**Author details**

classical adaptive approaches, soft computing techniques had to be used in our experiments

Comparing the results of ANFIS (**Table 1**) and adaptive algorithms (**Table 3**) for fECG extraction, it is demonstrated that ANFIS produces better results based upon SNR and RMSE improvements. A general disadvantage of using ANFISs is their longer processing time during network training, especially in the more sophisticated networks, which are necessary for more complex systems. From our experimental results in (**Table 1**), it is evident that the final values

In this chapter we focused on the use of advanced adaptive signal processing methods and highlighted the advantage of nonlinear methods such as adaptive neuro-fuzzy inference systems in enabling researchers to develop more reliable and accurate approaches in extracting diagnostic quality fECG signals and consequently facilitate the accurate detection of hypoxic conditions during pregnancy and labor. We included recent research findings from the most relevant engineering and medical literature and added our own contributions to the field. It is important to emphasize that currently only a fraction of the vast amount of diagnostic information in the abdominal ECG is used in clinical practice. Therefore, maximizing infor‐ mation extraction from fECG and CTG signals for the timely and reliable detection of fetal hypoxia is of tremendous clinical interest. This is a major challenge in signal processing and modern obstetrics as accurate determination of the fetus' status during pregnancy and labor is highly dependent on the quality of abdominal ECG monitoring, fECG signal filtering, and the consequent analysis of CTG and ST segments. In this chapter we also used a novel multichannel adaptive system that was designed, implemented, and validated by the authors to generate clinical-quality data and test and compare a variety of relevant signal processing algorithms. The primary component of this system is its adaptive block and the associated

to produce acceptable outcomes.

78 Advanced Biosignal Processing and Diagnostic Methods

**4. Conclusion**

of SNRouts are closely related to SNRins.

**Figure 17.** The Relationship between SNRin and SNRout for ANFIS31-ANFIS5.

Radek Martinek1\*, Radana Kahankova1 , Hana Skukova1 , Jaromir Konecny1 , Petr Bilik1 , Jan Zidek1 and Homer Nazeran2

\*Address all correspondence to: radek.martinek@vsb.cz

1 Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

2 Department of Electrical and Computer Engineering, University of Texas El Paso, El Paso, TX, USA
