Accurate Automatic Detection of End-Diastole From Left Ventricular Pressure Using Peak Curvature

Precise identification of end-diastole (ED), corresponding to the end of diastole and start of systole, is crucial for accurate assessment of cardiac function. The aims of this study were to develop a new algorithm based on peak curvature (kappa<i>p</i>) for detecting ED as a ldquocornerrdquo in left ventricular pressure (LVP) signals, and to compare this approach with ldquogold-standardrdquo ED obtained by manual annotation (ED_man) and ED calculated with previously described algorithms that use an LVP first-derivative threshold (<i>dP</i>/<i>dt</i> ₀ or <i>dP</i>/<i>dt</i> ₁₀₀), the peak LVP second-derivative (<i>d</i> ² <i>P</i>/<i>dt</i> ² <i>p</i>) or ECG R-wave peak ( ECG_R). Using customized software, all algorithms were applied to data derived from 213 large animal studies spanning a wide range of animal ages (fetus to adult), heart rates, inotropic states, and loading conditions. Differences between ED_man and each algorithm were then compared after defining an acceptance region for the ED detection based on ED_man interobserver variability. ED detected with kappa<i>p</i> was the most accurate (p < 0.001) and least variable (p < 0.001), with 97% of measurements within the acceptance region and difference from ED_man of (1.5 plusmn 4.2) ms. By contrast, ED was often detected early with <i>dP</i>/<i>dt</i> ₀ and <i>dP</i>/<i>dt</i> ₁₀₀ , and late with <i>d</i> ² <i>P</i>/<i>dt</i> ² <i>p</i> and ECG_R. These results indicate that the peak curvature algorithm using LVP provides accurate and reliable detection of ED.