  Spectrograms visualize the frequency components of a given signal which may
be an audio signal or even a time-series signal. Audio signals have higher
sampling rate and high variability of frequency with time. Spectrograms can
capture such variations well. But, vital signs which are time-series signals
have less sampling frequency and low-frequency variability due to which,
spectrograms fail to express variations and patterns. In this paper, we propose
a novel solution to introduce frequency variability using frequency modulation
on vital signs. Then we apply spectrograms on frequency modulated signals to
capture the patterns. The proposed approach has been evaluated on 4 different
medical datasets across both prediction and classification tasks. Significant
results are found showing the efficacy of the approach for vital sign signals.
The results from the proposed approach are promising with an accuracy of 91.55%
and 91.67% in prediction and classification tasks respectively.
