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 Precision = 97.2(82.2)%, Specificity = 99.8(93.8)%, and Sensitivity = 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 ± 1.3weeks) 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 CP5 = 76.2%, CP10 = 87.5%.

Significance

These results demonstrate the feasibility of fHR monitoring from dryelectrode aECG tailored for low-power wearables. Signal quality in clinical subjects matched the lowest PhysioNet cases, confirming robustness under low signal-to-noise conditions.