Multilevel Ventilator

• US 20080257349A1
• Filed: 05/10/2005
• Published: 10/23/2008
• Est. Priority Date: 05/10/2004
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: Breas Medical Ab (Shanghai Fosun Pharmaceutical (Group) Co., Ltd.)
• Inventors: Hedner, Jan, Tiedje, Mikael, Knagenhjelm, Petter, Grote, Lutger
• Application Number:
  US11/568,920
• Publication Number:
  US 20080257349A1
• Time in Patent Office: Days
• US Class Current: 128/204.23
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/083 Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61M 16/026 specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis
• CPC Class Codes: A61M 2205/3375 Acoustical, e.g. ultrasonic, measuring means
• CPC Class Codes: A61M 2205/3553 remote, e.g. between patient's home and doctor's office
• CPC Class Codes: A61M 2205/3584 using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/10 Electroencephalographic signals
• CPC Class Codes: A61M 2230/18 Rapid eye-movements [REM]
• CPC Class Codes: A61M 2230/205 partial oxygen pressure (P-O2)
• CPC Class Codes: A61M 2230/30 Blood pressure A61M2230/04 takes precedence
• CPC Class Codes: A61M 2230/432 partial CO2 pressure (P-CO2)
• CPC Class Codes: A61M 2230/435 partial O2 pressure (P-O2)
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A method and apparatus for detecting breathing disorders and controlling a breathing ventilator using an artificial neural network working on signals indicative of a patient'"'"'s physiological status. Preferably the artificial neural network works on a signal indicative of the pressure or flow of supplied breathing gas divided into two or more intervals (401, 402, 403, 404) indicative of patient (1) breathing cycle phases.

Claims

1. An apparatus for the detection of hypoventilation events and for controlling breathing assistance patterns employing an artificial neural network system, comprising:

one or several sensor input channels for periodic sampling of data consistent with a patient'"'"'s physiological status computational means comprising an artificial neural network system, for direct or indirect analysis of said data, for determining control parameters for control of breathing gas pressure in a breathing assisting apparatus, said sampled data related to a flow or pressure curve being divided into two or more intervals indicative of patient breathing cycle phases prior to being fed to said artificial neural network.

2. The apparatus according to claim 1, wherein said artificial neural network system comprises one or several independent artificial neural networks each determining different response parameters.

3. The apparatus according to claim 2, wherein said determined response parameters comprise one or several of inspiratory positive airway pressure (IPAP) level, expiratory positive airway pressure (EPAP) level, pressure limit, pressure support ventilation (PSV) level, tidal volume (V_T) level, continuous positive airway pressure (CPAP), positive end expiratory pressure (PEEP), fractional inhaled oxygen concentration (FiO2) level, breathing gas flow rate level, and minute ventilation level.

4. The apparatus according to claim 2, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actugraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrooculargrams (EOG), electrocardiogram (ECG) signals, blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

19. The apparatus of claim 1, wherein the patient'"'"'s physiological status is related to a breathing status.

5. A method for the detection of hypoventilation events and controlling breathing assistance patterns in a breathing gas ventilator system employing an artificial neural network system, comprising:

periodically acquiring sampled input parameters indicative of patient physiological status;

dividing a flow or pressure curve, obtained from said sampled input parameters, into two or more intervals indicative of patient breathing cycle phases;

finding a hypoventilation event directly or indirectly from said input parameters (5, 6, 7, 8, 9) and/or said divided flow or pressure curve using the artificial neural network system;

determining an appropriate response from predetermined criteria for a breathing gas ventilator apparatus using the result from said artificial neural network system; and

generating output control signals indicative of control settings for a mechanical ventilator apparatus for controlling said breathing gas ventilator apparatus according to said determined response.

6. The method according to claim 5, wherein said artificial neural network system comprises several independent artificial neural networks each determining different response parameters.

7. The method according to claim 5, wherein said artificial neural network system comprises one artificial neural network determining multiple response parameters.

8. The method according to claim 6, wherein one of said artificial neural networks is used to determine, an inspiratory positive airway pressure (IPAP) level for assisting the patient inhalation process.

9. The method according to claim 6, wherein one of said artificial neural networks is used to determine, an expiratory positive airway pressure (EPAP) level for assisting the patient exhalation process.

10. The method according to claim 6, wherein each of said artificial neural networks is used to determine one or several of:

a frequency of applied breathing gas duty cycle pressure change;

an airway pressure level duration time;

a pressure change cycle pattern for use in determining appropriate pressure settings and initiation of said pressure change cycle pattern;

a pressure change cycle pattern for use in response of a determined status of the patient (1) and initiation of said pressure change cycle pattern;

a pressure limit;

a pressure support ventilation (PSV) level;

a tidal volume (V_T) level;

a continuous positive airway pressure (CPAP);

a positive end expiratory pressure (PEEP);

a fractional inhaled oxygen concentration (FiO2) level;

a breathing gas flow rate level; and

a minute ventilation level.

11. The method according to claim 7, characterized in that said artificial neural network (301) is used to determine one or several control parameters of:

an inspiratory positive airway pressure (IPAP) level;

an expiratory positive airway pressure (EPAP) level;

a frequency breathing duty cycle of applied breathing gas pressure;

an airway pressure level duration time;

a pressure change cycle for use in determining appropriate pressure settings and initiation of said pressure change cycle; and

a pressure change cycle for use in response of a determined status of the patient (1) and initiation of said pressure change cycle;

a pressure limit;

a pressure support ventilation (PSV) level;

a tidal volume (V_T) level;

a continuous positive airway pressure (CPAP);

a positive end expiratory pressure (PEEP);

a fractional inhaled oxygen concentration (FiO2) level;

a breathing gas flow rate level; and

a minute ventilation level.

12. The method according to claim 5, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

13. The method according to claim 6, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

14. The method according to claim 7, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

15. The method according to claim 8, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

16. The method according to claim 9, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

17. The method according to claim 10, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.

18. The method according to claim 11, wherein said periodically sampled input parameters comprise signals indicative of one or several of gas flow, gas pressure in airway or mask system, muscle tone derived by actigraphy or electromyography (EMG) signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, electrooculargrams (EOG), blood pressure, O₂ level, CO₂ level, SpO2 (oxygen saturation) level, eye movement, skin circulation, and sound events indicative of a breathing disorder.