Modeling the Electrocardiogram as Oscillatory Almost-Cyclostationary Process

Abstract

A new model for the electrocardiogram (ECG) signal is proposed. Specifically, the ECG signal is modeled as an amplitude-modulated and time-warped version of a cyclostationary process which is the sum of a periodic signal and a zero-mean cyclostationary term. For the proposed model, the secondorder characterization is derived in both time and frequency domains. The autocorrelation function is shown to be the superposition of amplitude-and angle-modulated sine waves and the Loève bifrequency spectrum a spread version of that of the underlying cyclostationary process. The signal model belongs the recently introduced class of the oscillatory almost-cyclostationary processes. A procedure for estimating the secondorder statistical functions in both time and frequency domains is outlined. The effectiveness of the proposed model and of the estimation procedure is corroborated by measurements on real ECG signals which are in full agreement with the theoretical analytical expressions. The proposed model is shown to be effective with observation intervals much larger than those adopted up to now with the classical cyclostationary model and is suitable to be exploited for arrhythmia modeling and characterization, and for diagnosis and biometric purposes.

On a Novel Model for ECG Signals and its Statistical Properties

“…4 and Fig. [21] that ε (t) would be just a constant; hence, the model may not lead to a timevarying heart rate. Also, if one observes eqn.…”

“…Also, if one observes eqn. (2.2) of [21], it first considers a deterministic periodic component, representing the mean of ECG, added with a cyclostationary term, modeling the variation about the mean. Then, this signal is time-warped to model the almost periodicity.…”

“…In [6], [7] there is no classification of heartrelated abnormalities. In [21], the author proposed that the ECG signal model is oscillatory, almost cyclostationary, and works with much greater observation intervals than the traditional cyclostationary model. However we should note that due to the relatively large number of normal beats used in the average, a transient phenomenon such as a brief string of anomalous beats may go undetected if the observation interval is too long.…”

Isomorphic Multidimensional Structures of the Cyclic Random Process in Problems of Modeling Cyclic Signals with Regular and Irregular Rhythms

“…The developed models, methods and software tools for processing cyclic signals are organized in the form of a computer ontology, which is built on the basis of an axiomatic deductive strategy for the systematization of knowledge in modern intellectualized information systems [54,55]. Somewhat similar approaches to modeling cyclic signals with irregular cyclicity are carried out in the works of [56][57][58][59][60].…”