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Direct assessment of the fetal heart rate from abdominal composite recordings

Moslem B., Bazzi O., Khalaf J., Diab M.O.

Journal of Medical Informatics & Technologies

2013

Vol. 22

143--149

In respect to the main goal of our ongoing work for analyzing fetal electrocardiogram (FECG) signals for monitoring the health of the fetus, we investigate in this paper the possibility of extracting the fetal heart rate (FHR) directly from the abdominal composite recordings. Our proposed approach is based on a combination of Independent Component Analysis (ICA) and least mean square (LMS) adaptive filter. The FHR of the estimated FECG signal is finally compared to a reference value extracted from a FECG signal recorded by using a spiral electrode attached directly to the fetal scalp. The experimental results show that FHR can be successfully evaluated directly from the abdominal composite recordings without the need of using any external reference signal.

Refining the diagnostic quality of the abdominal fetal electrocardiogram using the techniques of artificial intelligence

Martinek R., Zidek J.

Przegląd Elektrotechniczny

2012

R. 88, nr 12b

155-160

This article deals with utilization of the combination of the fuzzy system and artificial intelligence techniques, called the Adaptive Neuro Fuzzy Inference System ANFIS, with the aim to refine the diagnostic quality of the abdominal fetal electrocardiogram FECG. Within the scope of the experiments carried out and based on the ANFIS structure the authors created a complex system for removing the undesirable mother’s MECG degrading the abdominal FECG. Current research shows that the application of the conventional systems for enhancing the diagnostic quality of the abdominal FECG faces a series of problems (e.g. non-linear character of the task to solve, computational complexity of RLS algorithms, etc.). The need for a higher diagnostic quality of the abdominal FECG is reflected in the authors’ intention to utilize the designed system for the latest intrapartum monitoring method, called ST analysis. In terms of this advanced method, the aspect subjected to a diagnostic analysis is the ST segment of the FECG curve. The results indicate that the system utilizing ANFIS shows better experimental results than the conventional systems based on the LMS or RLS adaptive algorithms. The proposed adaptive system aims to clear any doubts in evaluation of the results of ST analysis while using a non-invasive method of external monitoring.

W artykule przedstawiono wykorzystanie fuzji metod: zbiorów rozmytych i sztucznej inteligencji ANFIS do poprawy jakości diagnostyki elektrokardiografii płodu. Głównym problemem jest usunięcie sygnału pochodzącego od matki który znacznie przewyższa sygnał płodu. (Poprawa jakości sygnału elektrokardiogramu płodu przy wykorzystaniu narzędzi sztucznej inteligencji)