Low-complexity fetal heart rate monitoring from carbon-based single-channel dry electrodes maternal electrocardiogram

Abstract Objective . Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG ( aECG ) to extract fetal heart rate ( fHR ) using a low-complexity algorithm suitable for low-power wearables. Approach . A hybrid model combining machine learning, QRS masking, and data fusion was trained on two PhysioNet databases and synthetically generated aECG . Model selection employed the Akaike criterion with data balancing and random sampling. Main results . The algorithm was tested on 80 recordings from the Computer in Cardiology Challenge 2013 (CCC) and the abdominal and direct fetal database (ADFD), augmented with 100 synthetic aECG . Performance for fetal QRS detection reached P r e c i s i o n = 97.2 ( 82.2 ) %, S p e c i f i c i t y = 99.8 ( 93.8 ) %, and S e n s i t i v i t y = 97.4 ( 93.9 ) % on ADFD and CCC, respectively. Clinical validation used the Polar Electro Oy H10 dry-electrode device at the Maternity Hospital of Southwest Finland. Four subjects (gestational age 39.8 ± 1.3 weeks) were analyzed, with seven discarded. For fHR , the mean absolute percentage error was 1.9 ± 1.0 %, Availability 79.6 ± 3.9 %, and coverage probability C P 5 = 76.2 %, C P 10 = 87.5 %. Significance . These results demonstrate the feasibility of fHR monitoring from dry-electrode aECG tailored for low-power wearables. Signal quality in clinical subjects matched the lowest PhysioNet cases, confirming robustness under low signal-to-noise conditions.