SYSTEM FOR ACOUSTIC IDENTIFICATION OF OBSTRUCTION TYPES IN SLEEP APNOEA, AND CORRESPONDING METHOD

Abstract

The present invention relates to a classification system (1) for microprocessor-assisted identification of obstruction types (O1-O4) in sleep apnoea by means of appropriate classification of a snoring-noise signal (Au) to be analysed. The system comprises: a) an input interface for each snoring-noise signal (Au); b) a first classifier (K1) which can be trained such that it identifies and outputs the most probable type of snoring-noise origin (S1-S4) for a particular snoring-noise signal (Au); c) a second classifier (K2) which can be trained such that it identifies and outputs the most probable mouth position (M1-M2) for a particular snoring-noise signal (Au); and d) a third classifier (K3) or linkage matrix, which is designed to identify and output the most probable obstruction type (O1-O4) from the snoring-noise signal (Au) to be analysed, the determined type of snoring-noise origin (S1-S4) and the mouth position (M1-M2) determined therefor.

Claims

1. Classification system for microprocessor-supported identification of obstruction types in sleep apnoea by means of appropriate classification of a snoring-noise signal to be examined, comprising: a) an input interface for the respective snoring-noise signal; b) a first classifier adapted to learn in a training mode, when a first plurality of snoring-noise signals is input with a corresponding type of snoring-noise origin, such that in an identification mode, it identifies and outputs the most probable type of snoring-noise origin for a particular snoring-noise signal from a group of predefined types of snoring-noise origin; c) a second classifier adapted to learn in a training mode, when a second plurality of snoring-noise signals is input with a corresponding mouth position, such that in an identification mode, it identifies and outputs the most probable mouth position for a particular snoring-noise signal from a group of predefined mouth positions; d) a third classifier adapted to identify in an identification mode, when the type of snoring-noise origin identified by the first classifier and the mouth position identified by the second classifier are input, from a group of predefined obstruction types the most probable obstruction type and output it as an obstruction type signal; and e) an output interface to a display for the obstruction-type signal.

2. The classification system according to claim 1, the first classifier and the second classifier being adapted such that the respective training of the first and of the second classifier with a plurality of snoring-noise signals can be performed separately from one another, with the first classifier training and learning independently of the mouth position and the second classifier independently of the type of snoring-noise origin.

3. The classification system according to claim 1, the first and the second classifier being adapted such that the respective training of the first and the second classifier with an additional plurality of snoring-noise signals takes place together and simultaneously, the respective snoring-noise signal used including the respective type of snoring-noise origin and the respective mouth position as corresponding information.

4. The classification system according to claim 1, the first classifier being adapted to identify, indicate and forward to the third classifier, in the identification mode, the respective type of snoring-noise origin with a respective probability.

5. The classification system according to claim 1, the second classifier being adapted to identify, indicate and forward to the third classifier, in the identification mode, the respective mouth position with a respective probability.

6. The classification system according to claim 1, the third classifier being adapted to learn in a training mode, when the type of snoring-noise origin identified by the first classifier, the mouth position identified by the second classifier and an obstruction type are input, such that in the identification mode, it identifies the input obstruction type as the most probable obstruction type with the respective type of snoring-noise origin and the respective mouth position.

7. The classification system according to claim 1, the third classifier being adapted, in an identification mode, to identify, indicate and forward to the output interface the respective obstruction type with a respective probability.

8. The classification system according to claim 1, the third classifier being adapted to identify, in addition to the respective type of snoring-noise origin and the respective mouth position, other snoring or patient data associated with the snorer via an input interface and, in the training mode and/or in the identification mode, take them into account as parameters or parameter signals in classifying the obstruction type.

9. The classification system according claim 8, the snoring or patient data comprising at least one of the following parameters: body mass index, apnoea hypopnoea index, size of tonsils, size of tongue, Friedman score, time of snoring, duration of sleep.

10. The classification system according to claim 1, the first classifier being based on one of the following methods of machine learning: Support Vector Machine—SVM—, Naïve-Bayes-System, Least Mean Square method, k-Nearest Neighbours method—k-NN—, Linear Discriminant Analysis—LDA—, Random Forests method—RF—, Extreme Learning Machine—ELM—, Multilayer Perceptron—MLP—, Deep Neural Network—DNN—, logistic regression.

11. The classification system according to claim 1, wherein the second classifier is based on one of the following methods of machine learning: Support Vector Machine—SVM—, Naïve-Bayes-System, Least Mean Square method, k-Nearest Neighbours method—k-NN—, Linear Discriminant Analysis—LDA—, Random Forests method—RF—, Extreme Learning Machine —ELM—, Multilayer Perceptron—MLP—, Deep Neural Network—DNN—, logistic regression.

12. The classification system according to claim 1, wherein the third classifier is based on one of the following methods of machine learning: Support Vector Machine—SVM—, Naïve-Bayes-System, Least Mean Square method, k-Nearest Neighbours method—k-NN—, Linear Discriminant Analysis—LDA—, Random Forests method—RF—, Extreme Learning Machine—ELM—, Multilayer Perceptron—MLP—, Deep Neural Network—DNN—, logistic regression.

13. The classification system according to claim 1, wherein the third classifier is based on a matrix probability assessment of a first input vector of the types of snoring-noise origin and of at least one second input vector of the mouth positions, whose summary probabilities in turn result in the various obstruction types and their probabilities.

14. A method for a microprocessor-supported identification of obstruction types in case of sleep apnoea by classification of a recorded snoring-noise signal to be examined, comprising the following steps: A) training of a first classifier by inputting at its input port a first plurality of snoring-noise signals to which a respective type of snoring-noise origin is assigned, for classification and output of a respective most probable type of snoring-noise origin in a respective identification mode, the respective type of snoring-noise origin originating from a first group of classes of possible types of snoring-noise origins; B) training of a second classifier by inputting at its input port a second plurality of snoring-noise signals to which a respective mouth position is assigned, for classification and output of a respective most probable mouth position in a respective identification mode, the respective mouth position originating from a second group of classes of possible mouth positions; C) either training or matrix-shaped association of a third classifier by inputting at its input port the types of snoring-noise origin and mouth positions identified above for classification in the corresponding identification mode and output of a most probable obstruction type in case of sleep apnoea, the respective obstruction type originating from a third group of classes of obstruction types, or alternatively using the third classifier being preprogrammed by a parameter input for classification of the most probable obstruction type; D) identifying, in a respective identification mode, the type of snoring-noise origin from the snoring-noise signal by means of the first classifier, the mouth position by means of the second classifier, and the resulting obstruction type by means of the third classifier; and E) outputting the obstruction type for the snoring-noise signal to be examined, which was identified by means of the first, the second and the third classifier, at an output interface.

15. The method according to claim 14, wherein the training of the first classifier and the training of the second classifier with a plurality of the snoring-noise signals take place separately from one another, wherein the first classifier is trained and learns independently of the mouth position and the second classifier is trained independently of the type of snoring-noise origin.

16. The method according to claim 15, wherein the training and learning of the first and the second classifier take place with a time shift.

17. The method according to claim 15, wherein the training and learning of the first and the second classifier take place simultaneously.

18. The method according to claim 14, wherein the training of the first classifier and the training of the second classifier with another plurality of the snoring-noise signals take place together and simultaneously, wherein the type of snoring-noise origin and the respective mouth position being assigned to the respective employed snoring-noise signal.

19. The method according to claim 14, wherein in the identification mode, the respective types of snoring-noise origin are identified by the first classifier with respective probability values and fed to the third classifier.

20. The method according to claim 14, wherein in the identification mode, the respective mouth positions are identified by the second classifier with respective probability values and fed to the third classifier for identification of the obstruction type.

21. The method according to claim 14, wherein in the identification mode, the respective obstruction type is identified by the third classifier from the respective types of snoring-noise origin and mouth positions, with indication of a corresponding probability.

22. The method according to claim 14, wherein the first group of the types of snoring-noise origin comprise the following classes: velopharynx, oropharynx, tongue base area and/or epiglottis area.

23. The method according to claim 22, wherein the respective type of snoring-noise origin includes an orientation of the vibration, which is a lateral or a circular vibration.

24. The method according to claim 14, wherein the second group of mouth positions comprises the following mouth positions: mouth open, mouth closed.

25. The method according to claim 14, wherein the second group of mouth positions include mouth positions: mouth open, mouth closed, and intermediate mouth positions.

26. The method according to claim 14, wherein in addition to the respective type of snoring-noise origin and the respective mouth position additional snoring or patient data associated with the snorer are fed to the third classifier, which snoring or patient data are taken into account and evaluated by the third classifier during training and/or identification of the obstruction type.

27. The method according to claim 14, wherein the snoring or patient data comprise at least one of the following parameters: body mass index, apnoea hypopnoea index, size of tonsils, size of tongue, Friedman score, time of snoring, duration of sleep.

Description

[0059] Preferred embodiments of the present invention are described in the following figures and in a detailed description; however, they are not intended to limit the present invention thereto:

[0060] FIG. 1 schematically shows a classification system with a first and a second classifier to each of which a snoring-noise signal with a corresponding optional indicator is fed via an input interface, the output signals of the first and of the second classifier being fed to a third classifier for classification; the output signals of the third classifier which represent obstruction types are forwarded to a display unit via an output interface and are displayed there; additional snoring or patient data can be input via an input interface and fed to the third classifier for classification;

[0061] FIG. 2 shows a sectional lateral view of a patient's head including the neck-nose-throat area with the areas velopharynx, oropharynx, tongue base and epiglottis; and

[0062] FIG. 3 shows a signal flow diagram of a method for determining the respective most probable obstruction type or of probabilities of the respective obstruction type from the snoring-noise signal, including the optional indicators for purposes of training of the first and the second classifier.

DETAILED DESCRIPTION OF EMBODIMENTS

[0063] FIG. 1 schematically shows a possible embodiment of a classification system 1 for microprocessor-supported identification of obstruction types O1-O4 which can occur during sleep apnoea and are identified by classification system 1 from a snoring-noise signal Au to be examined. The classification system 1 comprises the following components:

[0064] A) an input interface for the respective snoring-noise signal Au which can have analog and/or digital inputs. For training the classification system 1, the snoring-noise signal Au has at least one additional indicator or a label with a type of snoring-noise origin S1-S4 and/or a mouth position M1-M2 which is assigned to the respective snoring-noise signal Au. Preferably, the snoring-noise signal Au also has an obstruction type O1-O4 as indicator which can be used for training the classification system 1. The input interface can generally also have an input for a keyboard, a button, an optical input or scanner or the like in order to record and forward the indicator(s) or labels.

[0065] B) a first classifier K1 adapted to learn in a training mode, when a first plurality of snoring-noise signals Au with a corresponding type of snoring-noise origin S1-S4 is input, such that in an identification mode, it can identify and output the most probable type of snoring-noise origin S1-S4 for a respective snoring-noise signal Au from a group of predefined types of snoring-noise origin S1-S4. Thus, the first classifier is a learning classifier. For clarity, if the snoring-noise signals Au of the training data were entered in the identification mode, the corresponding types of snoring-noise origin S1-S4 would be output correctly or at least on average with highest probability, with the preferred classifiers described above. If subsequently in the identification mode the snoring-noise signal Au to be examined is input, the most probable type of snoring-noise origin S1-S4 or the types of snoring-noise origin S1-S4 are determined as probability values and forwarded to a third classifier K3. [0066] C) a second classifier K2 adapted to learn, when a second plurality of snoring-noise signals Au is input with a corresponding mouth position M1-M2 in the respective training mode, that in the identification mode, it identifies and outputs the corresponding most probable mouth position M1-M2 from a group of predefined mouth positions M1-M2 for the corresponding snoring-noise signal Au. The second classifier thus is a learning classifier as well. If subsequently in the identification mode the snoring-noise signal Au to be examined is input, the most probable mouth position M1-M2 or the mouth positions M1-M2 are determined as probability values and forwarded to the third classifier K3. [0067] D) the third classifier K3 which is adapted to identify in an identification mode, when the type of snoring-noise origin S1-S4 identified by the first classifier K1 and the mouth position M1-M2 identified by the second classifier K2 are input, the most probable obstruction type O1-O4 of a group of predefined obstruction types O1-O4 and to output it as an obstruction type signal.

[0068] Preferably, the third classifier K3 is adapted to learn in a training mode, when the type of snoring-noise origin S1-S4 identified by the first classifier K1, the mouth position M1-M2 identified by the second classifier K2 and an obstruction type O1-O4 are input, such that in the identification mode, it will identify, for the respective type of snoring-noise origin S1-S4 and the respective mouth position M1-M2, the input obstruction type O1-O4 as the most probable obstruction type O1-O4.

[0069] Preferably, the third classifier K3 is adapted to learn in a training mode, when the type of snoring-noise origin S1-S4 identified by the first classifier K1, the mouth position M1-M2 identified by the second classifier K2 and an obstruction type O1-O4 are input, such that it will recognize, in the identification mode, the input obstruction type O1-O4 as the most probable obstruction type O1-O4 for the respective type of snoring-noise origin S1-S4 and mouth position M1-M2.

[0070] If the snoring-noise signal Au to be examined is input in the identification mode, the types of snoring-noise origin S1-S4 identified by the first classifier K1 and the mouth positions M1-M2 identified by the second classifier K2 are assigned to the most probable obstruction type(s) O1-O4 with corresponding probability values. The third classifier K3 can also be a connection matrix which, as described above, performs a precisely defined probability assessment by means of input parameters such as at least the types of snoring-noise origin S1-S4 and the mouth positions M1-M2. During this process, the connection matrix can also be adapted, by means of an implemented or subordinated learning algorithm, such that the precisely predefined probability assessment is preferably further learned before an identification mode or during continuous identification in a training mode; and

[0071] E) an output interface 3 with a display for the obstruction type signal.

[0072] Preferably, the classification system 1 also has an input interface 2 by means of which the additional snoring and patient data Px can be input which are, for instance, taken into account by the third classifier K3 during classification of the respective obstruction type O1-O4.

[0073] For purposes of clarity, it is noted that by the type(s) of snoring-noise origin S1-S4, the mouth position(s) M1-M2 and the obstruction type(s) O1-O4, signals may be intended where they have signal properties.

[0074] Preferably, an identification precision is determined by means of annotated test data. Preferably, the test data are an independent part of the training data set which, however, was not used for training.

[0075] Preferably, the snoring-noise signal Au is a signal or a signal vector comprising a microphone or audio signal representing the snoring-noise signal and one or more characteristics signals. The microphone or audio signal representing the snoring-noise signal can be preprocessed in various ways, for instance by bandpass filtering or as known in the state of the art.

[0076] Alternatively preferably, the snoring-noise signal Au is a characteristics vector obtained from the audio signal by means of a characteristics extractor, consisting of at least one or more acoustic characteristics. The acoustic characteristics can for instance be a fundamental frequency, a harmonic-noise-ratio—HNR—, Mel-Frequency Cepstral Coefficient—MFCC— and/or others. The characteristics extractor preferably extracts instead of an individual value per characteristic which describes an entire time period of a snoring event, information on a chronological history of the acoustic characteristics which are preferably presented as static values. The static values are preferably an average value, a median value, a standard deviation and/or a Gauss distribution.

[0077] A method suitable for the classification system described above for microprocessor-supported identification of the obstruction types O1-O4 in case of sleep apnoea by classification of the recorded snoring-noise signal Au to be examined comprises the following steps: [0078] A) training of a first classifier K1 by inputting at its input port a first plurality of snoring-noise signals Au to which a respective type of snoring-noise origin S1-S4 is assigned, for classification and output of the respective most probable type of snoring-noise origin S1-S4 in a respective identification mode, the respective type of snoring-noise origin S1-S4 originating from a first group of classes of the possible types of snoring-noise origin S1-S4; [0079] B) training of a second classifier K1 by inputting at its input port a second plurality of snoring-noise signals Au to which a respective mouth position M1-M2 is assigned, for classification and output of the respective most probable mouth position M1-M2 in a respective identification mode, the respective mouth position M1-M2 originating from a second group of classes of the possible mouth positions M1-M2; [0080] C) preferably training or matrix-shaped association of a third classifier K3 by inputting at its input port the types of snoring-noise origin S1-S4 and mouth positions M1-M2 identified above for classification in the corresponding identification mode and output of the most probable obstruction type O1-O4 in case of sleep apnoea, the respective obstruction type O1-O4 originating from a third group of classes of the obstruction types O1-O4; alternatively, the third classifier K3 can also be preprogrammed by a parameter input for classification of the most probable obstruction type O1-O4; [0081] D) identifying, in the respective identification mode, the type of snoring-noise origin S1-S4 from the snoring-noise signal Au by means of the first classifier K1, the mouth position M1-M2 by means of the second classifier K2, and the resulting obstruction type O1-O4 by means of the third classifier K3; and [0082] E) outputting the obstruction type O1-O4 for the snoring-noise signal Au to be examined, which was identified by means of the first K1, the second K2 and the third classifier K3, at an output interface 3. [0083] FIG. 3 shows an example of the method described above as a signal flow diagram.

[0084] Preferably, the method described above also comprises the following, wherein training of the first classifier K1 and training of the second classifier K2 with a plurality of the snoring-noise signals Au take place separately from one another, wherein the first classifier K1 being trained and learning independently of the mouth position M1-M2 and the second classifier K2 independently of the type of snoring-noise origin S1-S4. Preferably, training and learning of the first K1 and the second classifier K2 take place with a time shift or simultaneously.

[0085] Preferably, the method described above also comprises the following, wherein training of the first classifier K1 and training of the second classifier K2 with another plurality of the snoring-noise signals Au together and simultaneously, the type of snoring-noise origin S1-S4 and the respective mouth position M1-M2 being assigned to the respective employed snoring-noise signal Au.

[0086] Preferably, the method described above also comprises the following, wherein in the identification mode, the respective types of snoring-noise origin S1-S4 are identified by the first classifier K1 and fed to the third classifier K3.

[0087] Preferably, the method described above also comprises the following, wherein in the identification mode, the respective mouth positions M1-M2 are identified by the second classifier K2 and fed to the third classifier K3 for identification of the obstruction type O1-O4.

[0088] Preferably, the method described above also comprises the following, wherein in the identification mode, the respective obstruction type O1-O4 is identified by the third classifier K3 from the respective types of snoring-noise origin S1-S4 and mouth positions M1-M2, with indication of a corresponding probability.

[0089] Preferably, the method described above also comprises the following, wherein the first group of the types of snoring-noise origin S1-S4 comprising the following classes: velopharynx (V), oropharynx (O), tongue base area (T) and/or epiglottis area (E). Preferably, the respective type of snoring-noise origin S1-S4 also includes an orientation of the vibration, which can for instance be a lateral or a circular vibration.

[0090] Preferably, the second group of mouth positions comprises the following mouth positions: mouth open, mouth closed. Alternatively preferably, the second group of mouth positions can include more than two mouth positions with intermediate positions.

[0091] Preferably, the method described above is adapted such that in addition to the respective type of snoring-noise origin S1-S4 and the respective mouth position M1-M2, additional snoring or patient data Px associated with the snorer are fed to the third classifier K3, which data are taken into account and evaluated by the third classifier K3 during training and/or identification of the obstruction type O1-O4.

[0092] Preferably, the snoring or patient data Px comprise at least one of the following parameters: body mass index, apnoea hypopnoea index, size of tonsils, size of tongue, Friedman score, time of snoring, duration of sleep.

[0093] For purposes of clarity, it is noted that the indefinite article “a” in connection with an object does not limit the number of objects to exactly “one”, but that “at least one” is intended. This shall apply to all indefinite articles for example “a” etc.

[0094] For purposes of clarity, the terms “first”, “second”, “third” etc. as used herein are only employed to distinguish different pluralities, elements and/or components. Therefore, for instance, a first plurality can also be termed as second plurality, and consequently the second plurality can also be termed first plurality without deviating from the teachings of the present invention.

[0095] It is understood that instead of the two or four classes mentioned herein of types of snoring-noise origin, mouth positions and obstruction types, other pluralities can be used or detected as well.

[0096] The reference signs indicated in the Claims are only for better comprehensibility and do not limit the Claims in any way to the embodiments shown in the Figures.

LIST OF REFERENCE SIGNS

[0097] 1 classification system

[0098] 2 input interface

[0099] 3 output interface

[0100] Au snoring-noise signal

[0101] Sx, S1-S4 type of snoring-noise origin

[0102] Mx, M1, M2 mouth position

[0103] Ox, O1-O4 obstruction type

[0104] K1 first classifier

[0105] K2 second classifier

[0106] K3 third classifier

[0107] Px snoring or patient data

[0108] V velopharynx

[0109] O oropharynx

[0110] T area of tongue base

[0111] E area of epiglottis