Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a cardiomyocyte based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of cardiomyocytes into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies (hEBs). While some of the 9 cell clusters in the dataset presented with just one phenotype, the majority of the cell clusters presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of cardiomyocytes from an electrophysiological perspective.

The use of human embryonic stem cell cardiomyocytes (hESC-CMs) in tissue transplantation and repair has led to major recent advances in cardiac regenerative medicine. However, to avoid potential arrhythmias, it is critical that hESC-CMs used in replacement therapy be electrophysiologically compatible with the adult atrial, ventricular, and nodal phenotypes. The current method for classifying the electrophysiology of hESC-CMs relies mainly on the shape of the cell's action potential (AP), which each expert subjectively decides if it is nodallike, atrial-like or ventricular-like. However, the classification is difficult because the shape of the AP of an hESC-CMs may not coincide with that of a mature cell. In this paper, we propose to use a metamorphosis distance for comparing the AP of an hESC-CMs to that of an adult cell model. This involves constructing a family of APs corresponding to different stages of the maturation process, and measuring the amount of deformation between APs. Experiments show that the proposed distance leads to better interpolation and classification results.