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

BalancedAnaesthesia

Analgesia Muscle Relaxation

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

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.

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.

* Analgesia (Pain Relief)

no direct measurement subjective indirect measurement brain signals (i.e. SEP)

Postoperative conditions Cancer pain PCA (Patient-Controlled Analgesia)

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

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

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

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.)

1998 Shieh et al. (ROC) Unconsciousness (Desflurane)

2000 Shieh et al. (ROC) Muscle Relaxation (Roc.)

Computer Monitoring and Controlin Muscle Relaxation

Introduction:

Short-acting non-depolarizing relaxants

Advance of modern computer technology

The purpose of this approach:

Small, handy and easy to use

Clinical Methods of Nerve Stimulation:

• Single-twitch Stimulation• Train-of-four Stimulation • Double-burst Stimulation • Tetanic Stimulation

Recording of Evoked Responses:

• Mechanical Responses• Electromyographic Responses • Accelerative Responses

Mechanical Responses:

Electromyographic Responses:

Accelerative Responses:

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.

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.

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 )

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

Hierarchical Monitoring and Fuzzy Logic Control

EmergencyTable

CoarseTable

Self-Tuning

FLC(Fine Table)

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PharmacologicalFilter

InstrumentFilter

EMGPatient EMG (F)S.P.+ E,CE

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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

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

Automatic Control of Anaesthesiawith Desflurane Using Hierarchical

Structure

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.

PATIENT

COMPUTER

SAP, HR, AA

Supervision by Anaesthetist

Datex AS/3

Control Box

Stepping Motor

Vaporizer

An Automatic Control System for Inhalational Anaesthesia

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

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HRSAP

BIS

FuzzyController

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Rule-basedFuzzyModel

Neural NetworksVaporizer Model

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Automatic Monitoring and Control in the Operating Theatre

AS/3 MonitorVaporizer ControlNoteBook

Graseby 3500

Aspect 1050

Monitoring EEG Signals of AwakeningDuring Propofol Anaesthesia

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

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.

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 ?

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

EEG signals in Drowsy States

Fundamental EEG Signals Research in Drowsy States

EEG electrode positions on the scalp

Harmonie Digital EEG Systems- An Awake State

Harmonie Digital EEG Systems- A Sleep State

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

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Reproduction

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A Best Group

of Rules

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Volunteers

Image

Brain Signals

Experts

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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

The Prototype of Monitoring and Controlling Brain Signals for Deterring Driver Drowsiness

Pain Control and Evaluation using Fuzzy Logic Control

& Patient Controlled Analgesiafor Shock Wave Lithotripsy

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

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

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.

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

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

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

Syringe Pump

Notebook(Monitoring & Control)

Catheter

Pain signals controlled by the patient

Patient

Patient-Controlled Analgesia

Clinical Evaluation of the Pain

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Current Commercial Machine Fuzzy Control Machine

Prostate Research