This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative.Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks - Volume 188

The aim of this small-scale study was to measure, analyse and compare levels of acoustic noise, in a nine-bedded general intensive care unit (ICU). Measurements were undertaken using the Norsonic 116 sound level meter recording noise levels in the internationally agreed ‘A’ weighted scale. Noise level data were obtained and recorded at 5 min over 3 consecutive days. Results of noise level analysis indicated that mean noise levels within this clinical area was 56·42 dB(A), with acute spikes reaching 80 dB(A). The quietest noise level attained was that of 50 dB(A) during sporadic intervals throughout the 24-h period. Parametric testing using analysis of variance found a positive relationship (p ≤ 0·001) between the nursing shifts and the day of the week. However, Scheffe multiple range testing showed significant differences between the morning shift, and the afternoon and night shifts combined (p ≤ 0·05). There was no statistical difference between the afternoon and night shifts (p ≥ 0·05). While the results of this study may seem self-evident in many respects, what it has highlighted is that the problem of excessive noise exposure within the ICU continues to go unabated. More concerning is that the prolonged effects of excessive noise exposure on patients and staff alike can have deleterious effect on the health and well-being of these individuals.

Noise levels in a general intensive care unit : a descriptive study.

Aim:

The aim of this survey was to assess registered nurse’s perceptions of alarm setting and management in an Australian Regional Critical Care Unit.


Background:

The setting and management of alarms within the critical care environment is one of the key responsibilities of the nurse in this area. However, with up to 99% of alarms potentially being false-positives it is easy for the nurse to become desensitised or fatigued by incessant alarms; in some cases up to 400 per patient per day. Inadvertently ignoring, silencing or disabling alarms can have deleterious implications for the patient and nurse.


Method:

A total population sample of 48 nursing staff from a 13 bedded ICU/HDU/CCU within regional Australia were asked to participate. A 10 item open-ended and multiple choice questionnaire was distributed to determine their perceptions and attitudes of alarm setting and management within this clinical area.


Results:

Two key themes were identified from the open-ended questions: attitudes towards inappropriate alarm settings and annoyance at delayed responses to alarms. A significant number of respondents (93%) agreed that alarm fatigue can result in alarm desensitisation and the disabling of alarms, whilst 81% suggested the key factors are those associated with false-positive alarms and inappropriately set alarms.

Alarm setting for the critically ill patient : A descriptive pilot survey of nurses’ perceptions of current practice in an Australian Regional Critical Care Unit

Provided are techniques for detecting quality of physiologic data using contact pressure data for alarm generation. First data is received that comprises physiologic data and an alarm condition. Second data is received from pressure-sensitive sensors, wherein the second data comprises contact pressure data. In response to determining that the second data indicates movement of an entity, a delay is created for generation of an alarm for the alarm condition. In response to determining that the delay has expired, it is determined whether the first data has changed to indicate that the alarm condition is resolved. In response to determining that the first data has changed to indicate that the alarm condition is resolved, the alarm condition is cleared. In response to determining that the first data has not changed to indicate that the alarm condition is resolved, the alarm is generated.

Detecting quality of physiologic data using contact pressure data for alarm generation

The median filter is a much used and sometimes misunderstood tool available to image processing specialists. It should now be clear to readers that the median is not an alternative filter to those described in this text. It is simply a special case, one of many options that might arise from the design techniques should it happen to be the optimum for that example.

The general filter described above is a rank selection filter in that the output value of the filter always corresponds to one of the inputs. The filter is unable to average or interpolate and this means that it does not produce simple blurring. However, filters with larger windows can give streaking effects in images. This will be addressed later in the chapter.

The Median Filter and Its Variants

Today’s intensive care units monitor patients through the use of various medical devices, which generate a high ratio of false positive alarms due to a low alarm speciﬁcity. The false alarms have r ...

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Derya Kilinc

Papers

Implementing an Intelligent Alarm System in Intensive Care Units