Characterisation of acute myocardial ischaemia in a canine model based on principal component analysis of unipolar...

The study presents a method for identifying endocardial electrical features relevant to local ischaemia detection at rest. The method consists of, first, normalisation of electrograms to a uniform representation; secondly, the use of principal component analysis to reduce the dimensionality of the electrogram vector space; and, thirdly, a search for a classification axis that matches the degree of ischaemia present in the tissue. Left ventricular myocardial states were assessed by echocardiography and NOGA mapping in eight dogs at baseline and then immediately after, 5h after and 3 days after occlusion of the left anterior descending coronary artery. Five principal components were required to approximate electrograms with an average error of less than 10% of the peak-to-peak amplitude. Correlations of 0.77, 0.80 and 0.84 were obtained between the principal component-based parameters and the echocardiography scores at the three ischaemic stages, respectively. Expression of these parameters in the time domain showed that the major changes occurred in the depolarisation segment of the endocardial electrogram as well as in the ST-segment. In conclusion, the proposed method provides a suitable alternative co-ordinate system for the classification of ischaemic regions and highlights signal segments that change as a result of pathology.