Method for diagnosing heart disease, predicting sudden death, and analyzing treatment response using multifractial analysis

摘要

A method of analyzing electrocardiogram (EKG) data for use in the diagnosis of heart disease, prognosis of cardiac conditions, and the monitoring of heart disease therapies is disclosed. The method utilizes a wavelet-based multifractal analysis with one or more of (1) a discrete wavelet smoothing step to remove the effects of abnormal beats; (2) “Levy flight” analysis to detect the frequency of abnormal beats known to adversely affect the multifractal (MF) analysis; and (3) MF alpha analysis, a multifractal extension of monofractal short term (ST) alpha analysis. The invention further comprises an EKG test battery comprising Levy flight anomalous beat/beat cluster screening, followed by (ST) MF alpha analysis and MF Holder analysis (when validated by the Levy flight analysis). The wavelet smoothing step can also be used to classify human EKGs by observing the effect of sequential smoothing on the MF Holder coefficient. Alternative choices to the wavelet smoothing approach to removal of abnormal beat effects include probability distribution function analysis to determine the MF Holder coefficient directly, abnormal beat ridge skeleton removal to remove the offending beats based on a direct multifractal spectrum calculation, and the calculation of various types of entropy coefficients for the EKG time series.