Lu, HuiHuiLuHolzschuh, RobertRobertHolzschuhFreund, RobertRobertFreundOrtmann, SteffenSteffenOrtmannGrosse Meininghaus DirkKölpin, AlexanderAlexanderKölpin2026-07-302026-07-302026-07-14IEEE Access 14: 108098-108113 (2026)https://hdl.handle.net/11420/64121Continuous wave (CW) radar is an alternative to gold-standard electrocardiography (ECG) for contactless heart rate (HR) monitoring and heart rate variability (HRV) analysis. This work aims to monitor HR and HRV using a deep learning (DL) model with a 61 GHz CW radar system. We employed attention-based DL fusion model to combine information from radar heart sound (HS) and radar pulse wave (PW) signals. Additionally, we compared attention-based fusion with alternative fusion methods, including data fusion, feature fusion, and decision fusion. To evaluate the performance of the proposed method, a dataset of approximately 20 hours was collected from 20 participants in varying body positions. Using attention-based fusion, we achieved the overall highest F1 score of 97.15% for heartbeat detection, which outperformed solely HS-based or PW-based model, as well as other fusion methods. F1 scores exceeding 96% were obtained for all analyzed body positions. A high correlation of 97.11% and an RMSE of 28.99 ms were achieved for the estimation of the interbeat interval (IBI). In addition, we extracted HRV in time, frequency, and nonlinear domains. Low relative errors were achieved, e.g. 14.04% for TRI, 6.14% for LF, 6.35% for alpha2, and 10.93% for ApEn. The proposed approach achieved high-level performance for HR monitoring and HRV estimation using a CW radar system, showing great potential for various healthcare applications.en2169-35362026108098108113IEEEhttps://creativecommons.org/licenses/by/4.0/attentionCW radardeep learningfusionheart rateheart rate variabilityheart soundpulse waveTechnology::610: Medicine, HealthJournal Article10.1109/ACCESS.2026.371340410.15480/882.17686