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Hierarchical Monitoring and Fuzzy Logic Control in Anaesthesia

Hierarchical Monitoring and Fuzzy Logic Control in Anaesthesia. What is anaesthesia ? * Art or Science * Loss of Awareness * Loss of all Sensation (Pain, Temp., Position) * The process should be reversible * Modern general anaesthesia (TRIAD). Unconsciousness. Balanced

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Hierarchical Monitoring and Fuzzy Logic Control in Anaesthesia

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  1. Hierarchical Monitoring and Fuzzy Logic Control in Anaesthesia

  2. What is anaesthesia ? * Art or Science * Loss of Awareness * Loss of all Sensation (Pain, Temp., Position) * The process should be reversible * Modern general anaesthesia (TRIAD) Unconsciousness Balanced Anaesthesia Analgesia Muscle Relaxation

  3. Automatic Control in Anaesthesia The main problem : measurement of clinical signs for on-line input to the system * Muscle Relaxation: direct measurement: EMG control algorithm: PID, GPC, FLC, etc. * Unconsciousness (Depth of Anaesthesia): no direct measurement

  4. Indirect measurement: (a) SAP, HR (b) SW, LA, PR, MO (c) End Tidal of AA, MAC, (d) Plasma concentration (e) Heart Rate Variability (f) Brain Signals EEG, AEP, SEP Etc.

  5. Interpretative algorithm: (a) Aperiodic Analysis (b) Fourier Transform Analysis (c) Auto-Regression Algorithm (d) Fuzzy Logic (e) Neural Networks Etc. Control algorithm: (a) PID, GPC (b) Fuzzy Logic (c) Neural Networks Etc.

  6. * Analgesia (Pain Relief) no direct measurement subjective indirect measurement brain signals (i.e. SEP) Postoperative conditions Cancer pain PCA (Patient-Controlled Analgesia)

  7. Fuzzy logic features: * Can reason with imprecise data * Leads to "soft computing" * Concept of "machine IQ" * Cope with non-linear, complex, unknown processes * Link with neural networks * Link with genetic algorithms * Lack of stability theory

  8. Anaesthetists use “rules of thumb” “imprecise, personal rules” Ex: IFT1% is greater than the set point by a LARGE AMOUNT THENset the atracurium infusion rate to a HIGH LEVE This rules contains imprecise terms: a LARGE AMOUNT a HIGH LEVEL

  9. In Clinical Engineering: 1988 Linkens & Mahfouf (UK) Muscle Relaxation (Simulation) 1988 Sheppard & Ying (USA) MAP, SNP (Sodium Nitroprusside) 1992 Hacisalihzade et al. (Switzerland) MAP, Isoflurane 1994 Tsutsui & Arita (Japan) SAP, Enflurane 1995 Shieh et al. (UK) DOA, Propofol & Isoflurane

  10. 1995 Zbinden & Hacisalihzade (Switzerland) MAP, Isoflurane, Human 1996 Schaublin & Zbinden (Switzerland) End Tidal CO2, Ventilation (Fre., Vol.) 1996 Curatolo & Zbinden (Switzerland) Fi (Iso.) & O2 conc., min. flow 1996 Mason et al. (UK) Muscle relaxation (Atracurium) 1996 Shieh et al. (ROC) Muscle Relaxation (Atr.) 1997 Mason et al. (UK) Muscle relaxation (Atracurium), SOFLC 1997 Shieh et al. (ROC) Muscle Relaxation (Miv.)

  11. 1998 Shieh et al. (ROC) Unconsciousness (Desflurane) 2000 Shieh et al. (ROC) Muscle Relaxation (Roc.)

  12. Computer Monitoring and Control in Muscle Relaxation

  13. Introduction: Short-acting non-depolarizing relaxants Advance of modern computer technology The purpose of this approach: Small, handy and easy to use

  14. Clinical Methods of Nerve Stimulation: • Single-twitch Stimulation • Train-of-four Stimulation • Double-burst Stimulation • Tetanic Stimulation

  15. Recording of Evoked Responses: • Mechanical Responses • Electromyographic Responses • Accelerative Responses

  16. Mechanical Responses:

  17. Electromyographic Responses:

  18. Accelerative Responses:

  19. Requirements for the ideal neuromuscular blocking agent: 1. Non-depolarizing mechanism of action 2. Rapid onset of action 3. Short duration of action 4. Rapid recovery 5. Non cumulative 6. No cardiovascular side effects 7. No histamine release 8. Reversible by cholinesterase inhibitors 9. High potency 10. Pharmacologically inactive metabolites.

  20. The characteristics of Atracurium, Mivacurium and Rocuronium : 1. An aqueous for intravenous injection 2. Conc.: 10 (A), 2 (M), 10 (R) mg/ml 3. Non-depolarizing neuromuscular blocking drug 4. Onset time: 1.5 (A), 2.5 (M), 1 (R) min 5. Duration of action: Intermediate (A, R), Short(M) 6. Recovery: Intermediate (A, R), Rapid (M) 7. Cardiovascular effect: Yes(A, M), No (R) 8. Histamine release: Yes (A, M), No (R) 9. Metabolites.

  21. Atracurium: Laudanosine (Central effects) (Cisatracurium: 1/3 Laudanosine) Mivacurium: Enzyme Rocuronium : Liver, Kidney (Vecuronium: causing prolonged block) The Wellcome Foundation (A, M): J. Savarese Organon Teknika ( R )

  22. Hierarchical Monitoring of EMG via filters * Built-in filter: Noise High Frequency Disturbance * Pharmacological filter: T4/T1 T2/T1, T3/T2, T4/T3 * Median filter x1, x2, x3,x4,x5 => Median Value Example: T1% 7, 17, 11, 10, 8 => 10

  23. Hierarchical Monitoring and Fuzzy Logic Control Emergency Table Median Filter Coarse Table Pharmacological Filter Self-Tuning Instrument Filter S.P.+ E,CE - FLC (Fine Table) Patient EMG EMG (F)

  24. Manual Control for Atracurium: Pt. Weight: 54 kg; Pt. Age: 34 yr; Sex: Female; Clinical Diagnosis: Lipoma retroperitoneal tumour Operation: Debaking

  25. Automatic Control for Mivacurium: Pt. Weight = 58.5 kg; Pt. Age = 45 yr; Sex : Female Clinical Diagnosis: CPS; Operation: FESS

  26. Summary: (Muscle Relaxation) Clinically, this system was useful: * Provided stable surgical operating conditions * Minimized the amount of neuromuscular blocker required by each patient * Reduced the need for the anaesthetist to spend time controlling neuromuscular block * Allowed reliable antagonism of neuromuscular block at the end of surgery

  27. Automatic Control of Anaesthesia with Desflurane Using Hierarchical Structure

  28. Unconsciousness (Depth of Anaesthesia ) : no direct measurement indirect measurement: SAP, HR SW, LA, PR, MO End Tidal of AA, MAC Plasma concentration Brain Signals (i.e., EEG, AEP, SEP) Etc.

  29. An Automatic Control System for Inhalational Anaesthesia Vaporizer PATIENT Datex AS/3 Stepping Motor SAP, HR, AA COMPUTER Control Box Supervision by Anaesthetist

  30. A Clinical Trial (Unconsciousness) Pt. Weight: 52 kg; Pt. Age: 19 yr; Sex: Female; Clinical Diagnosis: CPS Operation: FESS

  31. Modelling of Anaesthesia for Unconsciousness Neural Networks Patient Model Fiaa HR Fuzzy Model Rule-based Fuzzy Model SDOA DOA SAP Gender Age BIS Fuzzy Model PDOA Weight Etaa Neural Networks Vaporizer Model Fuzzy Controller

  32. Automatic Monitoring and Control in the Operating Theatre AS/3 Monitor Vaporizer Control NoteBook Graseby 3500 Aspect 1050

  33. Monitoring EEG Signals of Awakening During Propofol Anaesthesia

  34. Problem: Detecting awareness In Paralyzed patient remained unsolved. Cardiovascular signs are not always reliable. The aim of this study: To investigate the changes of different waves in EEG signals during different anaesthetic stages To identify the parameter for early detection awareness

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