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
This week, I completed my calculations for DAR and Longest sleep bout based on 'day' being the 'lights on' period and 'night' being 'lights off'. Based on our reading of other sleep studies, it seems that most sleep research focuses on 'lights off' periods as being of interest. One of the most prevalent metrics in the papers I've seen regarding abnormal sleep patterns is Sleep Onset Latency, which is the amount of time it takes for a subject to fall asleep after lights have been turned off. Since this is also something of a set time at St. Luke's (although we cannot be absolutely certain that the patient will attempt to sleep as soon as the lights turn off), it appears as of now to be the most relevant way to split the 24-hour period.
After my initial research on sleep onset latency, I had hoped to calculate an 'individualized sleep time' similar to the way we calculated features based on individualized wake time. After talking with Dr. Sprint and Alexa, my plan is to simply calculate the number of minutes after lights-out (9:00PM). If a patient was already asleep at lights-out, they will receive a 0 for that night.
My next steps will be to look at the change in patient stats and/or features over time. Grouping this with our positive-negative feature classification approach, I am hoping this will give us a way to identify positive or negative changes in sleep over time.
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