CardiacDP: An R package for rapid automated cardiac data processing, integrating autocorrelation, a genetic algorithm, and a tracking index

Heart rate is a popular proxy of physiological responses, but the highly complex and variable cardiac data obtained from organisms such as marine invertebrates pose a major challenge to efficient and accurate data processing. To address this, we developed a novel, integrative algorithm for rapid and automated cardiac data processing. This algorithm primarily employs autocorrelation for time series analysis to identify recurrent heartbeats and compute heart rates from their periods. A genetic algorithm framework was used to implement such an autocorrelative analysis to filter out noise and extract meaningful signals, maximizing data utilization. A tracking index is also incorporated to reference previous timepoints and reduce errors associated with complex waveforms. To evaluate its performance, we compared the algorithm estimates to manually obtained heart rates of 33 individuals of marine invertebrates (from nine species of gastropods, bivalves, and crustaceans). The results showed that these features collectively improve data utilization (mean percentage count > 90%) and accuracy (mean absolute percentage error = 3%). By avoiding reliance on any predetermined characteristics, this algorithm can not only accommodate case-by-case variability and thus be applicable to diverse taxa, but also potentially extend to analyze periodicity in other biological time series data such as valvometry, acoustics and movement patterns. As an open-source tool, this algorithm encourages collaborative efforts and further developments that refine and expand its applications, thereby enhancing our capabilities in physiological monitoring and analyses.