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Showing posts from December, 2018

Summer - Week 10

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

Week 19

The Guide to Actigraphy data collection proved to be very helpful! We'd come across many articles comparing Actigraphy to Polysomnography, but few that discuss research using solely watch-based data collection. The study by Dr. Weeks and Dr. Skornyakov has not relied on Actigraphy logs, as is the general recommendation, because their subjects are not going to be consistently capable of filling them out with accurate information. The issue then will be determining whether our Day/Night times are appropriate for the individual patient. I think that Dr. Skornyakov's individualized sleep idea will see to this issue fairly well. An additional factor I have seen over and over again during our literature review is sleep onset latency. This is what is used in sleep labs to diagnose things like narcolepsy and insomnia. It is a measurement of how long it takes a subject to fall asleep once they lie down in complete darkness. Because we are dealing with patients who spend more time i

Week 18

Over Christmas break, I am going to focus on doing more literature review. Dr. Skornyakov has provided us with some interesting articles on sleep studies of subjects suffering from disordered sleep. One, in particular, that seems of interest to me is a brief manual on what collecting and interpreting Actigraphy data should (ideally) look like in a medical setting. The others focus on topics not exactly the same as ours, but all involve some form of evaluating sleep. Unfortunately, two of them rely on cognitive testing (good brain function should indicate good sleep). However, it could be interesting to look at what factors of collected data they associated with high brain activity. In addition to reading, I will continue to work on the Individualized wake time calculator.

Week 17

At the request of Dr. Skornyakov, Alexa, Dr. Sprint and I intend to calculate individualized wake time factors for each day. This will be determined by the patient's sleep/wake status between six and seven in the morning. The idea behind this is that previously if a patient had woken before our "Day" period began (7:00AM) their activity would be recorded in the previous day's sleep period. Dr. Skornyakov has provided clear instructions on how to calculate this time. I plan to start by adding an extra level of processing to the output files such that we will have an additional column all for this new factor. Then, I'll modify my feature calculator so that it accesses the features from the newly determined time period.

Week 16

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

Week 15

This week, I began working on calculating a new data feature suggested to us by Dr. Skornyakov and beginning to investigate the significance of the longest bout of sleep in a night. The latter became of interest to me after reading the same paper provided by Dr. Sprint several weeks ago that introduced the concepts of the DAR (daytime activity ratio). The study calculated an additional feature that aimed to capture how fractured a patients sleep was. While my partner and I were unable to reproduce the feature's calculation on our data due to some ambiguity in its description, it still got us thinking about paying attention to the degree of consistency of sleep. I am going to start by locating the longest bout of sleep during a night and determining its length. Once I have this working well, there are several other angles that I think would be interesting to explore: start times of longest bouts length of other bouts considered relative to the longest length of longest bout r