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Week 4 Team Status

What are the most significant risks that could jeopardize the success of the project?
Since we are all on spring break, our most significant risk this week is lack of communication. So far we have been working together in person to make sure we are all one the same page, and this has made our progress a lot more efficient.
How are these risks being managed? What contingency plans are ready?
We will text/facetime each other if any significant changes or progress has been made to make sure we are all up to date on where we are in the project.
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?
We did not make any changes to the requirements/block diagram/system spec this week.
Provide an updated schedule if changes have occurred.
There have been no changes.
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Eri: Journal Week 4

  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).
  • This week I was able to find five full ‘lub’ ‘dub’ beats. It did not work on all of our training algorithm at first, because it seems to not correctly determine all the S1 and S2 because the findpeak algorithm on matlab is not accurate enough
  • To fix this I found the average diastole distance and checked that the frequency of the first s1 peak is smaller than the first s2 peak to correctly determine five full ‘lub’ ‘dubs’
  • One problem I found this week was that some of the signal we disregarded as ‘noise’ using my algorithm could be useful in helping to classifying a murmur, since many heart sounds with murmurs seem to have low amplitude signals directly following an S1 or S2 beat.
  • Feature extraction for SVM: I have researched and decided to use the following features for my SVM:

Time Domain
• mean diastole period (time between S2 and the next S1)
• Mean of the ratio of duration of systole to the RR interval – I don’t know what RR interval is yet, but I will research this next week.
• Maximum mean value between S1 and S2
• Mean of the S2 amplitude peaks.
• Mean of the S1 amplitude peaks.

Frequency-domain

• Average ratio of the power of each diastolic segment in the 100-300Hz frequency band to the power in the 200-400Hz frequency band.
• Mean power of each diastolic segment in the 150-350Hz frequency band
• Mean power of each diastolic segment in the 200-400Hz frequency band

  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.
  2. What deliverables do you hope to complete in the next week?
    • Since I am on spring break right now I don’t think I will get much done this week. However, I will start work on extracting the features I mentioned above from the dataset.
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Journal Entry Week 3: Eri

  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).
  • This week, I finished removing the noise of the signal. I used what I did last week,  but instead of just working with the amplitude I found peaks of the Shannon entropy, since it emphasizes the average intensity signals more efficiently, while also attenuating the high and low energy signals.
  • I was also able to classify each peak as S1 or S2, since S1 heart beats have a lower frequency signal than that of S2.
  • One problem I ran into using the Shannon energy was that it sometimes just completely removed some low intensity signals that were not actually noise.
  1. Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    Progress is slightly behind schedule, since I was hoping that by the end of this week I could have a clean data that is segmented and filtered to work with. So far my algorithm can define where S1 and S2 is, but it does not segment five full beats yet. I believe this next step will not be that difficult since it just needs to start collecting information at the first S1 and end at the fifth S2, so I will finish working on that by the end of tomorrow, and our team will hopefully be back on schedule.
  2. What deliverables do you hope to complete in the next week?
    • Realistically, since I have a lot of midterms next week, I do not think I will be able to have a working classification algorithm that determines whether the heart sound is normal or abnormal yet, but this week I will do further research on SVM algorithm and start using the segmented and filtered clean data to experiment with the SVM classification algorithm.
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Team – Week 2 Status Report

What are the most significant risks that could jeopardize the success of the project?
We had originally thought that the machine learning process of indicating whether a heart sound has a murmur could be done independently of segmentation/filtering; however, we could not find sufficient data that allows us to test the two separately (i.e. we could not find enough data with already segmented heart sounds without noise)
How are these risks being managed? What contingency plans are ready?
Eri and Ryan are meeting frequently and working on everything together – this has allowed us to work more efficiently. Ari is also going to help once he finishes assembling the stethoscope, since that is taking a lot less time than we had previously imagined.
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?
We did not make any changes to the requirements/block diagram/system spec this week.
Provide an updated schedule if changes have occurred.
As stated above, Eri will be working on the segmentation and filtering with Ryan and this will speed up the timeline to finish these parts. After this, they will work on the ML part together with Ari.
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Eri: Journal Entry Week 2

This week I worked on removing noise from the heart sound data. I did this by first finding the average of all the peaks and then removing extra high or low amplitudes. Below is the graph of the original heart sound, along with the heart sound after the loud and quiet noises are removed.

Next I used a Fourier transform to find the heart sound in frequency. I then got rid of all the frequencies that are not within the range of 20Hz to 150 Hz – the range of the human heart sound – using a low pass and high pass filter. In addition to this, I also experimented with other algorithms such as the moving average; however it had a very slow computation speed and it did not remove the noise as well as desired. I am currently on schedule, since my goal was to finish the denoising algorithm. Although it does not one hundred percent get rid of all the noise yet, it is pretty close. This week I will work on segmenting the heart sound into 5 full lub dub beats using deep learning. I did some research this week of which algorithms work best, but I have not decided which to move forward with yet.