Abstract
We present a system to analyze patterns inside pulsatile signals and discover repetitions inside signals. We measure dominance of the repetitions using morphology and discrete nature of the signals by exploiting machine learning and information theoretic concepts. Patterns are represented as combinations of the basic features and derived features. Consistency of discovered patterns identifies state of physiological stability which varies from one individual to another. Hence it has immense impact on deriving the accurate physiological parameters for personalized health analytics. Proposed mechanism discovers the regular and irregular patterns by performing extensive analysis on several real life cardiac data sets. We have achieved more than 90% accuracy in identifying irregular patterns using our proposed method.
Published Version
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