hierarchical monitoring and fuzzy logic control in anaesthesia
TRANSCRIPT
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Hierarchical Monitoring and Fuzzy Logic Control in Anaesthesia
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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
BalancedAnaesthesia
Analgesia Muscle Relaxation
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Automatic Control in Anaesthesia
The main problem : measurement of clinicalsigns 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
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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.
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Control algorithm: (a) PID, GPC (b) Fuzzy Logic (c) Neural Networks Etc.
Interpretative algorithm: (a) Aperiodic Analysis (b) Fourier Transform Analysis (c) Auto-Regression Algorithm (d) Fuzzy Logic (e) Neural Networks Etc.
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* Analgesia (Pain Relief)
no direct measurement subjective indirect measurement brain signals (i.e. SEP)
Postoperative conditions Cancer pain PCA (Patient-Controlled Analgesia)
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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
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Anaesthetists use “rules of thumb” “imprecise, personal rules”Ex: IF T1% is greater than the set point by a LARGE AMOUNT THEN set the atracurium infusion rate to a HIGH LEVE
This rules contains imprecise terms: a LARGE AMOUNT a HIGH LEVEL
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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
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1995 Zbinden & Hacisalihzade (Switzerland) MAP, Isoflurane, Human1996 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), SOFLC1997 Shieh et al. (ROC) Muscle Relaxation (Miv.)
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1998 Shieh et al. (ROC) Unconsciousness (Desflurane)
2000 Shieh et al. (ROC) Muscle Relaxation (Roc.)
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Computer Monitoring and Controlin Muscle Relaxation
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Introduction:
Short-acting non-depolarizing relaxants
Advance of modern computer technology
The purpose of this approach:
Small, handy and easy to use
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Clinical Methods of Nerve Stimulation:
• Single-twitch Stimulation• Train-of-four Stimulation • Double-burst Stimulation • Tetanic Stimulation
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Recording of Evoked Responses:
• Mechanical Responses• Electromyographic Responses • Accelerative Responses
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Mechanical Responses:
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Electromyographic Responses:
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Accelerative Responses:
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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.
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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.
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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 )
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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
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Hierarchical Monitoring and Fuzzy Logic Control
EmergencyTable
CoarseTable
Self-Tuning
FLC(Fine Table)
MedianFilter
PharmacologicalFilter
InstrumentFilter
EMGPatient EMG (F)S.P.+ E,CE
-
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(a)
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Manual Control for Atracurium:
Pt. Weight: 54 kg;Pt. Age: 34 yr;Sex: Female;Clinical Diagnosis: Lipoma retroperitoneal tumourOperation: Debaking
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Automatic Control for Mivacurium:
Pt. Weight = 58.5 kg;Pt. Age = 45 yr;Sex : FemaleClinical Diagnosis: CPS;Operation: FESS
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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
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Automatic Control of Anaesthesiawith Desflurane Using Hierarchical
Structure
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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.
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PATIENT
COMPUTER
SAP, HR, AA
Supervision by Anaesthetist
Datex AS/3
Control Box
Stepping Motor
Vaporizer
An Automatic Control System for Inhalational Anaesthesia
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A Clinical Trial (Unconsciousness)
Pt. Weight: 52 kg;Pt. Age: 19 yr;Sex: Female;Clinical Diagnosis: CPSOperation: FESS
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Modelling of Anaesthesia for Unconsciousness
Neural Networks
PatientModel
FiaaGender
AgeWeight
HRSAP
BIS
FuzzyController
Etaa
Rule-basedFuzzyModel
Neural NetworksVaporizer Model
FuzzyModel
FuzzyModel
SDOA
PDOA
DOA
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Automatic Monitoring and Control in the Operating Theatre
AS/3 MonitorVaporizer ControlNoteBook
Graseby 3500
Aspect 1050
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Monitoring EEG Signals of AwakeningDuring Propofol Anaesthesia
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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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Summary: (Awareness, EEG)
* The Beta, Alpha, Sub-alpha and Delta waves have significant differences during different stages
* The mean(SD) of the mean percentage of beta wave in 8 patients’ EEG signals during induction, maintenance, and recovery stages were 73.43(4.84)%, 42.26(8.31)% and 72.14(7.19)%, respectively.
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Detection of Awareness
Method I: Questions asked during structured interview 1. What was last thing you remember before you went to sleep for your operation ? 2. What was the first thing you remember after your operation ? 3. Can you remember anything in between these two periods ? 4. Did you dream during your operation ? 5. What was the worst thing about your operation ?
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Method II:Tape-recorder using earphones(church bells, farmyard noises, light orchestral music, piano music, market voices, bird song, pop music and choir music).
Method III:Forearm Technique
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EEG signals in Drowsy States
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Fundamental EEG Signals Research in Drowsy States
EEG electrode positions on the scalp
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Harmonie Digital EEG Systems- An Awake State
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Harmonie Digital EEG Systems- A Sleep State
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Brain Signal Processing and Analysis
1. Aperiodic Theory
2. 95% Spectrum Edge Frequency
3. Variety Analysis
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A Genetic Fuzzy System Applied in Analysis of Brain Signals
Signal P.
Rules
CCD
Crossover
Inf. Eng.Fuzz. Defuzz.
Reproduction
Mutation
Encode
Decode
Output E
R
R
O
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A Best Group
of Rules
Yes
Volunteers
Image
Brain Signals
Experts
CL
No
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Commercial Products for Mechanical, Sound, and Electrical Stimulation for Preventing Drowsy States
(a) Pulse massage (b) Sound wave that move your mind (c) Nod suppresser
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The Prototype of Monitoring and Controlling Brain Signals for Deterring Driver Drowsiness
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Pain Control and Evaluation using Fuzzy Logic Control
& Patient Controlled Analgesiafor Shock Wave Lithotripsy
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Problem: Detecting pain Subjective & no direct measurement Clinical: Visual Analogue Scale (VAS)
Where will cause the pain: Endoscope Operating room:ESWL; Prostate Post OP.: PCA 300/2000 per month $ 4,500 , nearly 1.5 million NT ICU Cancer Pain
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How to study the Pain: Provide a constant pain Not too long or too short for experiment Easy communication with the patient
Why do we choose the ESWL to study the Pain ? Provide a constant pain using ultrasonic waves Used for destroying calculi in the upper urinary tract and gallstones (OP time < 1 hr) Patients are consciousness
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Current Drugs:
1) Pain killer: fentanyl (0.0785 mg/ml) Preventing vomit: droperidol (2.5 mg/ml) 2) Loading dose: Fentanyl: 2 ml; droperidol: 0.25 ml 3) NA to add further dose of fentanyl if the patient complain. 4) If NA can not handle, call anaesthetist.
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PCA (Patient-Controlled Analgesia) : Management of pain in (1) postoperative patients (2) cancer patients Function: (1) administer small bolus doses of pain-control drugs (2) at fixed intervals (3) controlled by the patient with the push of a button
Pain Control Using PCA
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PCA in ESWL Drug: alfentanil Conc.: 0.5 mg/ml Loading dose: 0.5 ml Bolus if needed: 0.4 ml Lockout time: 1 min Infusion rate: 120 ml/hr
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Fuzzy Logic Control:– bolus + continuous infusion
– pain feedback controlled by patient
– input variable : pain, chan_pain
– output variable: chan_inf
Pain Control Using Fuzzy Logic Control
pain: BP, SP, ZPchan_pain: NB, NS, ZR, PS, PBchan_inf: BI, SI, ZO, SD, BD
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Syringe Pump
Notebook(Monitoring & Control)
Catheter
Pain signals controlled by the patient
Patient
Patient-Controlled Analgesia
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Clinical Evaluation of the Pain
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Current Commercial Machine Fuzzy Control Machine
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Prostate Research
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