METHODS, SYSTEMS, AND APPARATUS FOR DETECTING RESPIRATION PHASES

1 . An apparatus for analyzing vibration signal data collected from a nasal bridge of a subject via a sensor to reduce errors in training an artificial neural network using the vibration signal data, the apparatus comprising:

a feature extractor to determine feature coefficients of the vibration signal data, the artificial neural network to generate a respiration phase classification for the vibration signal data based on the feature coefficients;

a classification verifier to verify the respiration phase classification; and

an output generator to generate a respiration phase output based on the verification.

2 . The apparatus as defined in claim 1 , further including:

a breathing rate analyzer to:

determine a breathing interval for the vibration signal data; and

compare the breathing interval to a breathing interval variance threshold; and

a trainer to train the artificial neural network if the breathing interval satisfies the breathing interval variance threshold.

3 . The apparatus as defined in claim 2 , wherein the respiration phase classification includes a first value and a second value and wherein the trainer is to train the artificial neural network if a mean of a first value of at least two respiration phase classifications for the vibration signal data or a mean of the second value of at least two respiration phase classifications for the vibration signal data satisfy a re-training threshold.

4 . The apparatus as defined in claim 1 , wherein the respiration phase output is one of inhalation or exhalation.

5 . The apparatus as defined in claim 1 , wherein the respiration phase classification is a first respiration phase classification, the artificial neural network to generate the first respiration phase classification for a first frame of the vibration signal data and the classification verifier to verify the first respiration phase classification relative to a second respiration phase classification for a second frame of the vibration signal data.

6 . The apparatus as defined in claim 5 , wherein the classification verifier is to detect an error if the first respiration phase classification is associated with inhalation and the second respiration phase classification is associated with exhalation, the first frame and the second frame being consecutive frames.

7 . The apparatus as defined in claim 6 , wherein an energy of the vibration signal data of the first frame and an energy of the vibration data of the second frame are to satisfy a moving average frame energy threshold.

8 . The apparatus as defined in claim 1 , further including a breathing interval verifier to determine if a breathing interval for the vibration signal data meets a breathing interval variance threshold, and wherein if the classification verifier detects an error in the respiration phase classification and the breathing interval verifier determines that the breathing interval meets the breathing interval variance threshold, the classification verifier is to generate an instruction for the artificial neural network to be re-trained.

9 . The apparatus as defined in claim 8 , wherein the classification verifier is to correct the respiration phase classification by updating the respiration phase classification with a corrected respiration phase classification, the respiration phase output to include the corrected respiration phase classification.

10 . A method for analyzing vibration signal data collected from a nasal bridge of a subject via a sensor, the method comprising:

determining, by executing an instruction with a processor, feature coefficients of the vibration signal data;

generating, by executing an instruction with the processor, a respiration phase classification for the vibration signal data based on the feature coefficients;

verifying, by executing an instruction with the processor, the respiration phase classification; and

generating, by executing an instruction with the processor, a respiration phase output based on the verification.

11 . The method as defined in claim 10 , further including:

determining a breathing interval for the vibration signal data;

comparing the breathing interval to a breathing interval variance threshold; and

if the breathing interval satisfies the breathing interval variance threshold, training an artificial neural network to generate the respiration phase classification.

12 . The method as defined in claim 11 , wherein the respiration phase classification includes a first value and a second value and further including training the artificial neural network if a mean of a first value of at least two respiration phase classifications for the vibration signal data or a mean of the second value of at least two respiration phase classifications for the vibration signal data satisfy a re-training threshold.

13 . The method as defined in claim 10 , wherein the respiration phase classification is a first respiration phase classification, and further including:

generating the first respiration phase classification for a first frame of the vibration signal data; and

verifying the first respiration phase classification relative to a second respiration phase classification for a second frame of the vibration signal data.

14 . The method as defined in claim 13 , further including detecting an error if the first respiration phase classification is associated with inhalation and the second respiration phase classification is associated with exhalation, the first frame and the second frame being consecutive frames.

15 . The method as defined in claim 14 , wherein an energy of the vibration signal data of the first frame and an energy of the vibration data of the second frame are to satisfy a moving average frame energy threshold.

16 . The method as defined in claim 10 , further including:

determining if a breathing interval for the vibration signal data meets a breathing interval variance threshold; and

generating an instruction for an artificial neural network to be trained if an error is detected in the respiration phase classification and if the breathing interval meets the breathing interval variance threshold.

17 . The method as defined in claim 16 , further including correcting the respiration phase classification by updating the respiration phase classification with a correction reparation phase classification, the respiration phase output to include the corrected respiration phase classification.

18 . A computer readable storage medium comprising instructions that, when executed, cause a machine to at least:

determine feature coefficients of vibration signal data collected from a nasal bridge of a subject via a sensor:

generate a respiration phase classification for the vibration signal data based on the feature coefficients;

verify the respiration phase classification; and

generate a respiration phase output based on the verification.

19 . The computer readable storage medium as defined in claim 18 , wherein the instructions, when executed, further cause the machine to:

generate the first respiration phase classification for a first frame of the vibration signal data; and

verify the first respiration phase classification relative to a second respiration phase classification for a second frame of the vibration signal data.

20 . The computer readable storage medium as defined in claim 18 , wherein the instructions, when executed, further cause the machine to:

divide the vibration signal data into frames; and

generate a respective respiration phase classification for each of the frames.