RESUMEN
Zebrafish (Danio rerio) is a powerful animal model used in many areas of genetics and disease research. Despite its advantages for cardiac research, the heartbeat pattern of zebrafish larvae under different stress conditions is not well documented quantitatively. Several effective automated heartbeat detection methods have been developed to reduce the workload for larva heartbeat analysis. However, most require complex experimental setups and necessitate direct observation of the larva heart. In this paper, we propose the Zebrafish Heart Rate Automatic Method (Z-HRAM), which detects and tracks the heartbeats of immobilized, ventrally positioned zebrafish larvae without direct larva heart observation. Z-HRAM tracks localized larva body deformation that is highly correlated with heart movement. Multiresolution dense optical flow-based motion tracking and principal component analysis are used to identify heartbeats. Here, we present results of Z-HRAM on estimating heart rate from video recordings of seizure-induced larvae, which were of low resolution (1024 × 760) and low frame rate (3 to 4 fps). Heartbeats detected from Z-HRAM were shown to correlate reliably with those determined through corresponding electrocardiogram and manual video inspection. We conclude that Z-HRAM is a robust, computationally efficient, and easily applicable tool for studying larva cardiac function in general laboratory conditions. Graphical abstract Flowchart of the automatic zebrafish heartbeat detection.