Toward Wireless Clinical Monitoring

Transcription

1 Toward Wireless Clinical Monitoring Chenyang Lu Department of Computer Science and Engineering

2 Outline Clinical event detec9on Wireless clinical monitoring for general hospital units Wireless structural health monitoring Real- 9me scheduling for wireless sensor- actuator networks 2

3 Challenge Clinical deteriora9on in hospitalized pa9ents is a serious concern 4-17% suffer from adverse events such as cardiac or respiratory arrest. 70% of such events could have been prevented. Goal: automated, early detec9on of clinical deteriora9on Real- 9me monitoring of vital signs and electronic clinical records Detect clinical deteriora9on using machine learning algorithms Automa9cally generate alarms to nursing staff for interven9on 3

4 Clinical Event Detec9on Project 1. Real- 9me vital sign monitoring using wireless sensor networks Reliable wireless monitoring system Clinical trial in Barnes- Jewish Hospital 2. Mul9-9er clinical event detec9on system Tier 1: Early warning based on clinical data in electronic medical records Tier 2: Real- 9me event detec9on based on wireless sensor data A larger clinical trial of integrated event detec9on system Mul9- disciplinary team: Computer Science & Engineering, School of Medicine, Barns- Jewish Hospital 4

5 Clinical Event Detec9on Project 1. Real- 9me vital sign monitoring using wireless sensor networks Reliable wireless monitoring system Clinical trial in Barnes- Jewish Hospital 2. Mul9-9er clinical event detec9on system Tier 1: Early warning based on clinical data in electronic medical records Tier 2: Real- 9me event detec9on based on wireless sensor data A larger clinical trial of integrated event detec9on system Mul9- disciplinary team: Computer Science & Engineering, School of Medicine, Barns- Jewish Hospital 5

6 Mo9va9on Early detec9on of clinical deteriora9on clinical deteriora9on is oven preceded by changes in vitals... but, real- 9me pa9ent monitoring is required wired pa9ent monitoring equipment in ICUs most general hospital units collect vitals manually wireless telemetry systems too expensive for wide adop9on Goal: reliable and low- cost wireless pa3ent monitoring for general hospital units 6

7 Related projects Elderly care: ALARM- NET, Intel Disaster recovery: SMART, AID- N, WIISARD Emergency room: MEDISN (24hr deployment) Commercial wireless telemetry (Phillips, Cisco, GE) WiFi => single- hop wireless, wired backbone adop9on limited to specialized hospital units 7

8 Wireless Sensor Networks vs. Wi- Fi More energy efficient at low data rate Common vital signs have low data rate. Nurses are too busy to change baeeries! Sufficient reliability Median network reliability > 99% in our clinical trial. Even a wired network would improve reliability only marginally. Low deployment cost Mesh networks do not require wired infrastructure. Ease adop9on (e.g., field hospitals, rural areas). Even major hospitals may not guarantee full Wi- Fi coverage. 8

9 System Architecture Base sta9on connected to electronic medical records Relays Connected to wall outlet Redundant deployment coverage fault tolerance Portable pulse oximeter pulse oximeter + microcontroller + radio baeery operated 9

10 Deployment in a Step- down Cardiology Unit 10

11 Reliable Network Architecture Challenge: Pa9ents in general hospital units are ambulatory. Solu9on: Two- 9er protocols for end- to- end data delivery. 1 Dynamic Relay Associa9on Protocol (DRAP): Pa9ent - > 1 st relay Dynamically associate the pa9ent node with a relay Single- hop protocol handles pa9ent mobility Simplify power management in pa9ent nodes (send only) 2 Sta9onary relay network: 1 st relay - > - > base sta9on Reuse well tested mesh rou9ng protocol: CTP Isolated from pa9ent mobility Wall- plugged => no need to worry about energy 11

12 Clinical Study Step- down cardiac care unit 16 pa9ent rooms, 1200 m 2 18 relays: redundant network with 4-5 hops Channel 26 of IEEE Data collec9on Pulse and oxygena9on collected every 30 or 60 seconds Data saved on base sta9on and Flash on pa9ent device 46 pa9ents enrolled >41 days of monitoring; 2-68 hours per pa9ent 5 pa9ents excluded from reliability analysis Ini9ally one pa9ent at a 9me, then up to 3 pa9ents at a 9me Data is hidden from nurses 12

13 Feasibility of Clinical Sensor Networks? Poten9al for detec9ng clinical deteriora9on System reliability Contributors of system failures Distribu9on of network and sensing failures Maintenance overhead on nursing staff Energy efficiency: baeery life of pa9ent nodes 13

14 Poten9al for Detec9ng Clinical Deteriora9on Pulmonary edema Sleep apnea Bradycardia 14

15 System reliability Network reliability >95% for all pa9ents. Median sensing reliability > 80%. System reliability dominated by sensing reliability. 15

16 Network Reliability Median 9me to failure > 22 min Quick recovery from failure 90% of 9me- to- recovery < 2 min, max = 18 min CDF of 9me- to- failure CDF of 9me- to- recovery 90% of outages " < 2 min 16

17 Sensing Reliability Failures are common: median 9me- to- failure < 2 min Recover from 90% of sensing failures within 4 min High variability due to human movement => oversampling Long- tailed distribu9on => need for an alarm system CDF of 9me- to- failure CDF of 9me- to- recovery 90% of outages < 4 min Long tail 17

18 Assuming Lower Sampling Rate 25 pa9ents have >80% sensing reliability for 1 reading/5 min S9ll orders of magnitude higher rate than manual measurement Oversampling at 1-2 reading/min helped Some pa9ents had only limited improvement => sensor disconnected CDF of sensing reliability 18

19 Benefits of Sensor Disconnec9on Alarms Nurses may be no9fied automa9cally about disconnec9ons aver 9meout Small number of interven9ons necessary 15 min 9meout: <2 interven9on per pa9ent, per day CDF of sensing reliability #alarms per day 19

20 PuOng them together Disconnec9on alarms and oversampling are complementary alarms => handles the long tail oversampling => improves overall sensing reliability 20

21 Conclusion A reliable clinical monitoring system using wireless sensor networks. First clinical study of a wireless sensor network in a step- down hospital unit Highly reliable network System reliability dominated by pulse oximeter sensors Oversampling Disconnec9on alarms Sufficient resolu9on for detec9ng clinical deteriora9on On- going: Mul9-9er clinical event detec9on system Tier 1: Early warning based on clinical data in electronic medical records Tier 2: Real- 9me event detec9on based on wireless sensor data A larger clinical trial of integrated event detec9on system 21

22 Acknowledgement Octav Chipara, Gruia- Catalin Roman, Computer Science Thomas Bailey, Marin Kollef, School of Medicine Nursing and IT staff, Barnes and Jewish Hospital Na9onal Center For Research Resources: CTSA grant Barnes- Jewish Hospital Founda9on NSF: NeTS- NOSS, CRI O. Chipara, C. Lu, T.C. Bailey and G.- C. Roman, Reliable Clinical Monitoring using Wireless Sensor Networks: Experience in a Step- down Hospital Unit, ACM Conference on Embedded Networked Sensor Systems (SenSys'10), November

23 Structural Health Monitoring Cyber- physical co- design of sensor networks and structural health monitoring/control Successful damage localiza9on on physical lab structures Advantages over centralized approaches reduce latency by 88% x3.4 increase in life9me under an hourly schedule NSF CPS grant G. Hackmann, W. Guo, G. Yan, C. Lu, S. Dyke, Cyber- Physical Codesign of Distributed Structural Health Monitoring With Wireless Sensor Networks, ICCPS'10. G. Hackmann, F. Sun, N. Castaneda, C. Lu and S. Dyke, A Holis9c Approach to Decentralized Structural Damage Localiza9on Using Wireless Sensor Networks, RTSS 08.

24 Real- Time Sensor- Actuator Networks WirelessHART Open industrial standard for wireless sensor- actuator networks in Targeted at process monitoring and control industry Commercial deployment in real- world environments Real- 9me scheduling for WirelessHART Mapping to real- 9me CPU scheduling Handle conflicts associated with wireless transmissions General theory for real- 9me scheduling for sensor- actuator networks A. Saifullah, Y. Xu, C. Lu and Y. Chen, Real- Time Scheduling for WirelessHART Networks, IEEE Real- Time Systems Symposium (RTSS'10), December O. Chipara, C. Lu and G.- C. Roman, Real- 9me Query Scheduling for Wireless Sensor Networks, IEEE Real- Time Systems Symposium (RTSS'07), December

25 Thanks! For more informa9on: hep:// 25