To generate potentially informative events from vital signs, we first segment each data channel into sequences of k consecutive measurements. We then perform Fourier transformation to obtain spectral profiles of each segment of raw signal. Multiple spectral profiles, extracted from periods of observation that are considered medically benign, are then assembled to form a kdimensional flat table. We apply principal component analysis to this, and the top p components are considered further. These p components form a null space model of the expected normal behavior of the given vital sign. We build one null space model for each channel separately; this concludes the learning stage of the process.