In daily life, lack of sleep is one of the main reasons for poor concentration. To support an effective napping, considered as one of good methods for recovering insufficient sleep and enhancing a user's concentration, we propose a hypnagogic time estimation using a heart rate sensor. Because a heart rate sensor has already been common, our method can be used widely and easily in our daily life. Most of existing sleep support systems aim to provide a comfortable wake-up by observing the sleep stage. Unlike these methods, we aim to provide an appropriate sleep duration by estimating a hypnagogic timing. By using various heart rate sensors, existing sleep support systems and 64ch electroencephalography, we tried to find out the relationship between various vital signals and sleep stages during a napping. Finally, we build a hypnagogic time estimation model by using the machine learning technique.