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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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Ryan – Week 2 Status Report

At the beginning of the week, Eri and I once again worked together on reducing noise of the heart sound test data. We first began by applying a band-pass filter to get rid of the higher and lower frequency noises that aren’t audible to a human ear (20 kHz). I’ve attached the original heart sound here: 

Then here is the audio file after filtering out those frequencies:

As you can tell the background noise was reduced, so that the S1 and S2 beats are more audible and easier to analyze. We had an issue when there were sounds that were within the frequency caused by scuffling the mic, which we have to do further noise reduction or just eliminate those portions of the audio to analyze whether an abnormality exists.

I then decided to help designing the actual ML algorithm to classify the data because I’ve worked with Convolutional Neural Networks in my class and this would eliminate the need to segment the data because CNN’s are time shift invariant. The structure of my network is this:

I was able to achieve a 99.5% accuracy in classifiying images of handwritten digits, so I have to test the accuracy with audio files with this structure. I plan on testing this first thing tomorrow and will have number results in the next weekly update. Of course changes to this structure will have to occur to optimize the results for this dataset.

We are now on schedule because we were able to eliminate noise from the sound file as well as eliminate the need for segmentation of the heart sounds. We do want to test the accuracy of the segmentation and then classification through Support Vector Machines in the future to compare with these results.

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Ari – Week 2 Status Report

Status Report #2

Arihant jain

Team A4 (Smart Stethoscope)

 

My task for the week was to work on the prototype of the physical device stethoscope, and work on wiring the prototype together with the parts we got and test the sound on a speaker.

 

  • 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 received the parts I had ordered last week and started to connect them and test the audio signal with an oscilloscope and then set up the wiring for turning the signal into a 3.5mm output so that we could listen to the microphone stethoscope system on a speaker.
  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
    • Progress on track!
  • What deliverables do you hope to complete in the next week?
    • We have a working prototype and now just need to see how much we can make the sound quality match our testing data.

 

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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.