Title: SEQuence-rPPG: A Fast BVP Signal Extraction Method From Frame Sequences. Abstract: Non-contact heart rate estimation has essential implications for the development of affective computing and telemedicine. However, existing deep learning-based methods often endeavor to achieve real-time measurements, so a simple, fast, pre-processing-free approach is needed. Our work consists of two main parts. Firstly, we proposed SEQ-rPPG, which first transforms the RGB frame sequence into the original BVP signal sequence by learning-based linear mapping and then outputs the final BVP signal using 1DCNN-based spectral transform, and time-domain filtering. Secondly, to address the shortcomings of the existing dataset in training the model, a new large-scale dataset was collected for training and testing. Our approach achieved competitive results on the collected large dataset(the best) and public dataset UBFC-rPPG(0.81 MAE with 30s time window, test only). It requires no complex pre-processing, has the fastest speed, can run in real-time on mobile ARM CPUs, and can achieve real-time beat-to-beat performance on desktop CPUs. Benefiting from the high-quality training set, other deep learning-based models reduced errors by at least 53$\%$. We compared the methods with and without the spectral transformation, and the results show that the processing in the time domain is effective.