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

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 generatedaECG. 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 syntheticaECG. Performance for fetal QRS detection reachedPrecision=97.2(82.2)%,Specificity=99.8(93.8)%, andSensitivity=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 age39.8±1.3 weeks) were analyzed, with seven discarded. ForfHR, the mean absolute percentage error was1.9±1.0%, Availability79.6±3.9%, and coverage probabilityCP5=76.2%,CP10=87.5%.Significance. These results demonstrate the feasibility offHRmonitoring from dry-electrodeaECGtailored for low-power wearables. Signal quality in clinical subjects matched the lowest PhysioNet cases, confirming robustness under low signal-to-noise conditions.