Heart rate variability is a non invasive and indirect measure of the autonomic control of the heart. Therefore, alterations to this control system caused by myocardial ischaemia are reflected in changes in the complex and irregular fluctuations of this signal. Multifractal analysis is a well suited tool for the analysis of this kind of fluctuations, since it gives a description of the singular behavior of a signal. Recently, a new approach for multifractal analysis was proposed, the wavelet leader based multifractal formalism, which shows remarkable improvements over previous methods. In order to characterize and detect ischaemic episodes, in this work we propose to perform a short-time windowed wavelet leader based multifractal analysis. Our results suggest that this new method provides appropriate indexes that could be used as a tool for the detection of myocardial ischaemia.