This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back
After last week, I had successfuly put together my script to calculate the length of the longest bout of sleep for each patient for each night during the study, but I was not displaying my output in a very meaningful way. This week, I was able to take the numbers I was getting and translate them into something that was somewhat insightful.
The first obstacle I discussed in my last post was the lack of consistent length in the patient's stay times. To overcome this, I came up with a way to standardize the stay of each patient's data to seven days. I used integer division to divide the stay length by seven to get a new 'day length'. I then found how many days remained that did not fit evenly into the 'day length' and distributed the extraneous days so that in many cases, the 'day length' for the first few days in a patients stay were a day longer than that in the last few days. By averaging the longest bout over each 'day', I was able to produce a panel of seven values for each and every patient. This made displaying the data in a graph much more effective.
Going forward, I plan to do more work on implementing the additional rules provided by Dr. Skornyakov regarding custom wake times and reading the literature she has shared with our team that may shed light on potential factors in identifying good sleep.
The first obstacle I discussed in my last post was the lack of consistent length in the patient's stay times. To overcome this, I came up with a way to standardize the stay of each patient's data to seven days. I used integer division to divide the stay length by seven to get a new 'day length'. I then found how many days remained that did not fit evenly into the 'day length' and distributed the extraneous days so that in many cases, the 'day length' for the first few days in a patients stay were a day longer than that in the last few days. By averaging the longest bout over each 'day', I was able to produce a panel of seven values for each and every patient. This made displaying the data in a graph much more effective.
Going forward, I plan to do more work on implementing the additional rules provided by Dr. Skornyakov regarding custom wake times and reading the literature she has shared with our team that may shed light on potential factors in identifying good sleep.
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