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Week 11 Group Journal

What are the most significant risks that could jeopardize the success of the project?
We are well on schedule to complete this stethoscope in time for the demo
How are these risks being managed? What contingency plans are ready?
N/A
Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
No changes made this week.
Provide an updated schedule if changes have occurred.
No changes made this week
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Week 10 Group Journal

What are the most significant risks that could jeopardize the success of the project?
We are having trouble importing some modules onto the raspberry pi and this seems to be the biggest risk right now, since everything else is coming together well.
How are these risks being managed? What contingency plans are ready?
We will spend extra time in lab and ask professors if this issue cannot be resolved.
Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
No changes made this week.
Provide an updated schedule if changes have occurred.
No changes made this week
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Week 8 Group Journal

What are the most significant risks that could jeopardize the success of the project?
The biggest risk this week is if the double blind trials does not perform well (i.e. it does not mimic heart sounds) then we need to think of other ways to test our stethoscope.
How are these risks being managed? What contingency plans are ready?
We individually came up with different ways to mimic heart sounds that we can try in case the speaker with foam does not perform well.
Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
No changes made this week.
Provide an updated schedule if changes have occurred.
No changes made this week

 

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Eri Journal: Week 11

  • What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
    • We all worked on integrating the whole stethoscope (front-end/back-end) so it accurately classified heart sounds through the stethoscope.
    • I worked on the final frontend GUI using Matplotlib and tkinter to display user’s heat sound signal for our raspberry pi.
  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    • Our progress is on schedule
  • What deliverables do you hope to complete in the next week?
    • Finish the project in time for the demo on Monday.
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Eri – Week 10

  • What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
    • We reached an accuracy of 89% this week
    • We found the specificity and the sensitivity. The specificity rate was around 95%, but the sensitivity was pretty low (65%).
    • We decided to find more datasets with abnormal heart sounds and after training our CNN with more dataset we were able to get our sensitivity rate to 79%.
    • Created a run time signal for the heart sounds from the stethoscope
    • This runtime signal shows up on our raspberry pi, but it’s a bit slower than we’d like.
  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    • Our progress is on schedule
  • What deliverables do you hope to complete in the next week?
    • Get the sensitivity rate up to 85% and finish integrating the stethoscope with our code. (We are nearly done with the integration part)
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Eri Week 9 Journal

  • What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
    • We finished our double blind trials on our testing environment this week
    • Through training the larger dataset on the SVM and CNN we finally were able to reach an average accuracy of around 83% using the CNN, which is close to our goal.
    • We started integrating all of the necessary things for the demo on Monday, and tomorrow we will focus more on transforming our MATLAB code into C.
  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    • Our progress is behind schedule since we wanted to be done with classifying AS/MR by the end of this week, but we realized we may just scrap that completely and just make this stethoscope work for classifying abnormal vs normal heart sound since we do not have a lot of time left.
  • What deliverables do you hope to complete in the next week?
    • Finish integrating our code with our stethoscope and start working on using the raspberry pi to be able to display whether the user has a normal or abnormal heart sound.
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Eri Week 8 Journal

  • What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
    • Double blind trials on testing environment – so far people cannot tell the difference between the heart sound from our stethoscope and the dataset heart sound
    • Researched ways to find the similarity of the heart sound from stethoscope and the dataset so we can get a percentage to prove the testing environment is good enough
    • Trained the CNN on larger dataset from Physionet.
  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    • Behind schedule – I did not work on this project enough this week due to carnival and other commitments.
    • I will put in more time next week and work with Ari and Ryan in lab more.
  • What deliverables do you hope to complete in the next week?
    • We will be working on our CNN for AR/MS to reach an accuracy of 85%.
    • Start training on abnormal heart sounds for MR/AS.
    • Find a percentage accuracy of the similarity for our testing environment
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Week 7 Group status report

What are the most significant risks that could jeopardize the success of the project?
Since carnival starts next week, our biggest risk is not putting in enough time over the weekend to finish our abnormal heart sound classification for MS/AR.
How are these risks being managed? What contingency plans are ready?
We plan to set up a meeting at least twice over carnival to all work togther on this to ensure we are keeping on track.
Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
No changes made this week.
Provide an updated schedule if changes have occurred.
No changes made this week.
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Eri: Week 7 Status Report

  • What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
    • I found more datasets to train the heart sound algorithm on.
    • I denoised all the data before putting it through the CNN.
    • The accuracy percentage went down when I ran the CNN on the denoised heart, but that is most likely because I ran it on my laptop with a much lower iteration and epoch, since it takes a lot more time to train the CNN on  my laptop.
    • Started to train the abnormal heart sounds for AR/MS.
  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    • On schedule.
  • What deliverables do you hope to complete in the next week?
    • We will be working on our CNN for it to reach an accuracy of 85%.
    • Finish training on abnormal heart sounds for MS/AR.
    • Purchase a ballistic gel to play the heart sound through to ensure our testing environment mimics that of a real heart sound well enough.
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Eri: Week 5 Journal

  1. What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
  • I started working on the SVM, but using the features I listed below that I extracted from the heartsound signal this week I was only able to get the accuracy up to 60%, which isn’t that great since it is only classifying between abnormal and normal.
    • mean value
    • median value
    • mean absolute deviation
    • 25th/75th percentile value
    • interquartile range
    • skewness value
    • Kurtosis value
    • spectral/shannon entropy
    • max frequency/max value/max ratio
    • systole/diastole time
  • Through research I learned I should also extract the Mel-frequency cepstral coefficients, but since our deadline is approaching and I don’t know anything about Mel-frequency cepstral coefficients, Ryan and I have decided to scrap the SVM approach and focus on the CNN. This is because he already managed to get the accuracy percentage to nearly 80%, which is close to the 85% we are aiming for.
  1. Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    My progress is on schedule this week. We were aiming to decide between CNN and SVM by the end of this week, and we successfully did this.
  2. What deliverables do you hope to complete in the next week?
    • I will fix my Gantt chart to match that of Ryan’s since we will work together on the CNN from now on. Next week we hope improve the initialization of the CNN and test it on the full Physionet dataset.