Arousals during sleep give deep insights into the patho-physiology of sleep disorders and sleep quality. Detecting arousals is a time-consuming process manually per-formed by a trained expert. The required measurement is performed on an inpatient basis and is uncomfortable for the patient. As arousals relate to the autonomic nervous system, they also reflect in the electrocardiogram, which is therefore a promising alternative biosignal. In this study, we developed a deep learning model for automatic detection of sleep arousals from heart rate. We developed our algorithm using 5323 recordings from the Sleep Heart Health Study. 1003 of them were held-out as test data. We derived RR intervals from the ECG and interpolated them into a 4 Hz signal. Next, we developed a convolutional neural network (CNN) for end-to-end event detection. Model output is a continuous arousal probabil-ity with a frequency of 1 Hz. The optimization resulted in a twelve-layer CNN that achieved a Cohens kappa of 0.47, an area under the precision-recall curve of 0.54 on hold-out test data. This study demonstrates the ability of machine learning to detect arousals during sleep from heart rate. As our approach uses only the heart rate, it is potentially trans-ferable to other signals, e.g. the photoplethysmogram.