Feature processing, extraction, and selection
Physiological measurements from the wearable device considered for our analysis included: minimum, maximum, and mean HR (one measurement recorded every 5 min in beats per minute); RR (one measurement recorded every 15 min in breaths per minute); activity duration defined as the time during which the patient was active within each 4-hour registration period (one measurement recorded every 4 h in seconds); activity intensity defined as the percent of target HR (220 beats per minute minus age) which was reached during activity (one measurement recorded every 4 h); and posture defined as the percent of time that the patient was standing upright (one measurement recorded every 4 h in degrees). Another measurement, workload, was calculated by multiplying activity duration and intensity measurements.The time series of minimum, maximum, and mean HR as well as RR were split into day (7am to 10pm) and night time (10pm to 7am) series. Time series data from only the first full 3 consecutive days of each visit were considered throughout the analysis. The Python package “tsfresh” [Given the large number of features generated with tsfresh package, dimensionality reduction and the selection of outcome-specific relevant features was necessary to fine-tune and achieve optimal machine learning model performance. For this purpose, the features obtained from the tsfresh package were filtered for multi-collinearity with a correlation score greater than 0.9 to obtain reliable estimates from the model. We then used SelectKBest() from scikit-learn (