Heart rate variability (HRV) analysis has been serving as a significant promising marker in clinical research over the last few decades. The rapidly growing heart rate data generated from various devices, particularly the electrocardiograph (ECG), need to be stored properly and processed timely. There is a pressing need to develop efficient approaches for performing HRV analyses based on ECG signals. In this paper, we introduce a cloud computing approach (called HRV-Spark) to compute HRV measures in parallel by leveraging Apache Spark and a QRS detection algorithm in [1]. We ran HRV-Spark on Amazon Web Services (AWS) clusters using large-scale datasets in the National Sleep Research Resource. We evaluated the performance and scalability of HRV-Spark in terms of the number of computing nodes in the AWS cluster, the size of the input datasets, and the hardware configuration of the computing nodes. The results show that HRV-Spark is an efficient and scalable approach for computing HRV measures.