Neue Ergebnisse zur Messung und Analyse der Schallfelder in Intensivstationen. Armin Kohlrausch Philips Research und TU Eindhoven

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Dominick Hines
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1 Neue Ergebnisse zur Messung und Analyse der Schallfelder in Intensivstationen Armin Kohlrausch Philips Research und TU Eindhoven

2 Hospitals are very noisy environments This is supported by numerous studies on hospital acoustics May 05, 2011 Healthcare Technology and News: The Loudest Place on the Block? Your Nearest Hospital 2

3 Many disconnected activities to improve

variations (in ICUs) For staff contribution to stress and burnout decreased short-term memory and mental efficiency decreased ability to aurally

4 Negative effects of noise on patients and staff For patients a reduction in the recuperative properties of sleep, including depth, continuity, and duration cardiovascular arousal decreased wound healing extended hospital stay increased dosages of pain medication lack of day-night variations (in ICUs) For staff contribution to stress and burnout decreased short-term memory and mental efficiency decreased ability to aurally distinguish critical physiological functions such as heart and lung sounds problems in communication, increased voice effort Lärmbekämpfung Nov. 2012, S. 263

5 Questions addressed in our noise monitoring and sound scape analysis What are the dominant sources of noise in an ICU? How often do they occur? How much does each source type contribute to the total noise dose? How long are the restorative periods allowing undisturbed rest for the patient?

6 Our approach Audio recording instead of the logging of noise levels Off-line post-processing to extract acoustic parameters Off-line manual annotation of the audio to investigate the noise sources

5m) Parallel sound-field microphone recording to reproduce

7 Recording in an intensive care unit ~3-day recording with a calibrated microphone (B&K) placed above the patient bed (~ 1.5m) Parallel sound-field microphone recording to reproduce spatial sound field ICU at Jeroen Bosch Hospital (NL) January 2011

8 Results - Overview L AF (A-weighted SPL in fast mode) L Aeq,8h (A-weighted energy-equivalent SPL) 8 8

9 28 Labels used for the annotation 9 No. Acronym Noise source Noise Category Remarks 1 PSP Patient-involved SPeech Patient-involved verbal (speech) and Patient (v) 2 PNVS Patient Non-Verbal Sound non-verbal (e.g. coughing) sounds 3 USP Unavoidable SPeech 4 ASP Avoidable SPeech 5 UISP UnIntelligible SPeech 6 ONVS Others Non-Verbal Sound 7 NA Nearby Alarm 8 RA Remote Alarm 9 RE Respiratory Equipment noise 10 CA Compressed Air 11 SD noise from Suctioning Device 12 ME noise from Medical Equipments 13 FS FootSteps 14 GM General Movement 15 WS Water Sound 16 PR Plastic Ripping 17 PN Paper Noise 18 BC Bed Clinking / bed rail 19 DRW DRaWer 20 MC Mouse Clicking 21 MOS Metalic Object Scrapping 22 NMOS Non-Metalic Object Scrapping 23 RB Rubbish Bin 24 DOB Dropped OBject 25 CBD CupBoard Door opening / closing 26 UT Unidentified Thump 27 RTR Telephone Ringing Staff (v) Alarms M. Devices Staff (a) 28 OUS Other Unidentifiable Sound Unidentified Conversations between staff; nonverbal sounds from staff Alarming sounds of medical devices Operational sounds of medical devices Noise generated by staff activities, e.g. footsteps, object-dropping, etc.

10 28 Labels used for the annotation 10 No. Acronym Noise source Noise Category Remarks 1 PSP Patient-involved SPeech Patient-involved verbal (speech) and Patient (v) 2 PNVS Patient Non-Verbal Sound non-verbal (e.g. coughing) sounds 3 USP Unavoidable SPeech 4 ASP Avoidable SPeech 5 UISP UnIntelligible SPeech 6 ONVS Others Non-Verbal Sound 7 NA Nearby Alarm 8 RA Remote Alarm 9 RE Respiratory Equipment noise 10 CA Compressed Air 11 SD noise from Suctioning Device 12 ME noise from Medical Equipments 13 FS FootSteps 14 GM General Movement 15 WS Water Sound 16 PR Plastic Ripping 17 PN Paper Noise 18 BC Bed Clinking / bed rail 19 DRW DRaWer 20 MC Mouse Clicking 21 MOS Metalic Object Scrapping 22 NMOS Non-Metalic Object Scrapping 23 RB Rubbish Bin 24 DOB Dropped OBject 25 CBD CupBoard Door opening / closing 26 UT Unidentified Thump 27 RTR Telephone Ringing Staff (v) Alarms M. Devices Staff (a) 28 OUS Other Unidentifiable Sound Unidentified Conversations between staff; nonverbal sounds from staff Alarming sounds of medical devices Operational sounds of medical devices Noise generated by staff activities, e.g. footsteps, object-dropping, etc.

11 Results - Occurrence 11 Excluding Patient (v), ~20 events/min during the day time ~15 events/min during the night time

12 12 Results - Accumulated duration

13 13 Results - Acoustic energy estimated per noise category

14 14 Results - Acoustic energy estimated per noise category

15 Conclusions Due to the short distance to the microphone, the patientinvolved noise was the strongest source. Excluding Patient (v), staff-generated noise was dominant over alarms or operational noise from life-supporting devices. However, other sensory/non-sensory aspects (e.g. tonality, familiarity, etc.) are also important in estimating the impact of noise on patients. Changes in staff behaviour or work-routine may improve the ICU acoustic environment, possibly assisted by technical solutions. 15

16 16 More detailed reference with more data and background

17 A nice extra: What you can see in long-term recordings In a follow-up project, long-term sound recording in eight rooms of one ICU department St Elisabeth hospital in Tilburg Long-term aggregation of sound energy measurement reveals time-aligned activities in the department Total dataset: 660 days of sound recording 17

18 18 Rich data set from the acoustic measurement

19 Rich data set from the acoustic measurement Check-up (doctor s round) 19

20 Rich data set from the acoustic measurement Check-ups 20

21 Rich data set from the acoustic measurement Check-up + X-ray 21

22 Rich data set from the acoustic measurement Shift change 22

23 Rich data set from the acoustic measurement Morning routine begins 23

24 Rich data set from the acoustic measurement Staff coffee break 24

25 Rich data set from the acoustic measurement Lunch/rest time 25

26 Rich data set from the acoustic measurement Check-up (patients turned) 26

27 Rich data set from the acoustic measurement Visiting period starts 27

28 Rich data set from the acoustic measurement Shift change 30

29 Rich data set from the acoustic measurement Dinner starts 29

30 Rich data set from the acoustic measurement Visiting period starts 32

31 Rich data set from the acoustic measurement Check-up 33

32 Rich data set from the acoustic measurement Shift-change 34

33 Questions? Suggestions? Ideas for further directions? 33

Quantifying temporal ventriloquism in audio-visual synchrony perception

Quantifying temporal ventriloquism in audio-visual synchrony perception Irene A. Kuling 1, James F. Juola 2,3 & Armin Kohlrausch 2,4 1 VU University Amsterdam 2 Eindhoven University of Technology 3 University