  As we age, our hearts undergo changes which result in reduction in complexity
of physiological interactions between different control mechanisms. This
results in a potential risk of cardiovascular diseases which are the number one
cause of death globally. Since cardiac signals are nonstationary and nonlinear
in nature, complexity measures are better suited to handle such data. In this
study, non-invasive methods for detection of cardiac aging using complexity
measures are explored. Lempel-Ziv (LZ) complexity, Approximate Entropy (ApEn)
and Effort-to-Compress (ETC) measures are used to differentiate between healthy
young and old subjects using heartbeat interval data. We show that both LZ and
ETC complexity measures are able to differentiate between young and old
subjects with only 10 data samples while ApEn requires at least 15 data
samples.
