Continuous blood pressure (BP) monitoring using wearable devices has received increasing attention due to its importance in diagnosing diseases. However, existing methods mainly measure BP intermittently, involve some form of user effort, and suffer from insufficient accuracy due to sensor properties. In order to overcome these limitations, we study the BP measurement technology based on heart sounds, and find that the time interval between the first and second heart sounds (TIFS) of bone-conducted heart sounds collected in the binaural canal is closely related to BP. Motivated by this, we propose HearBP, a novel BP monitoring system that utilizes inear microphones to collect bone-conducted heart sounds in the binaural canal. We first design a noise removing method based on U-net autoencoder-decoder to separate clean heart sounds from background noises. Then, we design a feature extraction method based on shannon energy and energy-entropy ratio to further mine the time domain and frequency domain features of heart sounds. In addition, combined with the principal component analysis algorithm, we achieve feature dimension reduction to extract the main features related to BP. Finally, we propose a network model based on dendritic neural regression to construct a mapping between the extracted features and BP. Extensive experiments with 41 participants show the average estimation error of 0.97mmHg and 1.61mmHg and the standard deviation error of 3.13mmHg and 3.56mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range specified by the FDA’s AAMI protocol.