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Advanced Patient Monitoring/Diagnostic Systems. Han C. Ryoo, Ph. D. Herbert Patrick, M.D., MSEE Hun H Sun, Ph.D. Objective. Application of computer, information technology and Signal Processing techniques to Patient Monitoring/Diagnostic Systems

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advanced patient monitoring diagnostic systems

Advanced Patient Monitoring/Diagnostic Systems

Han C. Ryoo, Ph. D.

Herbert Patrick, M.D., MSEE

Hun H Sun, Ph.D.

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

objective
Objective
  • Application of computer, information technology and Signal Processing techniques to Patient Monitoring/Diagnostic Systems
  • Provide clinicians with computer-aided diagnosis to improve health care services

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

schematic diagram for connectivity
Schematic Diagram for Connectivity

PDA

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

presentation summary
Presentation Summary
  • Connectivity in Data Collection

- Difficulties & Solutions

  • Patient Monitoring

- Current Problems

- Project goals for patient monitoring

  • Signal Processing Technique

- Data Fusion Theory and Application

  • Application

- Current Research Projects

  • Project Perspectives

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

introduction to monitoring devices systems
Introduction to Monitoring Devices/Systems

Invasive Edwards (Baxter) Vigilance Monitor

Non-Invasive IQ System

(Partially) Invasive

Philips Monitor

Invasive: EDVI, CI, SvO2

(SpO2, Pleth, NBP, ABP, PAP)

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

difficulties in data collection
Difficulties in Data Collection
  • No Data Retrieval from conventional devices

- Print-out data only

- Not possible to process

  • Patient data removed every 12 hours
  • Different Communication parameters
  • Interfacing multiple devices made by different manufacturers

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

aveleno data verceles m d
Aveleno Data (Verceles, M.D.)

HR: Heart rate

Pulse: Blood Pulse

CVP: Central Venous Pressure

NBP: Noninvasive Cuff Blood

Pressure

SpO2: Tissue Oxygenation

RESP: Respiration Rate

T1: Body Temperature

PERF: Perfusion

ABP: Arterial Blood Pressure

S – Systolic

D – Diastolic

M- Average

PAP: Pulmonary Artery Blood

Pressure

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

patient data in print out form
Patient Data in Print-Out Form
  • Signal Conditioning
  • -Pre-Amp, Filtering, Interpolation
  • -Synchronization in data channels and computer
  • systems
  • Data Transfer & Storage
  • -Data Compression, Real-time transfer through
  • Intranet and Internet
  • Signal Processing
  • Extract hidden features
  • Distinguish abnormal from normal signs
  • Algorithm development
  • Data Fusion – Advanced Signal Processing Tech.
  • - Optimal combination of features from multiple
  • sources for accurate diagnosis

Heart Rate

NBP

SpO2

Resp

ABP

S

M

D

PAP

Body Temperature

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

connectivity for data collection
Connectivity for Data Collection

Communication Parameters

1. Sampling rates

2. Data structures

- parity, data bits,

- start/stop bits

- flow control

Intranet/Internet

(Partially Invasive) Philips Monitor

Interface

Multiple

Devices

(Invasive) Edwards (Baxter) Device

Computer

Server

with

High Capacity

Bedside

Computer

(Non-invasive) IQ System

- invented by Dr. Sun

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

demo for data downloading from iq system
DEMO for Data Downloadingfrom IQ System

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

problems in patient monitoring
Problems in Patient Monitoring
  • Overloaded data over 24-hours
  • Time-Consuming labor for diagnosis

- No timely decision

  • False Alarm – Nursing Inefficiency
  • Vital signs only but No disease specific
  • Display but No automatic correlation
  • No Data and System Integration

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

project goals
Project Goals
  • Computer-aided diagnosis and monitoring (CAD/CAM)

- Early, timely detection of vital signs

- Accurate decision making with reduced

information overload and false alarm

- Symptom (disease) specific rather than

vital sign specific

- Global alarm with severity classification

  • Applying to various symptoms

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

strategic data collection
Strategic Data Collection
  • Medical Monitors chosen

- FDA approved, Commercially available

- Continuous data flow by invasive and/or non-invasive sensors

- Familiar with Clinicians and Nurses

- Easy to use and manipulate

  • Multiple channels for simultaneous data acquisition – ECG, BP, Resp, EEG, EMG, T, Oxygenation, Bio-impedance, and etc.

Drexel University Gateway

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

conventional vs new method
Conventional vs. New Method

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

techniques required
Techniques Required
  • Computer Networking, Database Design

- Data Interface, collection, transfer via Intranet/Internet, storage

  • Web Based Programming

- Innovative & Interactive Display Format Design

  • Signal Processing Techniques

- data cleaning, processing, extracting hidden features, data fusion, return of processed results

- Severity Stratification (Apache, TISS, or ROC)

  • Wireless/Mobile Communication

-remote sensing/monitoring

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

web based display design
Web-Based Display Design

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

web based patient monitor
Web-Based Patient Monitor

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

fusion theory for decision making
Fusion Theory for Decision Making
  • Fusion is a process of combining information from different sensors when there is no fusing law indicating the correct way
  • Fusion problem can be defined in terms of finding such a fusing law
  • Logical, Statistical, Clinical criteria and etc

i.e, AND, OR, Minimum Error, Multi-variate Classifier, and the other.

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

diagram for data fusion diagnosis
Diagram for Data Fusion Diagnosis

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

binary decision problem
Binary Decision Problem

Cost function (CF)

CF= C00 P(accept H0, H0 true)

+ C01 P(accept H0, H1 true)

+ C10 P(accept H1, H0 true)

+ C11 P(accept H1, H1 true)

S (k) : samples of input signal

n (k) : additive noise

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

multi sensor fusion system
Multi-Sensor Fusion System

Likelihood Ratio Test

Minimum error criterion

C00 = C11 = 0

C10 = C01 = 1

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

receiver operating characteristics roc
Receiver Operating Characteristics (ROC)

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

current research projects
Current Research Projects

I. Prediction of Survivor & Non-survivor

II. Septic Shock Detection (Drexel Synergy Grant 2002)

III. Identifying Life and Death (Drexel Synergy Grant 2003)

IV. Hypovolemic reversible and refractory circulatory collapse - Research Proposal to NIH

V. Automatic Resuscitation System Design

VI. PDA Project - GlaxoSmithKline (GSK) and Merck

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

prediction of survivor nonsurvivor
Prediction of Survivor & Nonsurvivor
  • Data (Hospital admission/Discharge or not survived)
    • 113 critically ill patients (70 survivors and 43 Non-survivors ) with

- Nonshock - Hypovolemic shock

      • - Cardiogenic shock - Bacterial shock
      • - Neurogenic shock - Others
    • 58 males and 55 females (Age :16 - 82 years)
  • 13 Physiological Variables

1. Systolic Pressure --- mm Hg (SP)

2. Mean Arterial Pressure --- mm Hg (MAP)

3. Heart Rate --- beats/min (HR)

4. Diastolic Pressure --- mm Hg (DP)

5. Mean Central Venous Pressure --- cm H2O (MCVP)

6. Body Surface Area --- m2 (BSA)

7. Cardiac Index --- liters/min m2 (CI)

8. Mean Circulation Time --- sec (MCT)

9. Urinary Output --- ml/hr (UO)

10. Plasma Volume Index --- ml/kg (PVI)

11. Red Cell Index --- ml/kg (RCI)

12. Hemoglobin --- gm/100 ml (Hgb)

13. Hematocrit --- percent (Hct)

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

the roc area by linear discriminant analysis with single variables

Initial Set of Measurements:

Admission

Final Set of Measurements:

Death / Discharge

Variable

Average

ROC Area

Average

ROC Area

Trend from initial to final

S

S*

S

NS

S

NS

SP

115

92

0.66

131

78

0.77

MAP

80

63

0.69

89

48

0.87

HR

102

108

0.56

101

88

0.69

DP

63

51

0.67

67

36

0.90

MCVP

78

107

0.98

72

108

0.55

BSA

172

163

0.62

172

165

0.57

CI

271

235

0.72

341

217

0.92

MCT

213

251

0.69

178

244

0.79

UO

80

15

0.98

127

7

0.99

PVI

481

476

0.72

540

519

0.78

RCI

220

203

0.96

211

198

0.96

HGB

115

113

0.65

111

97

0.75

HCT

351

346

0.69

318

283

0.61

Average

0.73

0.78

The ROC area by Linear Discriminant Analysis with Single Variables

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

prediction of survivior nonsurvivor
Prediction of Survivior & Nonsurvivor

113 data sets from Initial : PREDICTION (S- 70, NS- 43)

ROC

Histogram

S vs NS

S vs NS

Area – 0.9196

Area – 0.9196

Frequency

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

classification of survivor nonsurvivor
Classification of Survivor & Nonsurvivor
  • 113 data sets from Final : CLASSIFICATION (S- 70, NS- 43)

Histogram

ROC

S vs NS

Area 0.9764

Area 0.9764

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

septic shock detection drexel univ synergy grant 2002
Septic Shock Detection (Drexel Univ. Synergy Grant 2002)
  • Inclusion Criteria for Septic Shock

1. Systemic Inflammatory Response Syndrome (SIRS) : two or more of the following

- Body Temperature > 100.4º F, or < 96.8º F (>38º C or <36º C)

- Heart Rate > 90 beats/min

- Respiratory Rate (RR) > 20 breaths/min

- Hyperventilation (PaCO2 < 32 mmHg)

- White Blood Cell Count > 12,000 cells/mm3, or < 4,000 cells/mm3

- Immature Neutrophils > 10 %

2. SEPSIS = SIRS resulting from infection (bacterial, viral, fungal or parasitic)

- sputum or urine samples.

3. Severe SEPSIS = SEPSIS + Hypotension

(Systolic BP < 90 mmHg, or 40 mmHg drop from Baseline)

4. Septic Shock = Severe SEPSIS + Refractory to fluid resuscitation (FR)

-   Mean Arterial Pressure (MAP) <70 for 1 hour despite (FR)

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

septic shock sepsis detection
Septic Shock (SEPSIS) Detection

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

identifying life and death drexel univ synergy grant 2003
Identifying Life and Death(Drexel Univ. Synergy Grant 2003)

Premature Ventricular Contraction (PVC) not preceded by P wave

Premature Ventricular Contraction (PVC) not preceded by P wave

Increased R-R Intervals

Increased R-R Intervals

Heart stopped and Death Identified

Heart Stopped and Death Identified

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

identifying life and death
Identifying Life and Death

Heart Rate

Death

pNN50

NNSD

Irregular HR

Premature Ventricular Contraction

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

8:00 8:20 8:40 1H

identifying life and death1
Identifying Life and Death

Heart Rate

Heart Rate (HR)

Blood Pressure

Systolic

Mean

Diastolic

8:29 12:29 16:50 20:50 0:50 4:50 24h

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

identifying life and death2
Identifying Life and Death

Heart Rate (HR)

Death

Earlier respiration stop

Respiration Rate (RR)

Partial Pressure of arterial oxygen (SpO2)

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

hypovolemic reversible and refractory circulatory collapse
Hypovolemic reversible and refractory circulatory collapse

Can be recovered

Difficult Recovery

Responsive

Refractory

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

dynamic frank starling curve plot of edv vs sv or co
Dynamic Frank-Starling Curve(Plot of EDV vs. SV or CO)

1

1

+

2

2

Risky Situation

Almost Recovered

Optimal Recovery

-

3

3

t4

t5

t6

t7

t8

t2

t1

t3

Continuous cardiac index (CCI) vs. End-diastolic volume index (EDVI) with Slope Changes

Slope 1: Continue fluid infusion, Slope 2: Slower fluid infusion, Slope 3: Stop infusion

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

slide36
DEMO for Dynamic Frank-Sterling Curve

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

medical record format in icu
Medical Record Format in ICU

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

pda input format design
PDA Input Format Design

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

pda output format design
PDA Output Format Design

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

therapeutic diagnostic application
Therapeutic/Diagnostic Application
  • Anesthesia, Shock, Dehydration
  • Alertness/Vigilance Assessment
  • Sleep Disorder
  • Pilot State Monitor
  • Hypovolemia / Fluid Resuscitation
  • ……

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

deployment of our technology
Deployment of Our Technology
  • Hospital

- ICU, Surgical OR, Trauma Ctr., Dialysis Ctr., ER,

  • Home care & remote locations, ex. Islands
  • Military Purpose – Ship Crew, Battlefield
  • Educational & Research Usage
  • Remote monitoring with wireless communication

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

slide42
School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104
slide43
School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104
thank you
Thank You !!!

School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104