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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. Objective. Application of computer, information technology and Signal Processing techniques to Patient Monitoring/Diagnostic Systems

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Advanced Patient Monitoring/Diagnostic Systems

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  1. 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

  2. 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

  3. Schematic Diagram for Connectivity PDA School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. DEMO for Data Downloadingfrom IQ System School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  11. 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

  12. 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

  13. 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

  14. Conventional vs. New Method School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  15. 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

  16. Web-Based Display Design School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  17. Web-Based Patient Monitor School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  18. 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

  19. Diagram for Data Fusion Diagnosis School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  20. 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

  21. 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

  22. Receiver Operating Characteristics (ROC) School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. Septic Shock (SEPSIS) Detection School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. DEMO for Dynamic Frank-Sterling Curve School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  37. Medical Record Format in ICU School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  38. PDA Input Format Design School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  39. PDA Output Format Design School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

  40. 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

  41. 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

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

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

  44. Thank You !!! School of Biomedical Engineering & College of Medicine, Drexel University, Philadelphia, PA 19104

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