• Joel Balmer, Rachel Smith, Christopher G Pretty, Thomas Desaive, Geoff M Shaw, and J Geoffrey Chase.
• Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. joel.balmer@pg.canterbury.ac.nz.
• J Clin Monit Comput. 2021 Feb 1; 35 (1): 79-88.

AbstractIdentification of end systole is often necessary when studying events specific to systole or diastole, for example, models that estimate cardiac function and systolic time intervals like left ventricular ejection duration. In proximal arterial pressure waveforms, such as from the aorta, the dicrotic notch marks this transition from systole to diastole. However, distal arterial pressure measures are more common in a clinical setting, typically containing no dicrotic notch. This study defines a new end systole detection algorithm, for dicrotic notch-less arterial waveforms. The new algorithm utilises the beta distribution probability density function as a weighting function, which is adaptive based on previous heartbeats end systole locations. Its accuracy is compared with an existing end systole estimation method, on dicrotic notch-less distal pressure waveforms. Because there are no dicrotic notches defining end systole, validating which method performed better is more difficult. Thus, a validation method is developed using dicrotic notch locations from simultaneously measured aortic pressure, forward projected by pulse transit time (PTT) to the more distal pressure signal. Systolic durations, estimated by each of the end systole estimates, are then compared to the validation systolic duration provided by the PTT based end systole point. Data comes from ten pigs, across two protocols testing the algorithms under different hemodynamic states. The resulting mean difference ± limits of agreement between measured and estimated systolic duration, of [Formula: see text] versus [Formula: see text], for the new and existing algorithms respectively, indicate the new algorithms superiority.

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