate our methodology according to three main performance criteria: execution time, resource utilization and energy efficiency. The experimental results show that the proposed methodology is efficient and effective. Indeed, the computation times using the DE1 Soc FPGA and a modern CPU are about 3.5s and 74s respectively. In addition, the absolute error did not exceed 10-5.

A challenge to the evidence behind noise guidelines for UK hospitals

BACKGROUND

Teams assessing hospital noise against international guidelines regularly find that noise exceeds perceived safe levels in clinical settings. The care of sick people may be inherently noisy but recent efforts to tackle the problem propose a wider scope to identify sources and qualities of noise as well as more precision with noise recording.

AIMS

We sought to challenge the scientific evidence cited in the four major documents pertaining to hospital noise in the UK to clarify if evidence of harm from noise included in guidelines is available, contemporary and of high quality.

METHODS

Our team of hearing-health clinicians, acoustic scientists and acoustic engineers have conducted a narrative scoping review focused on critically appraising four of the most cited guidelines against which noise is measured in healthcare settings in the UK.

RESULTS

There is a lack of high-quality evidence for commonly accepted consequences of noise cited in current guidelines.

CONCLUSIONS

The current evidence base for noise guidelines in a healthcare setting is largely based on subjective heterogeneous and inconclusive research. Whilst reduced noise is not disputed as potentially beneficial for patient care, further hypothesis-driven research and interventions assessing the benefits or outcomes of any such intervention should be sought to produce high-quality evidence of relevance on the clinical coalface.

Characterization of sound pressure levels and sound sources in the intensive care unit: a 1 week observational study

Background

Exposure to elevated sound pressure levels within the intensive care unit is known to negatively affect patient and staff health. In the past, interventions to address this problem have been unsuccessful as there is no conclusive evidence on the severity of each sound source and their role on the overall sound pressure levels. Therefore, the goal of the study was to perform a continuous 1 week recording to characterize the sound pressure levels and identify negative sound sources in this setting.

Methods

In this prospective, systematic, and quantitative observational study, the sound pressure levels and sound sources were continuously recorded in a mixed medical-surgical intensive care unit over 1 week. Measurements were conducted using four sound level meters and a human observer present in the room noting all sound sources arising from two beds.

Results

The mean 8 h sound pressure level was significantly higher during the day (52.01 ± 1.75 dBA) and evening (50.92 ± 1.66 dBA) shifts than during the night shift (47.57 ± 2.23; F(2, 19) = 11.80, p < 0.001). No significant difference was found in the maximum and minimum mean 8 h sound pressure levels between the work shifts. However, there was a significant difference between the two beds in the based on location during the day (F(3, 28) = 3.91, p = 0.0189) and evening (F(3, 24) = 5.66, p = 0.00445) shifts. Cleaning of the patient area, admission and discharge activities, and renal interventions (e.g., dialysis) contributed the most to the overall sound pressure levels, with staff talking occurring most frequently.

Conclusion

Our study was able to identify that continuous maintenance of the patient area, patient admission and discharge, and renal interventions were responsible for the greatest contribution to the sound pressure levels. Moreover, while staff talking was not found to significantly contribute to the sound pressure levels, it was found to be the most frequently occurring activity which may indirectly influence patient wellbeing. Overall, identifying these sound sources can have a meaningful impact on patients and staff by identifying targets for future interventions, thus leading to a healthier environment.

Sleep disruption and delirium in critically ill children: Study protocol feasibility

Delirium is a serious complication of pediatric critical illness. Sleep disruption is frequently observed in children with delirium, and circadian rhythm dysregulation is one proposed cause of delirium. Children admitted to the pediatric intensive care unit (PICU) experience multiple environmental exposures with the potential to disrupt sleep. Although researchers have measured PICU light and sound exposure, sleep, and delirium, these variables have not yet been fully explored in a single study. Furthermore, caregiving patterns have not often been included as a component of the PICU environment. Measuring the light and sound exposure, caregiving patterns, and sleep of critically ill children requires continuous PICU bedside data collection. This presents multiple methodological challenges. In this paper, we describe the protocol for an observational pilot study of the PICU environment, sleep, and delirium experienced by a sample of 10 critically ill children 1-4 years of age. We also evaluate and discuss the feasibility (i.e., acceptability, implementation, practicality) of the study protocol. Light and sound exposure were measured with bedside sensors. Caregiving was quantified through video recording. Sleep was measured via actigraphy and confirmed by video recording. Delirium screening with the Cornell Assessment of Pediatric Delirium was conducted twice daily, either in person or via video review. This study provides a refined measurement framework to inform future, large-scale studies and the development of nurse-driven sleep promotion interventions.