  An important paradigm in smart health is developing diagnosis tools and
monitoring a patient's heart activity through processing Electrocardiogram
(ECG) signals is a key example, sue to high mortality rate of heart-related
disease. However, current heart monitoring devices suffer from two important
drawbacks: i) failure in capturing inter-patient variability, and ii)
incapability of identifying heart abnormalities ahead of time to take effective
preventive and therapeutic interventions.
  This paper proposed a novel predictive signal processing method to solve
these issues. We propose a two-step classification framework for ECG signals,
where a global classifier recognizes severe abnormalities by comparing the
signal against a universal reference model. The seemingly normal signals are
then passed through a personalized classifier, to recognize mild but
informative signal morphology distortions. The key idea is to develop a novel
deviation analysis based on a controlled nonlinear transformation to capture
significant deviations of the signal towards any of predefined abnormality
classes. Here, we embrace the proven but overlooked fact that certain features
of ECG signals reflect underlying cardiac abnormalities before the occurrences
of cardiac disease. The proposed method achieves a classification accuracy of
96.6% and provides a unique feature of predictive analysis by providing
warnings before critical heart conditions. In particular, the chance of
observing a severe problem (a red alarm) is raised by about 5% to 10% after
observing a yellow alarm of the same type. Although we used this methodology to
provide early precaution messages to elderly and high-risk heart-patients, the
proposed method is general and applicable to similar bio-medical signal
processing applications.
