Non-invasive method and apparatus for determining onset of physiological conditions

• US 20040167418A1
• Filed: 03/17/2004
• Published: 08/26/2004
• Est. Priority Date: 02/28/2001
• Status: Active Grant

• Current Assignee: University of Technology at Sydney
• Original Assignee: University of Technology at Sydney
• Inventors: Nguyen, Hung, Ghevondian, Nejhdeh
• Granted Patent: US 7,450,986 B2
• Time in Patent Office: Days
• US Class Current: 600/513
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The invention relates to the modelling and design of early warning systems for detecting medical conditions using physiological responses. The device comprises sensors for monitoring physiological parameters such as skin impedance, heart rate, and QT interval of a patient, means for establishing when those parameters change, the rate of change of the parameters, and a neural network processor for processing the information obtained by the sensors. The neural network processor is programmed with a fast learning algorithm. When the neural network establishes that a physiological condition is present in the patient an alarm signal will be generated. The invention extends to a method of non-invasive monitoring of a person using a neural network programmed with a fast learning algorithm. A non-invasive hypoglycaemia monitor is specifically described.

Claims

1. A non-invasive method of determining the value, presence or onset of a physiological condition in a person comprising the steps of:

continuously monitoring two or more of at least the following parameters of the patient;

skin impedance, heart rate, QT interval, and mean or peak frequency of the alpha wave;

establishing whether one or more of those monitored parameters changes, and if so, the rate of change of that parameter or parameters;

feeding data obtained in the first two steps into a neural network processor programmed with a fast learning algorithm; and

causing a signal to be generated when said neural network establishes conditions which suggest the presence or imminent onset of said physiological condition, or has estimated the value of said physiological condition.

2. A non-invasive method according to claim 1 wherein the monitoring of the heart rate and QT interval is done with an ECG.

4. A non-invasive method according to any preceding claim wherein the first learning algorithm has either a magnified gradient function or an optimal gradient function.

5. A non-invasive method according to any one of claims 1 to 3 wherein the fast learning algorithm is a robust sliding mode algorithm.

6. A non-invasive method according to any preceding claim wherein the physiological condition determined is any one of hypoglycaemia, hyperglycaemia, sudden infant death syndrome, chronic stress, sleep disorders, and driver fatigue

3. A non-invasive method according to either preceding claims wherein the monitoring of the alpha wave is done with an EEG.

7. Apparatus for generating an alarm when a physiological condition is present or imminent in a person, said apparatus comprising:

sensors for sensing at least two of the skin impedance, heart rate, QT interval, and mean or peak frequency of the alpha wave;

means for establishing when one or more of the sensed parameters changes and when so established, the rate of change of said changed parameters,

a neural network linked to said sensors and said means so as to receive a substantially continuous data stream from said sensors and said means, the neural network being programed with a fast learning algorithm and adapted to establish when the sensed parameters, and any change to those parameters, for a particular person are such as to indicate the presence or imminent onset of said physiological condition,

and alarm means linked to said neural network adapted to be triggered when the presence or imminent onset of said physiological condition is established.

8. Apparatus according to claim 7 wherein the fast learning algorithm has either a magnified gradient function or an optimal gradient function.

9. Apparatus according to claim 7 wherein the fast learning algorithm is a robust sliding mode algorithm.

10. Apparatus according to any one of claims 7 to 9 wherein an ECG is used to obtain the data for the hear rate and QT interval.

11. Apparatus according to any one of claims 7 to 10 wherein an EEG is used to obtain the data for the mean or peak alpha wave.

12. Apparatus according to any one of claims 7 to 11 wherein data is transmitted between the sensors and the neural network by radio frequency.

13. Apparatus according to any one of claims 7 to 12 wherein the device is adapted to estimate actual value of a physiological condition of a patient.

14. Apparatus according to claim 13 wherein the physiological condition which the device is adapted to estimate is the blood glucose level of the patient.

15. A method substantially as hereinbefore described with reference to the accompanying drawings.