Multi parametric classification of cardiovascular sounds

• US 20100145210A1
• Filed: 06/26/2006
• Published: 06/10/2010
• Est. Priority Date: 06/26/2006
• Status: Abandoned Application

• Current Assignee: Coloplast A/S
• Original Assignee: Coloplast A/S
• Inventors: Schmidt, Samuel, Struijk, Johannes, Graff, Claus
• Application Number:
  US12/308,755
• Publication Number:
  US 20100145210A1
• Time in Patent Office: Days
• US Class Current: 600/528
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• 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: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present application relates to a method for classifying a cardiovascular sound recorded from a living subject. The method comprises the step of extracting at least two signal parameters (309) from said cardiovascular sound, said at least two signal parameters characterizes at least two different properties of at least a part of said cardiovascular sound. The method further comprises the step of classifying said cardiovascular sound using said at least two signal parameters in a multivariate classification method (310). Furthermore, the application relates to a system, stethoscope and server for classifying a cardiovascular sound recorded from a living subject, where the above-described method has been implemented.

Claims

1. A method for classifying a cardiovascular sound recorded from a living subject, said method comprises the steps of:

extracting at least two signal parameters from said cardiovascular sound, said at least two signal parameters characterizes at least two different properties of at least a part of said cardiovascular sound,

classifying said cardiovascular sound using said at least two signal parameters in a multivariate classification method.

2. A method according to claim 1 characterized in that at least one of said at least two signal parameters is a frequency parameter describing a property in the frequency domain of at least a part of said cardiovascular sound.

3. A method according to claim 1 characterized in that at least one of said at least two signal parameters describing a property in the time domain of at least a part of said cardiovascular sound.

4. A method according to claim 2 characterized in that at least one of said frequency parameters is a frequency level parameter describing a frequency level property of at least a part of said cardiovascular sound.

5. A method according to claim 2 characterized in that at least one of said at least two signal parameters is a frequency bandwidth parameter describing a frequency bandwidth property of at least a part of said cardiovascular sound.

6. A method according to claim 4 characterized in that at least one of said frequency level properties characterizes the most powerful frequency component of at least a part of said cardiovascular sound.

7. A method according to claim 5 characterized in that at least one of said frequency bandwidth properties characterizes the bandwidth of the most powerful frequency component of at least a part of said cardiovascular sound.

8. A method according to claim 3 characterized in that at least one of said time parameters is a property characterizing the mobility of at least a part of said cardiovascular sound.

9. A method according to claim 1 characterized in that said method further comprises the step of dividing said cardiovascular sound into at least one sub-segment and at least one of said signal parameters is extracted from said at least one sub-segment.

10. A method according to claim 1 characterized in that said method further comprises the step of modelling at least a part of said cardiovascular sound and at least one of said signal parameters is extracted from said model.

11. A method according to claim 1 characterized in that said multivariate classification method is a discriminant function.

23. A computer-readable medium having stored therein instructions for causing a processing unit to execute a method according to claim 1.

24. A stethoscope comprising:

recording means adapted to record a cardiovascular sound from a living subject,

storing means adapted to store said recorded cardiovascular sound,

a computer-readable medium and a processing unit, said computer-readable medium having stored therein instructions for causing said processing unit to execute a method according to claim 1 and thereby classify said recorded cardiovascular sound.

25. A server device connected to a communication network comprising:

receiving means adapted to receive a cardiovascular sound recorded from a living subject through said communication network,

storing means adapted to store said received cardiovascular sound,

a computer-readable medium and a processing unit, said computer-readable medium having stored therein instructions for causing said processing unit to execute a method according to claim 1 and thereby classify said received cardiovascular sound.

26. A server device according to claim 25 characterized in that said receiving means are further adapted to receive said cardiovascular sound from a client connected to said communication network.

27. A server device according to claim 25 characterized in that said server device further comprises means for sending said classification of said cardiovascular sound to at least one client unit connected to said communication network.

12. A system for classifying a cardiovascular sound recorded from a living subject, said system comprises:

processing means for extracting at least two signal parameters from said cardiovascular sound, said at least two signal parameters characterizes at least two different properties of at least a part of said cardiovascular sound,

processing means for classifying said cardiovascular sound using said at least two signal parameters using a multivariate classification method.

13. A system according to claim 12 characterized in that said processing means for extracting at least two signal parameters from said cardiovascular sound is adapted to extract at least one frequency parameter describing a property in the frequency domain of at least a part of said cardiovascular sound.

14. A system according to claim 12 characterized in that said processing means for extracting at least two signal parameters from said cardiovascular sound is adapted to extract at least one time parameter describing a property in the time domain of at least a part of said cardiovascular sound.

15. A system according to claim 13 characterized in that said processing means adapted to extract at least one of said frequency parameters are further adapted to extract at least one frequency level parameter describing a frequency level property of at least a part of said cardiovascular sound.

16. A system according to claim 13 characterized in that said processing means adapted to extract at least one frequency parameter are further adapted to extract at least one frequency bandwidth parameter describing a frequency bandwidth property of at least a part of said cardiovascular sound.

17. A system according to claim 13 characterized in that said processing means adapted to extract at least one frequency level property are further adapted to extract the most powerful frequency component of at least a part of said cardiovascular sound.

18. A system according to claim 13 characterized in that said processing means adapted to extract at least one of said frequency bandwidth properties are further adapted to extract the bandwidth of the most powerful frequency component of at least a part of said cardiovascular sound.

19. A system according to claim 14 characterized in that said processing means for extracting at least one time parameters are further adapted to extract the mobility of at least a part of said cardiovascular sound.

20. A system according to claim 12 characterized in that said system further comprises processing means for dividing said cardiovascular sound into at least one sub-segment and at least one of said signal parameters is extracted from said at least one sub-segment.

21. A system according to claim 12 characterized in that said system further comprises processing means for modelling at least a part of said cardiovascular sound and in that said processing means for extracting at least two signal parameters from said cardiovascular sound are further adapted to extract at least one of said parameters from said model.

22. A system according to claim 12 characterized in that said multivariate classification method used by said processing means for classification of said cardiovascular sound is a discriminant function.