Multi-modal cardiac diagnostic decision support system and method

• US 20030093003A1
• Filed: 09/12/2002
• Published: 05/15/2003
• Est. Priority Date: 09/29/1999
• Status: Active Grant

• Current Assignee: 3M Innovative Properties Company (3M Company)
• Original Assignee: Zargis Medical Corp.
• Inventors: Watrous, Raymond, Reichek, Nathaniel
• Granted Patent: US 6,953,436 B2
• 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 5/30 Input circuits therefor
• CPC Class Codes: A61B 5/308 for electrocardiography [ECG]
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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 5/7285 for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• 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
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

A method for extracting features from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, and extracting physiologically significant features from the cardiac acoustic signal using a neural network. A method for evaluating cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, analyzing the cardiac acoustic signal with a wavelet decomposition to extract time-frequency information, and identifying basic heart sounds using neural networks applied to the extracted time-frequency information. A method for determining cardiac event sequences from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, and processing a sequence of features extracted from the cardiac acoustic signal by a probabilistic finite-state automation to determine a most probable sequence of cardiac events given the cardiac acoustic signal. A method for extracting findings from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, processing the cardiac acoustic signal to determine a most probable sequence of cardiac events given the cardiac acoustic signal, and extracting the clinical findings from the sequence of cardiac events. A method for determining a status of heart murmurs includes the steps of obtaining a cardiac acoustic signal, detecting a murmur, if any, from the cardiac acoustic signal, and determining whether the murmur is one of functional and pathological based upon expert rules.

Claims

1. A method for extracting features from cardiac acoustic signals, comprising the steps of:

obtaining a cardiac acoustic signal; and

extracting physiologically significant features from the cardiac acoustic signal, using a neural network.

2. The method according to claim 1, wherein the physiologically significant features correspond to at least one of basic heart sounds and components of the basic heart sounds.

3. A method for evaluating cardiac acoustic signals, comprising the steps of:

obtaining a cardiac acoustic signal;

analyzing the cardiac acoustic signal with a wavelet decomposition to extract time-frequency information; and

identifying basic heart sounds using neural networks applied to the extracted time-frequency information.

4. A method for determining cardiac event sequences from cardiac acoustic signals, comprising the steps of:

obtaining a cardiac acoustic signal; and

processing a sequence of features extracted from the cardiac acoustic signal by a probabilistic finite-state automation to determine a most probable sequence of cardiac events given the cardiac acoustic signal.

5. The method according to claim 4, wherein the probabilistic finite-state automation is a hidden markov model.

6. A method for extracting clinical findings from cardiac acoustic signals, comprising the steps of:

obtaining a cardiac acoustic signal;

processing the cardiac acoustic signal to determine a most probable sequence of cardiac events given the cardiac acoustic signal; and

extracting the clinical findings from the sequence of cardiac events.

7. The method according to claim 6, further comprising the step of determining whether to refer the patient for echocardiography, based upon the extracted clinical findings.

8. The method according to claim 6, further comprising the step of determining whether to refer the patient for further treatment by a cardiac specialist, based upon the extracted clinical findings.

9. The method according to claim 6, wherein the clinical findings correspond to findings which are typically derived from auscultation of a heart.

10. The method according to claim 6, wherein the clinical findings correspond to basic heart sounds and murmurs.

11. The method according to claim 10, wherein the clinical findings comprise at least one of present intensity, intensity profile, duration, time-alignment, and sequence and spectral quality features.

12. A method for determining the presence of cardiac diseases, comprising the steps of:

obtaining a cardiac acoustic signal of a patient;

processing the cardiac acoustic signal to find evidence of cardiac diseases;

receiving data corresponding to a medical record of the patient; and

providing a diagnostic recommendation corresponding to a probability of the cardiac diseases being present in the patient, based upon an analysis of the evidence and data using Bayesian networks.

13. The method according to claim 12, further comprising the step of determining whether to refer the patient for echocardiography, based upon the diagnostic recommendation.

14. The method according to claim 12, further comprising the step of determining whether to refer the patient for further treatment by a cardiac specialist, based upon the diagnostic recommendation.

15. A method for determining a status of heart murmurs, comprising the steps of:

obtaining a cardiac acoustic signal;

detecting a murmur, if any, from the cardiac acoustic signal; and

determining whether the murmur is one of functional and pathological, based upon expert rules.

16. A system for extracting features from cardiac acoustic signals, comprising:

a sensor adapted to obtain a cardiac acoustic signal; and

a neural network adapted to extract physiologically significant features from the cardiac acoustic signal.

17. The system according to claim 16, wherein the physiologically significant features correspond to at least one of basic heart sounds and components of the basic heart sounds.

18. A system for evaluating cardiac acoustic signals, comprising:

a sensor adapted to obtain a cardiac acoustic signal;

a wavelet decomposition circuit adapted to analyze the cardiac acoustic signal to extract time-frequency information; and

a neural network adapted to identify basic heart sounds from the extracted time-frequency information.

19. A system for determining cardiac event sequences from cardiac acoustic signals, comprising:

a sensor adapted to obtain a cardiac acoustic signal; and

a probabilistic finite-state automation adapted to process a sequence of features extracted from the cardiac acoustic signal to determine a most probable sequence of cardiac events given the cardiac acoustic signal.

20. The system according to claim 19, wherein system probabilistic finite-state automation is a hidden markov model.

21. A system for extracting clinical findings from cardiac acoustic signals, comprising the steps of:

a sensor adapted to obtain a cardiac acoustic signal;

a sequence interpreter adapted to process the cardiac acoustic signal to determine a most probable sequence of cardiac events given the cardiac acoustic signal; and

an extraction circuit adapted to extract the clinical findings from the sequence of cardiac events.

22. The system according to claim 21, further comprising a diagnostic decision support circuit adapted to determine whether to refer the patient for echocardiography, based upon the extracted clinical findings.

23. The system according to claim 21, further comprising a diagnostic decision support circuit adapted to determine whether to refer the patient for further treatment by a cardiac specialist, based upon the extracted clinical findings.

24. The system according to claim 21, wherein the clinical findings correspond to findings which are typically derived from auscultation of a heart.

25. The system according to claim 21, wherein the clinical findings correspond to basic heart sounds and murmurs.

26. The system according to claim 25, wherein the clinical findings comprise at least one of present intensity, intensity profile, duration, time-alignment, and sequence and spectral quality features.

27. A system for determining the presence of cardiac diseases, comprising:

a sensor adapted to obtain a cardiac acoustic signal of a patient;

an acoustic signal processor adapted to process the cardiac acoustic signal to find evidence of cardiac diseases;

an input device adapted to receive data corresponding to a medical record of the patient; and

a diagnostic decision support circuit adapted to provide a diagnostic recommendation corresponding to a probability of the cardiac diseases being present in the patient, based upon an analysis of the evidence and data using Bayesian networks.

28. The system according to claim 27, wherein said support circuit is further adapted to determine whether to refer the patient for echocardiography, based upon the diagnostic recommendation.

29. The system according to claim 27, wherein said support circuit is further adapted to determine whether to refer the patient for further treatment by a cardiac specialist, based upon the diagnostic recommendation.

30. A system for determining a status of heart murmurs, comprising:

a sensor adapted to obtain a cardiac acoustic signal;

an acoustic signal processor adapted to detect a murmur, if any, from the cardiac acoustic signal; and

a diagnostic decision support system adapted to determine whether the murmur is one of functional and pathological, based upon expert rules.