The fluctuation of an RR interval (RRI) on an electrocardiogram (ECG) is called heart rate variability (HRV). HRV reflects the autonomic nerve activity, thus HRV analysis has been used for health monitoring such as stress estimation, drowsiness detection, epileptic seizure prediction, and cardiovascular disease diagnosis. However, RRI and HRV features are easily affected by arrhythmia, which deteriorates the health monitoring performance. Premature ventricular contraction (PVC) is common arrhythmia that many healthy persons have. Thus, a new methodology for dealing with RRI fluctuation disturbed by PVC needs to be developed for realizing precise health monitoring. To modify RRI data affected by PVC, the present work proposes a new method based on a denoising autoencoder (DAE), which reconstructs original input data from the noisy input data by using a neural network. The proposed method, referred to as DAE-based RRI modification (DAERM), aims to correct the disturbed RRI data by regarding PVC as artifacts. The present work demonstrated the usefulness of the proposed DAE-RM through its application to real RRI data with artificial PVC (PVC-RRI). The result showed that DAE-RM successfully modified PVC-RRI data. In fact, the root means squared error (RMSE) of the modified RRI was improved by 83.5% from the PVC-RRI. The proposed DAERM will contribute to realizing precise HRV-based health monitoring in the future.