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.
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.
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.
