Singularity detection of the rabbit electrocardiogram: An evolutionary spectral method

The time–frequency characteristics of the spectral density of non-stationary signals (NSS) in the neighborhood of an instant time point can be determined using the evolutionary spectral analysis. An experimental rabbit model involving ligation of the left anterior descending coronary artery to simulate the physiology of early phase myocardial ischemia (EPMI) has been previously described. Clinically, EPMI derived from left coronary artery stenosis is the main symptom of coronary heart diseases including acute myocardial infarction. Here, we propose a new algorithm for estimating the evolutionary spectral density functions, which is an effective approach to determine the instantaneous frequency spectra (IFS) of NSS under the uncertainty principle in the time–frequency domain. The localization singularity information in the data recorded from a living system could be detected by means of the IFS. Electrocardiogram (ECG) data recorded from experimental rabbits were analyzed with the new algorithm. Results showed that the Q’s value of the evolutionary spectral quality number of the QRS-complex data was the characteristic parameter of ECG, and there was a matched connection between the time–frequency characteristics of QRS-complex data and the myocardial ischemia symptoms of the rabbits. These results provide valuable information regarding features of the EPMI for use in clinical diagnoses.