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

<i>Objective</i>. 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 (<i>aECG</i>) to extract fetal heart rate (<i>fHR</i>) using a low-complexity algorithm suitable for low-power wearables.<i>Approach</i>. A hybrid model combining machine learning, QRS masking, and data fusion was trained on two PhysioNet databases and synthetically generated<i>aECG</i>. Model selection employed the Akaike criterion with data balancing and random sampling.<i>Main results</i>. 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<i>aECG</i>. 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. For<i>fHR</i>, the mean absolute percentage error was1.9±1.0%, Availability79.6±3.9%, and coverage probabilityCP5=76.2%,CP10=87.5%.<i>Significance</i>. These results demonstrate the feasibility of<i>fHR</i>monitoring from dry-electrode<i>aECG</i>tailored for low-power wearables. Signal quality in clinical subjects matched the lowest PhysioNet cases, confirming robustness under low signal-to-noise conditions.