Method and system for diagnosing coronary artery disease (CAD) using a voice signal
The present invention extends to methods, systems, for diagnosing coronary artery disease (CAD) in patients by using their voice signal comprising receiving voice signal data indicative of speech from the patient.
1. A computer-implemented method for diagnosing a patient, comprising:
a. receiving voice signal data indicative of speech from the patient;
b. segmenting the voice signal data into frames of 32 ms with a frame shift of 10 ms;
c. computing Mel Frequency Cepstral Coefficients (MFCC);
d. applying various statistical feature extraction operators upon the MFCC time series and;
e. determining an existence of at least one coronary artery disease symptom associated with the patient;
wherein an existence of at least one coronary artery disease symptom associated with the patient is determined based at least in part upon the ratio of intensity between at least two frequency bands found in the Cepstral representation and/or its statistical properties.
2. The method of claim 1 , wherein the step of computing MFCC is performed by computing a Cepstral representation using any degree of freedom.
3. The method of claim 1 , wherein the Cepstral representation comprises time-series is used for statistical feature extraction.
4. The method of claim 1 , wherein the step of segmenting the voice signal data into frames, further provides a power spectrum density (PSD) and/or its Root Mean Squaring (RMS) spectrogram with any resolution between 1 to 200 frames per second.
5. The method of claim 1 , wherein the step of computing Mel Frequency Cepstral Coefficients (MFCC) from a log scaling function that resemble the human acoustic perception of sounds is achieved by using any number of Mel frequency triangular filter banks.
6. The method of claim 1 , wherein the step of computing Mel Frequency Cepstral Coefficients (MFCC) from a log scaling functions that resemble the human acoustic perception of sound pressure levels is achieved by converting to decibels (DB).
7. The method of claim 1 , wherein for each of the two or more of frequency bands the intensity ratio values is manifested at a given time period.
8. The method of claim 1 , wherein the voice signal data has a finite duration and each time period separating the respective plurality of intensity ratio values is essentially evenly distributed within the duration of the speech.
9. The method of claim 1 , wherein the existence of at least one coronary artery disease symptom associated with the patient is determined based at least in part upon the type of statistical operator function including at least one decay feature.
10. The method of claim 9 , wherein the zero-crossing type operator measure provides an indicator of the severity of the coronary artery disease symptom.
11. The method of claim 9 , wherein the averaging type operator measure provides an indicator of the severity of the coronary artery disease symptom.
12. The method of claim 9 , wherein the maximum type operator measure provides an indicator of the severity of the coronary artery disease symptom.
13. The method of claim 9 , wherein at least one of a height and a width of the crater feature provides an indicator of the severity of the coronary artery disease symptom.
14. The method of claim 1 , wherein the existence of at least one coronary artery disease symptom associated with the patient is determined based at least in part upon the zero-crossing and/or averaging and/or maximum statistical operators including at least one crater feature.
15. A computer-implemented system for diagnosing a patient, the system comprising:
a. one or more processors; and
b. a memory system communicatively coupled to the one or more processors, the memory system comprises executable instructions including:
i. receiving voice signal data indicative of speech from the patient;
ii. segmenting the voice signal data into frames of 32 ms with a frame shift of 10 ms;
iii. computing Mel Frequency Cepstral Coefficients (MFCC);
iv. applying various statistical feature extraction operators upon the MFCC time series and;
v. determining an existence of at least one coronary artery disease symptom associated with the patient;
wherein an existence of at least one coronary artery disease symptom associated with the patient is determined based at least in part upon the ratio of intensity between at least two frequency bands found in the Cepstral representation and/or its statistical properties.
16. The system of claim 15 , wherein the step of computing MFCC is performed by computing a Cepstral representation using any degree of freedom.
17. The system of claim 15 , wherein the Cepstral representation comprises time-series is used for statistical feature extraction.
18. The system of claim 15 , wherein the step of segmenting the voice signal data into frames, further provides a power spectrum density (PSD) and/or its Root Mean Squaring (RMS) spectrogram with any resolution between 1 to 200 frames per second.
19. The system of claim 15 , wherein the step of computing Mel Frequency Cepstral Coefficients (MFCC) from a log scaling function that resemble the human acoustic perception of sounds is achieved by using any number of Mel frequency triangular filter banks.
20. The system of claim 15 , wherein the step of computing Mel Frequency Cepstral Coefficients (MFCC) from a log scaling functions that resemble the human acoustic perception of sound pressure levels is achieved by converting to decibels (DB).
21. The system of claim 15 , wherein for each of the two or more of frequency bands the intensity ratio values is manifested at a given time period.
22. The system of claim 15 , wherein the voice signal data has a finite duration and each time period separating the respective plurality of intensity ratio values is essentially evenly distributed within the duration of the speech.
23. The system of claim 15 , wherein the existence of at least one coronary artery disease symptom associated with the patient is determined based at least in part upon the type of statistical operator function including at least one decay feature.
24. The system of claim 23 , wherein the zero-crossing type operator measure provides an indicator of the severity of the coronary artery disease symptom.
25. The system of claim 23 , wherein the averaging type operator measure provides an indicator of the severity of the coronary artery disease symptom.
26. The system of claim 23 , wherein the maximum type operator measure provides an indicator of the severity of the coronary artery disease symptom.
27. The system of claim 23 , wherein at least one of a height and a width of the crater feature provides an indicator of the severity of the coronary artery disease symptom.
28. The system of claim 15 , wherein the existence of at least one coronary artery disease symptom associated with the patient is determined based at least in part upon the zero-crossing and/or averaging and/or maximum statistical operators including at least one crater feature.