Input: Five cell arrays:
a) The
first cell array has tissue glucose levels every 5 mins for 2.5 hrs during a
lunch meal
The data starts
from 30 mins before meal intake an continues up to 2 hrs after the start of
meal consumption
There are
several such time series for one subject.
b) The
second cell array has time stamps of each time series in the first cell array
c) The
third cell array has insulin basal infusion input time series at different
times during the 2.5 hr time interval
d) The
fourth cell array has time stamps for each basal or bolus insulin delivery time
series.
e) The
fifth cell array has insulin bolus infusion input time series at different
times during the 2.5 hr time interval
Some facts about the data:
Each cell array is an array of
time series each of which can have varying lengths.
Each subject has multiple such
time series but the total number of time series data for each subject may vary.
You have data from 5 subjects
The insulin input may not be
every 5 mins hence the insulin time series length may vary significantly
The time stamp which has the
highest insulin delivery is the time at which the meal was logged.
Tasks:
a) Extract
4 different types of time series features from only the CGM data cell array and
CGM timestamp cell array (10 points each) total 40
b) For
each time series explain why you chose such feature (5 points each) total 20
c) Show
values of each of the features and argue that your intuition in step b is
validated or disproved? (5 points each ) total 20
d) Create
a feature matrix where each row is a collection of features from each time
series. SO if there are 75 time series and your feature length after
concatenation of the 4 types of featues is 17 then the feature matrix size will
be 75 X 17 (10 points)
e) Provide
this feature matrix to PCA and derive the new feature matrix. Chose the top 5
features and plot them for each time series. (5 points)
f) For
each feature in the top 5 argue why it is chosen as a top five feature in PCA?
(3 points each) total 15
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