Reliable and Real-time Wireless Sensor Networks: Protocols and Medical Applications

Transcription

1 Reliable and Real-time Wireless Sensor Networks: Protocols and Medical Applications Octav Chipara University of Iowa 1

2 Detecting clinical deterioration at low cost Clinical deterioration in hospitalized patients 4-17% suffer adverse events (e.g., cardiac or respiratory arrests) up to 70% of such events could be prevented. Early detection of clinical deterioration clinical deterioration is often preceded by changes in vitals Real-time patient monitoring is required wired patient monitoring inconvenient wireless telemetry systems too expensive for wide adoption most general hospital units collect vitals manually and infrequently 2

3 Detecting clinical deterioration at low cost Clinical deterioration in hospitalized patients 4-17% suffer adverse events (e.g., cardiac or respiratory arrests) up to 70% of such events could be prevented. Early detection of clinical deterioration clinical deterioration is often preceded by changes in vitals Real-time patient monitoring is required wired patient monitoring inconvenient wireless telemetry systems too expensive for wide adoption most general hospital units collect vitals manually and infrequently Goal: reliable and real-.me wireless clinical monitoring for general hospital units 2

4 Wireless sensor networks vs. Wi-Fi Commercial telemetry systems (Phillips, Cisco, GE): Wi-Fi single-hop wireless, wired backbone adoption limited to specialized hospital units Benefits of wireless sensor networks more energy efficient than Wi-Fi at low data rate common vital signs have low data rate nurses are too busy to change batteries! low deployment cost eliminate wired infrastructure mesh networks ease of adoption (e.g., field hospitals, rural areas) reliability of wireless sensor networks - an open question! 3

5 Related work Wireless sensor networks for medical applications Assisted living: ALARM-NET Disaster recovery: AID-N, CodeBlue, WIISARD Emergency room: MEDISN, SMART Motion analysis: Mercury Numerous systems, limited results from clinical deployments MEDISN, John Hopkins Code Blue, Harvard Code Blue, Harvard 4

6 Related work Wireless sensor networks for medical applications Assisted living: ALARM-NET Disaster recovery: AID-N, CodeBlue, WIISARD Emergency room: MEDISN, SMART Motion analysis: Mercury Numerous systems, limited results from clinical deployments First holistic reliability study performed in a hospital unit using sensor networks MEDISN, John Hopkins Code Blue, Harvard Code Blue, Harvard 4

7 System architecture Base-station laptop that will store medical data Relay nodes redundant deployment: coverage fault-tolerance plugged into wall outlets Patient nodes pulse oximeter + microcontroller + radio same pulse-oximeter as used in hospitals battery powered Relay node Patient node 5

8 Reliable network architecture Initial solution: use CTP [1] all nodes in the network Insight: isolate the impact of mobility Solution: two-tier architecture for end-to-end data delivery Dynamic Relay Association Protocol (DRAP): Patient 1st relay DRAP used on mobile nodes dynamically associate relays single-hop protocol handles patient mobility simplify power management in patient nodes (send only) Stationary relay network: 1st relay base station CTP used on fixed nodes reuse well-tested CTP wall-plugged no need to worry about energy [1] O. Gnawali et. al., Collection Tree Protocol. SenSys

9 Clinical deployment Step-down cardiac care unit 16 patient rooms, 1200 m 2 Network 18 relays: redundant network longest path: 3-4 hops channel 26 of IEEE HR and SpO2 collected every 30/60s disposable adult probes data not available to nursing staff 46 patients enrolled 41 days in total, 2-68 hours per patient up to 3 patients at a time 5 patients excluded from analysis base station relays 7

10 Potential for detecting clinical deterioration SpO2 SpO2 HR HR Bradycardia Pulmonary edema SpO2 HR Sleep apnea 8

11 System reliability Network reliability per patient: 99.68% median, range 95.2% - 100% effectiveness of two-tier DRAP/CTP network architecture Sensing reliability per patient: 80% median, range 0.46% % 29% of patients with sensing reliability < 50% System reliability dominated by sensing reliability! 9

12 Reliability metrics valid reading invalid reading 10

13 Reliability metrics valid reading invalid reading time-to-failure 10

14 Reliability metrics valid reading invalid reading time-to-failure time-to-recovery 10

15 Sensing reliability Failures are common: median time-to-failure 2 min long-tailed distribution caused by sensor disconnections short failure bursts caused by human movement An option to improve reliability oversampling CDF of time-to-failure CDF of time-to-recovery 11

16 Sensing reliability Failures are common: median time-to-failure 2 min long-tailed distribution caused by sensor disconnections short failure bursts caused by human movement An option to improve reliability oversampling CDF of time-to-failure CDF of time-to-recovery long tail 11

17 Sensing reliability Failures are common: median time-to-failure 2 min long-tailed distribution caused by sensor disconnections short failure bursts caused by human movement An option to improve reliability oversampling CDF of time-to-failure CDF of time-to-recovery 90% of outages < 4 min 11

18 Sensing reliability Failures are common: median time-to-failure 2 min long-tailed distribution caused by sensor disconnections short failure bursts caused by human movement An option to improve reliability oversampling 90% of outages < 2 min CDF of time-to-failure CDF of time-to-recovery 90% of outages < 4 min 11

19 Sources of sensing errors Heart Rate Time (min) Time (min) Hand movement Improper placement 12

20 Relax sampling requirement Increase the required sampling period to 5, 10, 15 min consider a success if one valid measurement per sampling still orders of magnitude higher rate than manual measurement Oversampling is beneficial reliability is increased, but at a diminishing return Sensing reliability for different required sampling rates 13

21 Relax sampling requirement Increase the required sampling period to 5, 10, 15 min consider a success if one valid measurement per sampling still orders of magnitude higher rate than manual measurement Oversampling is beneficial reliability is increased, but at a diminishing return Sensing reliability for different required sampling rates 13

22 Relax sampling requirement Increase the required sampling period to 5, 10, 15 min consider a success if one valid measurement per sampling still orders of magnitude higher rate than manual measurement Oversampling is beneficial reliability is increased, but at a diminishing return Sensing reliability for different required sampling rates 13

23 Alarms for sensor disconnections Automatically notify a nurse after receiving no valid data for a time balance nursing effort and reliability gain similar reliability to 5 and 10 min timeout at 15 min timeout 1.55 interventions per patient, per day Sensing reliability with different timeouts # of alarms per patient, per day 14

24 Alarms for sensor disconnections Automatically notify a nurse after receiving no valid data for a time balance nursing effort and reliability gain similar reliability to 5 and 10 min timeout at 15 min timeout 1.55 interventions per patient, per day Sensing reliability with different timeouts # of alarms per patient, per day 14

25 Alarms and oversampling are complementary Complementary mechanisms alarms handles disconnections oversampling handles intermittent failures Sensing reliability when alarms and oversampling are combined 15

26 Alarms and oversampling are complementary Complementary mechanisms alarms handles disconnections oversampling handles intermittent failures Sensing reliability when alarms and oversampling are combined 15

27 Automatic detection of clinical deterioration 79.3% True Positive Rate/(sensitivity) Random False Positive Rate/(1 - specificity) Sample: 29 with significant cardiac/pulmonary problems and 7 without CUSUM partitions time series into chunks with similar statistical props. Chunk features: 5th- and 95-percentiles, slope of linear fit deterioration detected by comparison with thresholds 16

28 Contributions Reliable two-tier architecture: 99.68% reliability per patient Cross-disciplinary research - key to understanding the problem system reliability is dominated by sensing errors complementary mechanisms for combating sensing errors oversampling disconnections alarms reduced patient with low reliability (<70%) from 42% to 12% Potential for detecting deterioration possible through inspection by clinician preliminary results of automatic detection of clinical deterioration 17

29 Contributions Reliable two-tier architecture: 99.68% reliability per patient Cross-disciplinary research - key to understanding the problem system reliability is dominated by sensing errors complementary mechanisms for combating sensing errors oversampling disconnections alarms reduced patient with low reliability (<70%) from 42% to 12% Potential for detecting deterioration possible through inspection by clinician preliminary results of automatic detection of clinical deterioration O. Chipara, C. Lu, T. C. Bailey, and G.-C. Roman. Reliable Clinical Monitoring using Wireless Sensor Networks: Experiences in a Step-down Hospital Unit. SenSys 2010 O. Chipara, C. Brooks, S. Bhattacharya, C. Lu, R. Chamberlain, G-C Roman, and T. C. Bailey. Reliable Real-time Clinical Monitoring Using Sensor Network Technology. AMIA

30 Research opportunities in medical domain Low-cost physiological monitoring applications: clinical deterioration, diabetes, Parkinson s trade-off between accuracy and resource utilization (energy, bandwidth) Smart living spaces for elderly patients applications: activity tracking, medication reminders sensor placement to improve activity detection Monitoring communities applications: diseases control, support groups (weight/depression) extraction of social relationships Integrated medical systems emergence of rich electronic medical records 18

31 Research challenges for computer science Predictable and robust wireless embedded systems holistic approach: sensing + computation + wireless techniques and tools to reason about system properties Interoperability heterogenous systems with extreme differences in capabilities multiple wireless technologies that need to co-exist privacy and security cannot be sacrificed resource-aware middleware for efficiency Management and configuration large scale systems algorithms for self-configuration and self-organization frequency- and time-domain wireless spectrum allocation 19

32 ?questions 20

33 Power management Radio consumes 19 ma DRAP achieves duty-cycles of 0.12% % during trial Sensor consumes 24 ma periodically turn the sensor on 8-seconds warm-up time sample for 7 seconds Internal flash 2.3 ma used to maintain statistics cache data in RAM, write infrequently to the internal flash Achieves up to 3 days of continuous operation 21

34 Network reliability Time-to-failure time interval during which the system continuously operates until failure Time-to-recovery time interval from the occurrence of a failure until the system recovers CDF of time-to-failure CDF of time-to-recovery 22

35 Network reliability Time-to-failure time interval during which the system continuously operates until failure Time-to-recovery time interval from the occurrence of a failure until the system recovers CDF of time-to-failure CDF of time-to-recovery median.me to failure 19 min 22

36 Network reliability Time-to-failure time interval during which the system continuously operates until failure Time-to-recovery time interval from the occurrence of a failure until the system recovers CDF of time-to-failure CDF of time-to-recovery recover from 90% of failures within 2 mins => quick recovery median.me to failure 19 min 22

37 System architecture 23

38 CTP under normal activity first-hop reliability: 85.38% relay reliability: 96.49% end-to-end reliability: 82.39% Normal activity experiment showed most packet losses occur on the first hop packet drops tend to follow user movement 24

39 DRAP reliability DRAP one-hop reliability: 100% relay reliability: 99.33% end-to-end reliability: 99.33% CTP one-hop reliability: 85.38% relay reliability: 96.49% end-to-end reliability: 82.39% 25