Method and system for quantifying anaesthesia or a state of vigilance

• US 9,474,452 B2
• Filed: 05/21/2012
• Issued: 10/25/2016
• Est. Priority Date: 06/17/2011
• Status: Expired due to Fees

• Current Assignee: Technische Universitat Munchen
• Original Assignee: Technische Universitat Munchen
• Inventors: Kochs, Eberhard F., Schneider, Gerhard, Jordan, Denis, Omerovic, Adem, Kreuzer, Matthias
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/124,024
• Publication Number:
  US 20140155706A1
• Time in Patent Office: 1,618 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/38 Acoustic or auditory stimuli
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A method and a system for quantifying anaesthesia and/or a state of vigilance (e.g. monitoring sedation or sleep) from a plurality of parameters acquired from a subject allows to determine an indicator that reliably quantifies the hypnotic component of anaesthesia and/or the state of vigilance and/or the analgesic component of anaesthesia, even if the number of parameters varies while monitoring the subject. Preferably, a first sub-indicator based on a first subset of parameters adapted to characterize a boundary region between consciousness and unconsciousness is determined, and a second sub-indicator based on a second subset of parameters adapted to characterize a level of hypnosis of said subject is determined, and said first sub-indicator and said second sub-indicator are then combined to compute a global indicator value.

Claims

1. A method for quantifying at least one of anaesthesia and a state of vigilance from a plurality of parameters acquired from a subject, said method comprising the steps of:

acquiring a plurality of p parameters pertaining to said subject, said parameters being selected from a first parameter group comprising electrocardiogram data acquired from said subject, a heart rate, a heart rate variability, a blood pressure, a blood pressure variability, a breathing gas composition, a pharmacokinetic/pharmacodynamic modelled effect site concentration and/or plasma concentration, and/or a second parameter group comprising electroencephalogram data and/or auditory evoked potential data acquired from said subject, and/or a third parameter group comprising subject data and/or medication data;

wherein p≧

2, and at least one of said p parameters is selected from said first parameter group or said second parameter group;

wherein said parameters of at least one of said first parameter group and said second parameter group are acquired continuously or at predetermined time intervals by monitoring said subject; and

determining an indicator that performs at least one of quantifying a depth of anaesthesia, detecting intraoperative awareness and quantifying an analgesic component of anaesthesia or a state of vigilance of said subject from said p parameters;

wherein said step of determining said indicator comprises a step of combining said p parameters, wherein said number p of parameters is variable while monitoring said subject;

wherein said step of determining said indicator comprises the steps of;

determining a first sub-indicator based on a first subset of parameters chosen among said p parameters and/or based on a comparison with a first training set of data, wherein said first sub-indicator is adapted to characterize a first aspect of an anaesthesia or vigilance state of said subject;

determining a second sub-indicator based on at least one of a second subset of parameters chosen among said p parameters and a comparison with a second training set of data, said second subset of parameters being different from said first subset of parameters and/or said second training set of data being different from said first training set of data, wherein said second sub-indicator is adapted to characterize a second aspect of an anaesthesia or vigilance state of said subject; and

combining said first sub-indicator and said second sub-indicator to compute said indicator.

2. The method according to claim 1, wherein said step of combining said p parameters comprises the step of employing one or more of an adaptive neuro fuzzy inference system, a neural network, regression, support vector machines and statistical relational machine learning.

3. The method according to claim 1, further comprising the step of replacing a parameter that becomes unavailable by means of a model-based replacement using initialized values or ranges of values, an average value imputation, univariate regression from previously recorded parameter values, multivariate regression from previously recorded parameter values, or matching of an incomplete parameter vector with a complete vector of previously recorded parameter values, or by means of a prediction employing a K-nearest neighbour model, a self-organizing map, multi-layer perception, or recurrent neural network techniques.

4. The method according to claim 1, wherein at least one parameter among said p parameters is chosen from said second parameter group.

5. The method according to claim 1, wherein said first aspect is a boundary region between alertness and unconsciousness of said subject, and said first subset preferably comprises at least one of electroencephalogram data, in particular at least one of an ordinal permutation entropy computed from said electroencephalogram data and a symbolic transfer entropy computed from said electroencephalogram data, a weighted spectral median frequency computed from said electroencephalogram data, a breathing gas composition, in particular an inspiratory oxygen concentration, an expiratory oxygen concentration, an expiratory CO₂ concentration, an inspiratory anaesthetic gas concentration, an expiratory anaesthetic gas concentration, a pharmacokinetic/pharmacodynamic modelled effect site concentration, a plasma concentration, electrocardiogram data, an approximate entropy of a heart rate variability, an ordinal permutation entropy of a heart rate variability, subject data and medication data.

6. The method according to claim 1, wherein said second aspect is a level of anaesthesia of said subject, and said second subset preferably comprises at least one of electroencephalogram data, in particular an approximate entropy, an ordinal permutation entropy computed from said electroencephalogram data, a burst suppression ratio computed from said electroencephalogram data, a breathing gas composition, in particular an inspiratory oxygen concentration, an expiratory oxygen concentration, an expiratory CO₂ concentration, an inspiratory anaesthetic gas concentration, an expiratory anaesthetic gas concentration, a pharmacokinetic/pharmacodynamic modelled effect site concentration, a plasma concentration, electrocardiogram data, an approximate entropy of a heart rate variability, an ordinal permutation entropy of a heart rate variability, subject data, and medication data.

7. The method according to claim 1, wherein said step of determining said indicator comprises the step of determining a third sub-indicator based on at least one of a third subset of parameters chosen among said p parameters and a comparison with a third training set of data, said third subset of parameters being different from at least one of said first subset of parameters, and/or said second subset of parameters and/or said third training set being different from at least one of said first training set and said second training set, wherein said third sub-indicator is adapted to characterize deep hypnosis, and further comprises the step of combining said first sub-indicator, said second sub-indicator, and said third sub-indicator to compute said indicator.

8. The method according to claim 7, wherein said third subset comprises at least one of an approximate entropy computed from said electroencephalogram data, an ordinal permutation entropy computed from said electroencephalogram data, and a burst suppression ratio computed from said electroencephalogram data.

9. The method according claim 1, wherein said number p of parameters and/or the type of parameters are chosen in accordance with a type and/or dose of anaesthetic or sedative to be administered to said subject.

10. The method according to claim 1, further comprising a step of recording at least one parameter value among said p parameter values and/or at least one sub-indicator value among said sub-indicator values and/or said indicator value, and/or raw input data, in particular recording at least one time-synchronized predefined event and/or free text comment if said indicator value is detected not to correspond to a clinically evaluated patient state and/or if a predefined event occurs during surgery.

11. The method according to claim 1, further comprising the step of employing at least one of said p parameters and raw input data acquired by monitoring said subject to modify an algorithm upon which said step of determining said indicator is based.

12. A system for quantifying at least one of anaesthesia and a state of vigilance from a plurality of parameters acquired from a subject, said system comprising:

acquisition means adapted to acquire a plurality of p parameters pertaining to said subject, said parameters being selected from at least one of a first parameter group, a second parameter group, and a third parameter group, wherein said first parameter group comprises at least one of electrocardiogram data acquired from said subject, a heart rate, a heart rate variability, a blood pressure, a blood pressure variability, a breathing gas composition, a pharmacokinetic/pharmacodynamic modelled effect site concentration and plasma concentration, wherein said second parameter group comprises at least one of electroencephalogram data and auditory evoked potential data acquired from said subject, and wherein said a third parameter group comprises at least one of subject data and medication data;

wherein p≧

2, and at least one of said p parameters is selected from said first parameter group or said second parameter group;

wherein said acquisition means are adapted to acquire said parameters of at least one of said first parameter group and said second parameter group continuously or at predetermined time intervals; and

calculation means adapted to determine from said p parameters an indicator that quantifies a depth of anaesthesia and/or detects intraoperative awareness and/or quantifies an analgesic component of anaesthesia or a state of vigilance of said subject;

characterized in that said acquisition means are adapted to acquire a number p of parameters that varies while monitoring said subject, and said calculation means are adapted to determine said indicator by combining said number p of parameters that varies while monitoring said subject;

wherein said calculations means are adapted to;

determine a first sub-indicator based on a first subset of parameters chosen among said p parameters and/or based on a comparison with a first training set of data, wherein said first sub-indicator is adapted to characterize a first aspect of an anaesthesia or vigilance state of said subject;

determine a second sub-indicator based on at least one of a second subset of parameters chosen among said p parameters and a comparison with a second training set of data, said second subset of parameters being different from said first subset of parameters and/or said second training set of data being different from said first training set of data, wherein said second sub-indicator is adapted to characterize a second aspect of an anaesthesia or vigilance state of said subject; and

combine said first sub-indicator and said second sub-indicator to compute said indicator.

13. The system according to claim 12, wherein at least one of said acquisition means and said calculation means are adapted to implement a method, said method comprising the steps of:

acquiring the plurality of p parameters pertaining to said subject, said parameters being selected from the first parameter group, and/or the second parameter group, and/or the third parameter group; and

determining the indicator from said p parameters.

14. A storage device with computer-readable instructions stored thereon, wherein the computer-readable instructions are adapted to be used on a computer for controlling a system according to claim 12.

15. A method for quantifying at least one of anaesthesia and a state of vigilance from a plurality of parameters acquired from a subject, said method comprising the steps of:

acquiring a plurality of p parameters pertaining to said subject, said parameters being selected from a first parameter group comprising electrocardiogram data acquired from said subject, a heart rate, a heart rate variability, a blood pressure, a blood pressure variability, a breathing gas composition, a pharmacokinetic/pharmacodynamic modelled effect site concentration and/or plasma concentration, and/or a second parameter group comprising electroencephalogram data and/or auditory evoked potential data acquired from said subject, and/or a third parameter group comprising subject data and/or medication data;

wherein p≧

2, and at least one of said p parameters is selected from said first parameter group or said second parameter group;

wherein said parameters of at least one of said first parameter group and said second parameter group are acquired continuously or at predetermined time intervals by monitoring said subject; and

determining an indicator that performs at least one of quantifying a depth of anaesthesia, detecting intraoperative awareness and quantifying an analgesic component of anaesthesia or a state of vigilance of said subject from said p parameters;

wherein said step of determining said indicator comprises a step of combining said p parameters, wherein said number p of parameters is variable while monitoring said subject; and

wherein said step of combining said p parameters comprises the step of employing one or more of an adaptive neuro fuzzy inference system, a neural network, regression, support vector machines and statistical relational machine learning.